Spark Streaming is an extension of the core Spark API that enables continuous data stream processing. It is particularly useful when data needs to be processed in real-time. Carol McDonald, HBase Hadoop Instructor at MapR, will cover:
+ What is Spark Streaming and what is it used for?
+ How does Spark Streaming work?
+ Example code to read, process, and write the processed data
Spark Streaming allows processing of live data streams at scale. Recent improvements include:
1) Enhanced fault tolerance through a write-ahead log and replay of unprocessed data on failure.
2) Dynamic backpressure to automatically adjust ingestion rates and ensure stability.
3) Visualization tools for debugging and monitoring streaming jobs.
4) Support for streaming machine learning algorithms and integration with other Spark components.
Yahoo migrated most of its Pig workload from MapReduce to Tez to achieve significant performance improvements and resource utilization gains. Some key challenges in the migration included addressing misconfigurations, bad programming practices, and behavioral changes between the frameworks. Yahoo was able to run very large and complex Pig on Tez jobs involving hundreds of vertices and terabytes of data smoothly at scale. Further optimizations are still needed around speculative execution and container reuse to improve utilization even more. The migration to Tez resulted in up to 30% reduction in runtime, memory, and CPU usage for Yahoo's Pig workload.
This document provides an overview and objectives of a session on getting started with HBase application development. It discusses why NoSQL and HBase are needed due to limitations of relational databases in scaling horizontally to handle big data. It provides an introduction to the HBase data model, architecture, and basic operations like put, get, scan, and delete. It explains how HBase stores data in a sorted map structure and how writes flow through the write ahead log, memstore, and are flushed to HFiles on disk.
Apache Eagle is a distributed real-time monitoring and alerting engine for Hadoop created by eBay to address limitations of existing tools in handling large volumes of metrics and logs from Hadoop clusters. It provides data activity monitoring, job performance monitoring, and unified monitoring. Eagle detects anomalies using machine learning algorithms and notifies users through alerts. It has been deployed across multiple eBay clusters with over 10,000 nodes and processes hundreds of thousands of events per day.
Apache Drill is the next generation of SQL query engines. It builds on ANSI SQL 2003, and extends it to handle new formats like JSON, Parquet, ORC, and the usual CSV, TSV, XML and other Hadoop formats. Most importantly, it melts away the barriers that have caused databases to become silos of data. It does so by able to handle schema-changes on the fly, enabling a whole new world of self-service and data agility never seen before.
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
This document provides an overview of using R, Hadoop, and Rhadoop for scalable analytics. It begins with introductions to basic R concepts like data types, vectors, lists, and data frames. It then covers Hadoop basics like MapReduce. Next, it discusses libraries for data manipulation in R like reshape2 and plyr. Finally, it focuses on Rhadoop projects like RMR for implementing MapReduce in R and considerations for using RMR effectively.
Re-envisioning the Lambda Architecture : Web Services & Real-time Analytics ...
This document summarizes Brian O'Neill's talk on re-envisioning the Lambda architecture using Storm and Cassandra for real-time analytics of web services data. The talk covered using polyglot persistence with technologies like Kafka, Cassandra, Elasticsearch and Titan to build scalable data pipelines. It also discussed using Storm and Trident to build real-time analytics topologies to compute metrics like averages across partitions in Cassandra using conditional updates. The talk concluded by proposing embedding the batch computation layer within the stream processing layer to enable code and logic reuse across layers.
Unified Batch & Stream Processing with Apache Samza
The traditional lambda architecture has been a popular solution for joining offline batch operations with real time operations. This setup incurs a lot of developer and operational overhead since it involves maintaining code that produces the same result in two, potentially different distributed systems. In order to alleviate these problems, we need a unified framework for processing and building data pipelines across batch and stream data sources.
Based on our experiences running and developing Apache Samza at LinkedIn, we have enhanced the framework to support: a) Pluggable data sources and sinks; b) A deployment model supporting different execution environments such as Yarn or VMs; c) A unified processing API for developers to work seamlessly with batch and stream data. In this talk, we will cover how these design choices in Apache Samza help tackle the overhead of lambda architecture. We will use some real production use-cases to elaborate how LinkedIn leverages Apache Samza to build unified data processing pipelines.
Speaker
Navina Ramesh, Sr. Software Engineer, LinkedIn
Transformation Processing Smackdown; Spark vs Hive vs Pig
This document provides an overview and comparison of different data transformation frameworks including Apache Pig, Apache Hive, and Apache Spark. It discusses features such as file formats, source to target mappings, data quality checks, and core processing functionality. The document contains code examples demonstrating how to perform common ETL tasks in each framework using delimited, XML, JSON, and other file formats. It also covers topics like numeric validation, data mapping, and performance. The overall purpose is to help users understand the different options for large-scale data processing in Hadoop.
Jim Scott, CHUG co-founder and Director, Enterprise Strategy and Architecture for MapR presents "Using Apache Drill". This presentation was given on August 13th, 2014 at the Nokia office in Chicago, IL.
Jim has held positions running Operations, Engineering, Architecture and QA teams. He has worked in the Consumer Packaged Goods, Digital Advertising, Digital Mapping, Chemical and Pharmaceutical industries. His work with high-throughput computing at Dow Chemical was a precursor to more standardized big data concepts like Hadoop.
Apache Drill brings the power of standard ANSI:SQL 2003 to your desktop and your clusters. It is like AWK for Hadoop. Drill supports querying schemaless systems like HBase, Cassandra and MongoDB. Use standard JDBC and ODBC APIs to use Drill from your custom applications. Leveraging an efficient columnar storage format, an optimistic execution engine and a cache-conscious memory layout, Apache Drill is blazing fast. Coordination, query planning, optimization, scheduling, and execution are all distributed throughout nodes in a system to maximize parallelization. This presentation contains live demonstrations.
The video can be found here: http://vimeo.com/chug/using-apache-drill
Realtime Detection of DDOS attacks using Apache Spark and MLLib
In this talk we will show how Hadoop Ecosystem tools like Apache Kafka, Spark, and MLLib can be used in various real-time architectures and how they can be used to perform real-time detection of a DDOS attack. We will explain some of the challenges in building real-time architectures, followed by walking through the DDOS detection example and a live demo. This talk is appropriate for anyone interested in Security, IoT, Apache Kafka, Spark, or Hadoop.
Presenter Ryan Bosshart is a Systems Engineer at Cloudera and is the first 3 time presenter at BigDataMadison!
This document provides an overview of installing and programming with Apache Spark on Hortonworks Data Platform (HDP). It introduces Spark and its components, benefits over other frameworks, and Hortonworks' commitment to Spark. The document outlines an example Spark programming workflow using Resilient Distributed Datasets (RDDs) in Scala, and covers common RDD transformations, actions, and persistence methods. It also discusses Spark deployment modes like standalone and on YARN, and reference HDP architectures using Spark.
The document discusses how Spark can be used to supercharge ETL workflows by running them faster and with less code compared to traditional Hadoop approaches. It provides examples of using Spark for tasks like sessionization of user clickstream data. Best practices are covered like optimizing for JVM issues, avoiding full GC pauses, and tips for deployment on EC2. Future improvements to Spark like SQL support and Java 8 are also mentioned.
Easy, Scalable, Fault-tolerant stream processing with Structured Streaming in...
Last year, in Apache Spark 2.0, we introduced Structured Steaming, a new stream processing engine built on Spark SQL, which revolutionized how developers could write stream processing application. Structured Streaming enables users to express their computations the same way they would express a batch query on static data. Developers can express queries using powerful high-level APIs including DataFrames, Dataset and SQL. Then, the Spark SQL engine is capable of converting these batch-like transformations into an incremental execution plan that can process streaming data, while automatically handling late, out-of-order data, and ensuring end-to-end exactly-once fault-tolerance guarantees.
Since Spark 2.0 we've been hard at work building first class integration with Kafka. With this new connectivity, performing complex, low-latency analytics is now as easy as writing a standard SQL query. This functionality in addition to the existing connectivity of Spark SQL make it easy to analyze data using one unified framework. Users can now seamlessly extract insights from data, independent of whether it is coming from messy / unstructured files, a structured / columnar historical data warehouse or arriving in real-time from pubsub systems like Kafka and Kinesis.
We'll walk through a concrete example where in less than 10 lines, we read Kafka, parse JSON payload data into separate columns, transform it, enrich it by joining with static data and write it out as a table ready for batch and ad-hoc queries on up-to-the-last-minute data. We'll use techniques including event-time based aggregations, arbitrary stateful operations, and automatic state management using event-time watermarks.
This is slides from our recent HadoopIsrael meetup. It is dedicated to comparison Spark and Tez frameworks.
In the end of the meetup there is small update about our ImpalaToGo project.
Good systems development often depends on multiple data management disciplines that provide a solid foundation. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with its associated technologies, this perspective often represents a typical tool-and-technology focus, which has not achieved significant results to date. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies in support of business strategy.
Takeaways:
Metadata value proposition: How to leverage metadata in support of your business strategy
Understanding foundational metadata concepts based on the DAMA DMBOK
Guiding principles & lessons learned
Spark Streaming | Twitter Sentiment Analysis Example | Apache Spark Training ...
This Edureka Spark Streaming Tutorial will help you understand how to use Spark Streaming to stream data from twitter in real-time and then process it for Sentiment Analysis. This Spark Streaming tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
1) What is Streaming?
2) Spark Ecosystem
3) Why Spark Streaming?
4) Spark Streaming Overview
5) DStreams
6) DStream Transformations
7) Caching/ Persistence
8) Accumulators, Broadcast Variables and Checkpoints
9) Use Case – Twitter Sentiment Analysis
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1yNuLGF.
Matei Zaharia talks about the latest developments in Spark and shows examples of how it can combine processing algorithms to build rich data pipelines in just a few lines of code. Filmed at qconsf.com.
Matei Zaharia is an assistant professor of computer science at MIT, and CTO of Databricks, the company commercializing Apache Spark.
Apache HBase + Spark: Leveraging your Non-Relational Datastore in Batch and S...
This document discusses leveraging Apache HBase as a non-relational datastore in Apache Spark batch and streaming applications. It outlines integration patterns for reading from and writing to HBase using Spark, provides examples of API usage, and discusses future work including using HBase edits as a streaming source.
Apache HBase Internals you hoped you Never Needed to Understand
Covers numerous internal features, concepts, and implementations of Apache HBase. The focus will be driven from an operational standpoint, investigating each component enough to understand its role in Apache HBase and the generic problems that each are trying to solve. Topics will range from HBase’s RPC system to the new Procedure v2 framework, to filesystem and ZooKeeper use, to backup and replication features, to region assignment and row locks. Each topic will be covered at a high-level, attempting to distill the often complicated details down to the most salient information.
This document appears to be test results from running the Yahoo! Cloud Serving Benchmark on a system. It includes performance metrics like request latency distributions and throughput for different request sizes and concurrency levels. Various graphs and tables are presented showing results from multiple benchmark runs. The benchmark was run to test the performance of the system for serving requests in a cloud computing environment.
Spark Streaming allows processing of live data streams using Spark. It works by receiving data streams, chopping them into batches, and processing the batches using Spark. This presentation covered Spark Streaming concepts like the lifecycle of a streaming application, best practices for aggregations, operationalization through checkpointing, and achieving high throughput. It also discussed debugging streaming jobs and the benefits of combining streaming with batch, machine learning, and SQL processing.
Zhan Zhang presents improvements made to bring HBase data efficiently into Spark with DataFrame support. The improvements include high performance by moving computation to data and reducing network overhead through partition pruning and column pruning. Full DataFrame support is provided, allowing Spark SQL and integrated language queries to run on existing HBase tables with Java primitive type support.
The document discusses big data and Hadoop concepts. It covers Hadoop operations like put, get, scan, filter, delete as well as join and group by. It also discusses the different types of data access patterns like random write, sequential read, sequential write and random read. The document focuses on big data, Hadoop operations, and data access patterns.
TWDI Accelerate Seattle, Oct 16, 2017: Distributed and In-Database Analytics ...
Event: TDWI Accelerate Seattle, October 16, 2017
Topic: Distributed and In-Database Analytics with R
Presenter: Debraj GuhaThakurta
Description: How to develop scalable and in-DB analytics using R in Spark and SQL-Server
TDWI Accelerate, Seattle, Oct 16, 2017: Distributed and In-Database Analytics...
R is a popular statistical programming language used for data analysis and machine learning. It has over 3 million users and is taught widely in universities. While powerful, R has some scaling limitations for big data. Several Apache Spark integrations with R like SparkR and sparklyr enable distributed, parallel processing of large datasets using R on Spark clusters. Other options for scaling R include H2O for in-memory analytics, Microsoft ML Server for on-premises scaling, and ScaleR for portable parallel processing across platforms. These solutions allow R programs and models to be trained on large datasets and deployed for operational use on big data in various cloud and on-premises environments.
NoSQL HBase schema design and SQL with Apache Drill Carol McDonald
The document provides an overview of HBase, including:
- HBase is a column-oriented NoSQL database modeled after Google's Bigtable. It is designed to handle large volumes of sparse data across clusters in a distributed fashion.
- Data in HBase is stored in tables containing rows, column families, columns, and versions. Tables are partitioned into regions distributed across region servers. The HMaster manages the cluster and Zookeeper coordinates operations.
- Common operations on HBase include put (insert/update), get, scan, and delete. The meta table stored in Zookeeper maps rows to their regions. This allows clients to efficiently access data in HBase's distributed architecture.
The document discusses Spark Streaming and its execution model. It describes how Spark Streaming receives data streams, divides them into micro-batches, and processes the micro-batches using Spark. It also covers transformations and actions that can be performed on DStreams, window operations, and deployment options for Spark Streaming including local, standalone, and cluster modes.
Strata NYC 2015: What's new in Spark StreamingDatabricks
Spark Streaming allows processing of live data streams at scale. Recent improvements include:
1) Enhanced fault tolerance through a write-ahead log and replay of unprocessed data on failure.
2) Dynamic backpressure to automatically adjust ingestion rates and ensure stability.
3) Visualization tools for debugging and monitoring streaming jobs.
4) Support for streaming machine learning algorithms and integration with other Spark components.
Yahoo migrated most of its Pig workload from MapReduce to Tez to achieve significant performance improvements and resource utilization gains. Some key challenges in the migration included addressing misconfigurations, bad programming practices, and behavioral changes between the frameworks. Yahoo was able to run very large and complex Pig on Tez jobs involving hundreds of vertices and terabytes of data smoothly at scale. Further optimizations are still needed around speculative execution and container reuse to improve utilization even more. The migration to Tez resulted in up to 30% reduction in runtime, memory, and CPU usage for Yahoo's Pig workload.
This document provides an overview and objectives of a session on getting started with HBase application development. It discusses why NoSQL and HBase are needed due to limitations of relational databases in scaling horizontally to handle big data. It provides an introduction to the HBase data model, architecture, and basic operations like put, get, scan, and delete. It explains how HBase stores data in a sorted map structure and how writes flow through the write ahead log, memstore, and are flushed to HFiles on disk.
Apache Eagle is a distributed real-time monitoring and alerting engine for Hadoop created by eBay to address limitations of existing tools in handling large volumes of metrics and logs from Hadoop clusters. It provides data activity monitoring, job performance monitoring, and unified monitoring. Eagle detects anomalies using machine learning algorithms and notifies users through alerts. It has been deployed across multiple eBay clusters with over 10,000 nodes and processes hundreds of thousands of events per day.
Apache Drill is the next generation of SQL query engines. It builds on ANSI SQL 2003, and extends it to handle new formats like JSON, Parquet, ORC, and the usual CSV, TSV, XML and other Hadoop formats. Most importantly, it melts away the barriers that have caused databases to become silos of data. It does so by able to handle schema-changes on the fly, enabling a whole new world of self-service and data agility never seen before.
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
This document provides an overview of using R, Hadoop, and Rhadoop for scalable analytics. It begins with introductions to basic R concepts like data types, vectors, lists, and data frames. It then covers Hadoop basics like MapReduce. Next, it discusses libraries for data manipulation in R like reshape2 and plyr. Finally, it focuses on Rhadoop projects like RMR for implementing MapReduce in R and considerations for using RMR effectively.
Re-envisioning the Lambda Architecture : Web Services & Real-time Analytics ...Brian O'Neill
This document summarizes Brian O'Neill's talk on re-envisioning the Lambda architecture using Storm and Cassandra for real-time analytics of web services data. The talk covered using polyglot persistence with technologies like Kafka, Cassandra, Elasticsearch and Titan to build scalable data pipelines. It also discussed using Storm and Trident to build real-time analytics topologies to compute metrics like averages across partitions in Cassandra using conditional updates. The talk concluded by proposing embedding the batch computation layer within the stream processing layer to enable code and logic reuse across layers.
Unified Batch & Stream Processing with Apache SamzaDataWorks Summit
The traditional lambda architecture has been a popular solution for joining offline batch operations with real time operations. This setup incurs a lot of developer and operational overhead since it involves maintaining code that produces the same result in two, potentially different distributed systems. In order to alleviate these problems, we need a unified framework for processing and building data pipelines across batch and stream data sources.
Based on our experiences running and developing Apache Samza at LinkedIn, we have enhanced the framework to support: a) Pluggable data sources and sinks; b) A deployment model supporting different execution environments such as Yarn or VMs; c) A unified processing API for developers to work seamlessly with batch and stream data. In this talk, we will cover how these design choices in Apache Samza help tackle the overhead of lambda architecture. We will use some real production use-cases to elaborate how LinkedIn leverages Apache Samza to build unified data processing pipelines.
Speaker
Navina Ramesh, Sr. Software Engineer, LinkedIn
Transformation Processing Smackdown; Spark vs Hive vs PigLester Martin
This document provides an overview and comparison of different data transformation frameworks including Apache Pig, Apache Hive, and Apache Spark. It discusses features such as file formats, source to target mappings, data quality checks, and core processing functionality. The document contains code examples demonstrating how to perform common ETL tasks in each framework using delimited, XML, JSON, and other file formats. It also covers topics like numeric validation, data mapping, and performance. The overall purpose is to help users understand the different options for large-scale data processing in Hadoop.
Jim Scott, CHUG co-founder and Director, Enterprise Strategy and Architecture for MapR presents "Using Apache Drill". This presentation was given on August 13th, 2014 at the Nokia office in Chicago, IL.
Jim has held positions running Operations, Engineering, Architecture and QA teams. He has worked in the Consumer Packaged Goods, Digital Advertising, Digital Mapping, Chemical and Pharmaceutical industries. His work with high-throughput computing at Dow Chemical was a precursor to more standardized big data concepts like Hadoop.
Apache Drill brings the power of standard ANSI:SQL 2003 to your desktop and your clusters. It is like AWK for Hadoop. Drill supports querying schemaless systems like HBase, Cassandra and MongoDB. Use standard JDBC and ODBC APIs to use Drill from your custom applications. Leveraging an efficient columnar storage format, an optimistic execution engine and a cache-conscious memory layout, Apache Drill is blazing fast. Coordination, query planning, optimization, scheduling, and execution are all distributed throughout nodes in a system to maximize parallelization. This presentation contains live demonstrations.
The video can be found here: http://vimeo.com/chug/using-apache-drill
Realtime Detection of DDOS attacks using Apache Spark and MLLibRyan Bosshart
In this talk we will show how Hadoop Ecosystem tools like Apache Kafka, Spark, and MLLib can be used in various real-time architectures and how they can be used to perform real-time detection of a DDOS attack. We will explain some of the challenges in building real-time architectures, followed by walking through the DDOS detection example and a live demo. This talk is appropriate for anyone interested in Security, IoT, Apache Kafka, Spark, or Hadoop.
Presenter Ryan Bosshart is a Systems Engineer at Cloudera and is the first 3 time presenter at BigDataMadison!
This document provides an overview of installing and programming with Apache Spark on Hortonworks Data Platform (HDP). It introduces Spark and its components, benefits over other frameworks, and Hortonworks' commitment to Spark. The document outlines an example Spark programming workflow using Resilient Distributed Datasets (RDDs) in Scala, and covers common RDD transformations, actions, and persistence methods. It also discusses Spark deployment modes like standalone and on YARN, and reference HDP architectures using Spark.
ETL with SPARK - First Spark London meetupRafal Kwasny
The document discusses how Spark can be used to supercharge ETL workflows by running them faster and with less code compared to traditional Hadoop approaches. It provides examples of using Spark for tasks like sessionization of user clickstream data. Best practices are covered like optimizing for JVM issues, avoiding full GC pauses, and tips for deployment on EC2. Future improvements to Spark like SQL support and Java 8 are also mentioned.
Easy, Scalable, Fault-tolerant stream processing with Structured Streaming in...DataWorks Summit
Last year, in Apache Spark 2.0, we introduced Structured Steaming, a new stream processing engine built on Spark SQL, which revolutionized how developers could write stream processing application. Structured Streaming enables users to express their computations the same way they would express a batch query on static data. Developers can express queries using powerful high-level APIs including DataFrames, Dataset and SQL. Then, the Spark SQL engine is capable of converting these batch-like transformations into an incremental execution plan that can process streaming data, while automatically handling late, out-of-order data, and ensuring end-to-end exactly-once fault-tolerance guarantees.
Since Spark 2.0 we've been hard at work building first class integration with Kafka. With this new connectivity, performing complex, low-latency analytics is now as easy as writing a standard SQL query. This functionality in addition to the existing connectivity of Spark SQL make it easy to analyze data using one unified framework. Users can now seamlessly extract insights from data, independent of whether it is coming from messy / unstructured files, a structured / columnar historical data warehouse or arriving in real-time from pubsub systems like Kafka and Kinesis.
We'll walk through a concrete example where in less than 10 lines, we read Kafka, parse JSON payload data into separate columns, transform it, enrich it by joining with static data and write it out as a table ready for batch and ad-hoc queries on up-to-the-last-minute data. We'll use techniques including event-time based aggregations, arbitrary stateful operations, and automatic state management using event-time watermarks.
This is slides from our recent HadoopIsrael meetup. It is dedicated to comparison Spark and Tez frameworks.
In the end of the meetup there is small update about our ImpalaToGo project.
Good systems development often depends on multiple data management disciplines that provide a solid foundation. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with its associated technologies, this perspective often represents a typical tool-and-technology focus, which has not achieved significant results to date. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies in support of business strategy.
Takeaways:
Metadata value proposition: How to leverage metadata in support of your business strategy
Understanding foundational metadata concepts based on the DAMA DMBOK
Guiding principles & lessons learned
Spark Streaming | Twitter Sentiment Analysis Example | Apache Spark Training ...Edureka!
This Edureka Spark Streaming Tutorial will help you understand how to use Spark Streaming to stream data from twitter in real-time and then process it for Sentiment Analysis. This Spark Streaming tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
1) What is Streaming?
2) Spark Ecosystem
3) Why Spark Streaming?
4) Spark Streaming Overview
5) DStreams
6) DStream Transformations
7) Caching/ Persistence
8) Accumulators, Broadcast Variables and Checkpoints
9) Use Case – Twitter Sentiment Analysis
Unified Big Data Processing with Apache SparkC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1yNuLGF.
Matei Zaharia talks about the latest developments in Spark and shows examples of how it can combine processing algorithms to build rich data pipelines in just a few lines of code. Filmed at qconsf.com.
Matei Zaharia is an assistant professor of computer science at MIT, and CTO of Databricks, the company commercializing Apache Spark.
This document discusses leveraging Apache HBase as a non-relational datastore in Apache Spark batch and streaming applications. It outlines integration patterns for reading from and writing to HBase using Spark, provides examples of API usage, and discusses future work including using HBase edits as a streaming source.
Apache HBase Internals you hoped you Never Needed to UnderstandJosh Elser
Covers numerous internal features, concepts, and implementations of Apache HBase. The focus will be driven from an operational standpoint, investigating each component enough to understand its role in Apache HBase and the generic problems that each are trying to solve. Topics will range from HBase’s RPC system to the new Procedure v2 framework, to filesystem and ZooKeeper use, to backup and replication features, to region assignment and row locks. Each topic will be covered at a high-level, attempting to distill the often complicated details down to the most salient information.
This document appears to be test results from running the Yahoo! Cloud Serving Benchmark on a system. It includes performance metrics like request latency distributions and throughput for different request sizes and concurrency levels. Various graphs and tables are presented showing results from multiple benchmark runs. The benchmark was run to test the performance of the system for serving requests in a cloud computing environment.
Spark Streaming allows processing of live data streams using Spark. It works by receiving data streams, chopping them into batches, and processing the batches using Spark. This presentation covered Spark Streaming concepts like the lifecycle of a streaming application, best practices for aggregations, operationalization through checkpointing, and achieving high throughput. It also discussed debugging streaming jobs and the benefits of combining streaming with batch, machine learning, and SQL processing.
Zhan Zhang presents improvements made to bring HBase data efficiently into Spark with DataFrame support. The improvements include high performance by moving computation to data and reducing network overhead through partition pruning and column pruning. Full DataFrame support is provided, allowing Spark SQL and integrated language queries to run on existing HBase tables with Java primitive type support.
The document discusses big data and Hadoop concepts. It covers Hadoop operations like put, get, scan, filter, delete as well as join and group by. It also discusses the different types of data access patterns like random write, sequential read, sequential write and random read. The document focuses on big data, Hadoop operations, and data access patterns.
TWDI Accelerate Seattle, Oct 16, 2017: Distributed and In-Database Analytics ...Debraj GuhaThakurta
Event: TDWI Accelerate Seattle, October 16, 2017
Topic: Distributed and In-Database Analytics with R
Presenter: Debraj GuhaThakurta
Description: How to develop scalable and in-DB analytics using R in Spark and SQL-Server
TDWI Accelerate, Seattle, Oct 16, 2017: Distributed and In-Database Analytics...Debraj GuhaThakurta
R is a popular statistical programming language used for data analysis and machine learning. It has over 3 million users and is taught widely in universities. While powerful, R has some scaling limitations for big data. Several Apache Spark integrations with R like SparkR and sparklyr enable distributed, parallel processing of large datasets using R on Spark clusters. Other options for scaling R include H2O for in-memory analytics, Microsoft ML Server for on-premises scaling, and ScaleR for portable parallel processing across platforms. These solutions allow R programs and models to be trained on large datasets and deployed for operational use on big data in various cloud and on-premises environments.
Many of the systems we want to monitor happen as a stream of events, examples include event data from web or mobile applications, sensors, medical devices. What do we need to do to build a real time streaming application , and how do we do this with High Performance at Scale?
This Free Code Friday will help you get a jump-start on scaling distributed computing by taking an example time series application and coding through different aspects of working with such a dataset. We will cover building an end to end distributed processing pipeline using MapR Streams (Kafka API), Apache Spark, and MapR-DB (HBase API), to rapidly ingest, process and store large volumes of high speed data.
Deep dive into spark streaming, topics include:
1. Spark Streaming Introduction
2. Computing Model in Spark Streaming
3. System Model & Architecture
4. Fault-tolerance, Check pointing
5. Comb on Spark Streaming
This document discusses Spark Streaming and its use for near real-time ETL. It provides an overview of Spark Streaming, how it works internally using receivers and workers to process streaming data, and an example use case of building a recommender system to find matches using both batch and streaming data. Key points covered include the streaming execution model, handling data receipt and job scheduling, and potential issues around data loss and (de)serialization.
Unified Big Data Processing with Apache Spark (QCON 2014)Databricks
This document discusses Apache Spark, a fast and general engine for big data processing. It describes how Spark generalizes the MapReduce model through its Resilient Distributed Datasets (RDDs) abstraction, which allows efficient sharing of data across parallel operations. This unified approach allows Spark to support multiple types of processing, like SQL queries, streaming, and machine learning, within a single framework. The document also outlines ongoing developments like Spark SQL and improved machine learning capabilities.
Spark is a fast and general cluster computing system that improves on MapReduce by keeping data in-memory between jobs. It was developed in 2009 at UC Berkeley and open sourced in 2010. Spark core provides in-memory computing capabilities and a programming model that allows users to write programs as transformations on distributed datasets.
ScalaTo July 2019 - No more struggles with Apache Spark workloads in productionChetan Khatri
Scala Toronto July 2019 event at 500px.
Pure Functional API Integration
Apache Spark Internals tuning
Performance tuning
Query execution plan optimisation
Cats Effects for switching execution model runtime.
Discovery / experience with Monix, Scala Future.
HKOSCon18 - Chetan Khatri - Scaling TB's of Data with Apache Spark and Scala ...Chetan Khatri
This document summarizes a presentation about scaling terabytes of data with Apache Spark and Scala. The key points are:
1) The presenter discusses how to use Apache Spark and Scala to process large scale data in a distributed manner across clusters. Spark operations like RDDs, DataFrames and Datasets are covered.
2) A case study is presented about reengineering a data processing platform for a retail business to improve performance. Changes included parallelizing jobs, tuning Spark hyperparameters, and building a fast data architecture using Spark, Kafka and data lakes.
3) Performance was improved through techniques like dynamic resource allocation in YARN, reducing memory and cores per executor to better utilize cluster resources, and processing data
Remember the last time you tried to write a MapReduce job (obviously something non trivial than a word count)? It sure did the work, but has lot of pain points from getting an idea to implement it in terms of map reduce. Did you wonder how life will be much simple if you had to code like doing collection operations and hence being transparent* to its distributed nature? Did you want/hope for more performant/low latency jobs? Well, seems like you are in luck.
In this talk, we will be covering a different way to do MapReduce kind of operations without being just limited to map and reduce, yes, we will be talking about Apache Spark. We will compare and contrast Spark programming model with Map Reduce. We will see where it shines, and why to use it, how to use it. We’ll be covering aspects like testability, maintainability, conciseness of the code, and some features like iterative processing, optional in-memory caching and others. We will see how Spark, being just a cluster computing engine, abstracts the underlying distributed storage, and cluster management aspects, giving us a uniform interface to consume/process/query the data. We will explore the basic abstraction of RDD which gives us so many awesome features making Apache Spark a very good choice for your big data applications. We will see this through some non trivial code examples.
Session at the IndicThreads.com Confence held in Pune, India on 27-28 Feb 2015
http://www.indicthreads.com
http://pune15.indicthreads.com
The document provides an overview of data science with Python and integrating Python with Hadoop and Apache Spark frameworks. It discusses:
- Why Python should be integrated with Hadoop and the ecosystem including HDFS, MapReduce, and Spark.
- Key concepts of Hadoop including HDFS for storage, MapReduce for processing, and how Python can be integrated via APIs.
- Benefits of Apache Spark like speed, simplicity, and efficiency through its RDD abstraction and how PySpark enables Python access.
- Examples of using Hadoop Streaming and PySpark to analyze data and determine word counts from documents.
This document provides an overview of WANdisco's NonStop HBase solution for making HBase continuously available for enterprise deployments. It discusses traditional high availability approaches that rely on backups and describes how these can fail. It then introduces WANdisco's patented active-active replication technology that provides 100% uptime with zero downtime. The document demonstrates how WANdisco implements multiple active HBase masters and region servers using a distributed coordination engine and Paxos consensus protocol. This allows HBase to avoid single points of failure and provides seamless failover for clients. It concludes with a demo of the NonStop HBase solution in action.
Lambda Architecture: The Best Way to Build Scalable and Reliable Applications!Tugdual Grall
Lambda Architecture is a useful framework to think about designing big data applications. This framework has been built initially at Twitter. In this presentation you will learn, based on concrete examples how to build deploy scalable and fault tolerant applications, with a focus on Big Data and Hadoop.
This presentation was delivered at the OOP conference, Munich, Feb 2016
This document discusses Apache Spark, an open-source cluster computing framework. It provides an overview of Spark, including its main concepts like RDDs (Resilient Distributed Datasets) and transformations. Spark is presented as a faster alternative to Hadoop for iterative jobs and machine learning through its ability to keep data in-memory. Example code is shown for Spark's programming model in Scala and Python. The document concludes that Spark offers a rich API to make data analytics fast, achieving speedups of up to 100x over Hadoop in real applications.
The Future of Hadoop: A deeper look at Apache SparkCloudera, Inc.
Jai Ranganathan, Senior Director of Product Management, discusses why Spark has experienced such wide adoption and provide a technical deep dive into the architecture. Additionally, he presents some use cases in production today. Finally, he shares our vision for the Hadoop ecosystem and why we believe Spark is the successor to MapReduce for Hadoop data processing.
This document discusses new features and capabilities in MATLAB for working with scientific data formats and performing technical computing. It highlights enhancements to reading HDF5 and NetCDF files with both high-level and low-level interfaces. It also covers new capabilities for handling dates/times, big data, and accessing web services through RESTful APIs.
This document provides an overview of analyzing large datasets using Apache Spark. It discusses:
- The evolution of big data systems to address high volume, velocity, and variety of data including Hadoop, Pig, Hive, and Spark.
- Common data processing models like batch, lambda architecture, and kappa architecture.
- Running Spark on OpenShift for scalable data analysis across clusters of machines.
- A demonstration of using Spark on OpenShift with Oshinko to process streaming data from Kafka on OpenShift.
This document discusses Infobip's journey towards enabling real-time querying of aggregated data. Initially, Infobip had a monolithic architecture with a single database that became a bottleneck. They introduced multiple databases and microservices but querying spanned databases and results had to be joined. A data warehouse (GREEN) provided reporting but was not real-time. To enable real-time queries, Infobip implemented a lambda architecture using Kafka as the real-time data pipeline and Druid for real-time querying and aggregations, achieving sub-second responses and less than 2 seconds of data delay. This allows real-time insights from ingested messaging data while GREEN remains the batch/serving layer.
Machine Learning with H2O, Spark, and Python at Strata 2015Sri Ambati
Machine Learning with H2O, Spark, and Python at Strata SJ 2015-by Cliff Click and Michal Malohlava
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
This document provides an introduction and overview of StatsD, including:
- A brief history of StatsD and how it was originally created by Flickr and implemented by Etsy.
- An overview of the StatsD architecture which involves sending metrics from applications over UDP to the StatsD server, which then sends the data to Carbon over TCP.
- An explanation of the different metric types StatsD supports - counters, gauges, sets, and timings - and examples of common use cases.
- Instructions for installing and running a StatsD server as well as examples of using StatsD clients in Node.js and Java applications.
Similar to Free Code Friday - Spark Streaming with HBase (20)
How Data-Driven Approaches are Changing Your Data Management Strategies
Introducing data-driven strategies into your business model alters the way your organization manages and provides information to your customers, partners and employees. Gone are the days of “waterfall” implementation strategies from relational data to applications within a data center. Now, data-driven business models require agile implementation of applications based on information from all across an organization–on-premises, cloud, and mobile–and includes information from outside corporate walls from partners, third-party vendors, and customers. Data management strategies need to be ready to meet these challenges or your new and disruptive business models will fail at the most critical time: when your customers want to access it.
ML Workshop 2: Machine Learning Model Comparison & EvaluationMapR Technologies
This document discusses machine learning model comparison and evaluation. It describes how the rendezvous architecture in MapR makes evaluation easier by collecting metrics on model performance and allowing direct comparison of models. It also discusses challenges like reject inferencing and the need to balance exploration of new models with exploitation of existing models. The document provides recommendations for change detection and analyzing latency distributions to better evaluate models over time.
Self-Service Data Science for Leveraging ML & AI on All of Your DataMapR Technologies
MapR has launched the MapR Data Science Refinery which leverages a scalable data science notebook with native platform access, superior out-of-the-box security, and access to global event streaming and a multi-model NoSQL database.
Enabling Real-Time Business with Change Data CaptureMapR Technologies
Machine learning (ML) and artificial intelligence (AI) enable intelligent processes that can autonomously make decisions in real-time. The real challenge for effective ML and AI is getting all relevant data to a converged data platform in real-time, where it can be processed using modern technologies and integrated into any downstream systems.
Machine Learning for Chickens, Autonomous Driving and a 3-year-old Who Won’t ...MapR Technologies
The document discusses machine learning and autonomous driving applications. It begins with a simple machine learning example of classifying images of chickens posted on Twitter. It then discusses how autonomous vehicles use machine learning by gathering large amounts of sensor data to train models for tasks like object recognition. The document also summarizes challenges for applying machine learning at an enterprise scale and how the MapR data platform can address these challenges by providing a unified environment for storing, accessing, and processing large amounts of diverse data.
ML Workshop 1: A New Architecture for Machine Learning LogisticsMapR Technologies
Having heard the high-level rationale for the rendezvous architecture in the introduction to this series, we will now dig in deeper to talk about how and why the pieces fit together. In terms of components, we will cover why streams work, why they need to be persistent, performant and pervasive in a microservices design and how they provide isolation between components. From there, we will talk about some of the details of the implementation of a rendezvous architecture including discussion of when the architecture is applicable, key components of message content and how failures and upgrades are handled. We will touch on the monitoring requirements for a rendezvous system but will save the analysis of the recorded data for later. Listen to the webinar on demand: https://mapr.com/resources/webinars/machine-learning-workshop-1/
Machine Learning Success: The Key to Easier Model ManagementMapR Technologies
Join Ellen Friedman, co-author (with Ted Dunning) of a new short O’Reilly book Machine Learning Logistics: Model Management in the Real World, to look at what you can do to have effective model management, including the role of stream-first architecture, containers, a microservices approach and a DataOps style of work. Ellen will provide a basic explanation of a new architecture that not only leverages stream transport but also makes use of canary models and decoy models for accurate model evaluation and for efficient and rapid deployment of new models in production.
Data Warehouse Modernization: Accelerating Time-To-Action MapR Technologies
Data warehouses have been the standard tool for analyzing data created by business operations. In recent years, increasing data volumes, new types of data formats, and emerging analytics technologies such as machine learning have given rise to modern data lakes. Connecting application databases, data warehouses, and data lakes using real-time data pipelines can significantly improve the time to action for business decisions. More: http://info.mapr.com/WB_MapR-StreamSets-Data-Warehouse-Modernization_Global_DG_17.08.16_RegistrationPage.html
Live Tutorial – Streaming Real-Time Events Using Apache APIsMapR Technologies
For this talk we will explore the power of streaming real time events in the context of the IoT and smart cities.
http://info.mapr.com/WB_Streaming-Real-Time-Events_Global_DG_17.08.02_RegistrationPage.html
Bringing Structure, Scalability, and Services to Cloud-Scale StorageMapR Technologies
Deploying storage with a forklift is so 1990s, right? Today’s applications and infrastructure demand systems and services that scale. Customers require performance and capacity to fit the use case and workloads, not the other way around. Architects need multi-temperature, multi-location, highly available, and compliance friendly platforms that grow with the generational shift in data growth and utility.
Churn prediction is big business. It minimizes customer defection by predicting which customers are likely to cancel a service. Though originally used within the telecommunications industry, it has become common practice for banks, ISPs, insurance firms, and other verticals. More: http://info.mapr.com/WB_PredictingChurn_Global_DG_17.06.15_RegistrationPage.html
The prediction process is data-driven and often uses advanced machine learning techniques. In this webinar, we'll look at customer data, do some preliminary analysis, and generate churn prediction models – all with Spark machine learning (ML) and a Zeppelin notebook.
Spark’s ML library goal is to make machine learning scalable and easy. Zeppelin with Spark provides a web-based notebook that enables interactive machine learning and visualization.
In this tutorial, we'll do the following:
Review classification and decision trees
Use Spark DataFrames with Spark ML pipelines
Predict customer churn with Apache Spark ML decision trees
Use Zeppelin to run Spark commands and visualize the results
An Introduction to the MapR Converged Data PlatformMapR Technologies
Listen to the webinar on-demand: http://info.mapr.com/WB_Partner_CDP_Intro_EMEA_DG_17.05.31_RegistrationPage.html
In this 90-minute webinar, we discuss:
- The MapR Converged Data Platform and its components
- Use cases for the Converged Data Platform
- MapR Converged Partner Program
- How to get started with MapR
- Becoming a partner
How to Leverage the Cloud for Business Solutions | Strata Data Conference Lon...MapR Technologies
IT budgets are shrinking, and the move to next-generation technologies is upon us. The cloud is an option for nearly every company, but just because it is an option doesn’t mean it is always the right solution for every problem.
Most cloud providers would prefer that every customer be tightly coupled with their proprietary services and APIs to create lock-in with that cloud provider. The savvy customer will leverage the cloud as infrastructure and stay loosely bound to a cloud provider. This creates an opportunity for the customer to execute a multicloud strategy or even a hybrid on-premises and cloud solution.
Jim Scott explores different use cases that may be best run in the cloud versus on-premises, points out opportunities to optimize cost and operational benefits, and explains how to get the data moved between locations. Along the way, Jim discusses security, backups, event streaming, databases, replication, and snapshots across a variety of use cases that run most businesses today.
Is your organization at the analytics crossroads? Have you made strides collecting and sharing massive amounts of data from electronic health records, insurance claims, and health information exchanges but found these efforts made little impact on efficiency, patient outcomes, or costs?
Changes in how business is done combined with multiple technology drivers make geo-distributed data increasingly important for enterprises. These changes are causing serious disruption across a wide range of industries, including healthcare, manufacturing, automotive, telecommunications, and entertainment. Technical challenges arise with these disruptions, but the good news is there are now innovative solutions to address these problems. http://info.mapr.com/WB_Geo-distributed-Big-Data-and-Analytics_Global_DG_17.05.16_RegistrationPage.html
This document is the agenda for a MapR product update webinar that will take place in Spring 2017. It introduces MapR's new Persistent Application Client Container (PACC) which allows applications to easily persist data in Docker containers. It also discusses MapR Edge for IoT which extends MapR's converged data platform to the edge. The webinar will cover Hive, Spark, and Drill updates in the new MapR Ecosystem Pack 3.0. Speakers from MapR will provide details on these products and there will be a question and answer session.
3 Benefits of Multi-Temperature Data Management for Data AnalyticsMapR Technologies
SAP® HANA and SAP® IQ are popular platforms for various analytical and transactional use cases. If you’re an SAP customer, you’ve experienced the benefits of deploying these solutions. However, as data volumes grow, you’re likely asking yourself: How do I scale storage to support these applications? How can I have one platform for various applications and use cases?
Cisco & MapR bring 3 Superpowers to SAP HANA DeploymentsMapR Technologies
SAP HANA is an increasingly popular platform for various analytical and transactional use cases with its in-memory architecture. If you’re an SAP customer you’ve experienced the benefits.
However, the underlying storage for SAP HANA is painfully expensive. This slows down your ability to grow your SAP HANA footprint and serve up more applications.
You’re not the only one still loading your data into data warehouses and building marts or cubes out of it. But today’s data requires a much more accessible environment that delivers real-time results. Prepare for this transformation because your data platform and storage choices are about to undergo a re-platforming that happens once in 30 years.
With the MapR Converged Data Platform (CDP) and Cisco Unified Compute System (UCS), you can optimize today’s infrastructure and grow to take advantage of what’s next. Uncover the range of possibilities from re-platforming by intimately understanding your options for density, performance, functionality and more.
Drill can query JSON data stored in various data sources like HDFS, HBase, and Hive. It allows running SQL queries over JSON data without requiring a fixed schema. The document describes how Drill enables ad-hoc querying of JSON-formatted Yelp business review data using SQL, providing insights faster than traditional approaches.
Support en anglais diffusé lors de l'événement 100% IA organisé dans les locaux parisiens d'Iguane Solutions, le mardi 2 juillet 2024 :
- Présentation de notre plateforme IA plug and play : ses fonctionnalités avancées, telles que son interface utilisateur intuitive, son copilot puissant et des outils de monitoring performants.
- REX client : Cyril Janssens, CTO d’ easybourse, partage son expérience d’utilisation de notre plateforme IA plug & play.
Measuring the Impact of Network Latency at TwitterScyllaDB
Widya Salim and Victor Ma will outline the causal impact analysis, framework, and key learnings used to quantify the impact of reducing Twitter's network latency.
RPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptxSynapseIndia
Your comprehensive guide to RPA in healthcare for 2024. Explore the benefits, use cases, and emerging trends of robotic process automation. Understand the challenges and prepare for the future of healthcare automation
Comparison Table of DiskWarrior Alternatives.pdfAndrey Yasko
To help you choose the best DiskWarrior alternative, we've compiled a comparison table summarizing the features, pros, cons, and pricing of six alternatives.
TrustArc Webinar - 2024 Data Privacy Trends: A Mid-Year Check-InTrustArc
Six months into 2024, and it is clear the privacy ecosystem takes no days off!! Regulators continue to implement and enforce new regulations, businesses strive to meet requirements, and technology advances like AI have privacy professionals scratching their heads about managing risk.
What can we learn about the first six months of data privacy trends and events in 2024? How should this inform your privacy program management for the rest of the year?
Join TrustArc, Goodwin, and Snyk privacy experts as they discuss the changes we’ve seen in the first half of 2024 and gain insight into the concrete, actionable steps you can take to up-level your privacy program in the second half of the year.
This webinar will review:
- Key changes to privacy regulations in 2024
- Key themes in privacy and data governance in 2024
- How to maximize your privacy program in the second half of 2024
Scaling Connections in PostgreSQL Postgres Bangalore(PGBLR) Meetup-2 - MydbopsMydbops
This presentation, delivered at the Postgres Bangalore (PGBLR) Meetup-2 on June 29th, 2024, dives deep into connection pooling for PostgreSQL databases. Aakash M, a PostgreSQL Tech Lead at Mydbops, explores the challenges of managing numerous connections and explains how connection pooling optimizes performance and resource utilization.
Key Takeaways:
* Understand why connection pooling is essential for high-traffic applications
* Explore various connection poolers available for PostgreSQL, including pgbouncer
* Learn the configuration options and functionalities of pgbouncer
* Discover best practices for monitoring and troubleshooting connection pooling setups
* Gain insights into real-world use cases and considerations for production environments
This presentation is ideal for:
* Database administrators (DBAs)
* Developers working with PostgreSQL
* DevOps engineers
* Anyone interested in optimizing PostgreSQL performance
Contact info@mydbops.com for PostgreSQL Managed, Consulting and Remote DBA Services
Implementations of Fused Deposition Modeling in real worldEmerging Tech
The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries:
1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes.
2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions.
3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines.
4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors.
5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering.
6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands.
7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems.
8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering.
9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively.
Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.
Quantum Communications Q&A with Gemini LLM. These are based on Shannon's Noisy channel Theorem and offers how the classical theory applies to the quantum world.
The DealBook is our annual overview of the Ukrainian tech investment industry. This edition comprehensively covers the full year 2023 and the first deals of 2024.
Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Em...Erasmo Purificato
Slide of the tutorial entitled "Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends" held at UMAP'24: 32nd ACM Conference on User Modeling, Adaptation and Personalization (July 1, 2024 | Cagliari, Italy)
Quality Patents: Patents That Stand the Test of TimeAurora Consulting
Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality.
Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality.
Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality.
Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank?
** Episode Overview **
In this first episode of our quality series, Kristen Hansen and the panel discuss:
⦿ What do we mean when we say patent quality?
⦿ Why is patent quality important?
⦿ How to balance quality and budget
⦿ The importance of searching, continuations, and draftsperson domain expertise
⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications
https://www.aurorapatents.com/patently-strategic-podcast.html
BT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdfNeo4j
Presented at Gartner Data & Analytics, London Maty 2024. BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Join this session to hear their story, the lessons they learned along the way and how their future innovation plans include the exploration of uses of EKG + Generative AI.
Spark is really cool… This is NOT a silver bullet… This is another GREAT tool to have… Stop using hammers for putting in screws.
a significant amount of data needs to be quickly processed in near real time to gain insights. Common examples include activity stream data from a web or mobile application, time-stamped log data, transactional data, and event streams from sensor or device networks.
the stream-based approach applies processing to the data stream as it is generated. This allows near real-time processing
Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data. Spark Streaming is for use cases which require a significant amount of data to be quickly processed as soon as it arrives. Example real-time use cases are:
Website monitoring , Network monitoring
Fraud detection
Web clicks
Advertising
Internet of Things: sensors
Having data in log files is not good for real time processing
Its becoming important to process events as they arrive
Spark Streaming brings Spark's api to stream processing, letting you write streaming jobs the same way you write batch jobs.
Spark streaming supports data sources such as HDFS directories, TCP sockets, Twitter, message queues and distributed stream and log transfer frameworks, such as Flume, Kafka, and Amazon Kinesis.
Data Streams can be processed with Spark’s core APIS, Dataframes SQL, or machine learning APIs, and can be persisted to a filesystem, HDFS, MapR-FS, MapR-DB, HBase, or any data source offering a Hadoop OutputFormat or Spark connector.
DStreams can be created from various input sources, such as Flume, Kafka, or HDFS. Once built, they offer two types of operations: transformations, which yield a new DStream, and output operations, which write data to an external system.
A data stream is a continuous sequence of records or events. Common examples include activity stream data from a web or mobile application, time-stamped log data, transactional data, and event streams from sensor or device networks. the stream-based approach applies processing to the data stream as it is generated. This allows near real-time processing.
A stream processing architecture is typically made of a following components:
Data sources – source of data streams. examples are sensor network, mobile application, web client, a log from a server, or even a thing from Internet of Things.
Message bus – messaging systems such as Kafka and Flume ActiveMQ, RabbitMQ, etc.
Stream processing system – framework for processing data streams.
NoSql store for storing processed data must be capable of fast read and writes. HBase, Cassandra, MongoDb are popular choices.
End applications – dashboards and other applications which use the processed data.
DStreams can be created from various input sources, such as Flume, Kafka, or HDFS. Once built, they offer two types of operations: transformations, which yield a new DStream, and output operations, which write data to an external system.
Spark Streaming supports various input sources, including file-based sources and network-based sources such as socket-based sources, the Twitter API stream, Akka actors, or message queues and distributed stream and log transfer frameworks, such Flume, Kafka, and Amazon Kinesis.
Spark Streaming provides a set of transformations available on DStreams; these transformations are similar to those available on RDDs. These include map, flatMap, filter, join, and reduceByKey. Streaming also provides operators such as reduce and count. These operators return a DStream made up of a single element . Unlike the reduce and count operators on RDDs, these do not trigger computation on Dstreams , they are not actions, they return another DStream.
Stateful transformations maintain state across batches, they use data or intermediate results from previous batches to compute the results of the current batch. They include transformations based on sliding windows and on tracking state across time. updateStateByKey() is used to track state across events for each key. For example this could be used to maintain stats (state) by a key accessLogDStream.reduceByKey(SUM_REDUCER).
Actions are output operators that, when invoked, trigger computation on the DStream. They are as follows:
print: This prints the first 10 elements of each batch to the console and is typically used for debugging and testing.
saveAsObjectFile, saveAsTextFiles, and saveAsHadoopFiles: These functions output each batch to a Hadoop-compatible filesystem.
forEachRDD: This operator allows to apply any arbitrary processing to the RDDs within each batch of a DStream.
Streaming data is continuous and needs to be batched to process. Spark Streaming divides the data stream into batches of X seconds called Dstreams (discretized stream) Internally, each DStream is represented as a sequence of RDDs arriving at each time step.
A DStream is a sequence of mini-batches, where each mini-batch is represented as a Spark RDD . The stream is broken up into time periods equal to the batch interval
Each RDD in the stream will contain the records that are received by the Spark Streaming application during a given time window called the batch interval.
Spark Streaming receives data from various input sources and groups it into small batches called the batch interval. , Each input batch forms an RDD.
a Dstream is a sequence of RDDs, where each RDD has one time slice of the data in the stream.
Spark’s primary abstraction is a distributed collection of items called a Resilient Distributed Dataset (RDD). RDDs can be created from Hadoop InputFormats (such as HDFS files) or by transforming other RDDs.
An RDD is simply a distributed collection of elements. You can think of the distributed collections like of like an array or list in your single machine program, except that it’s spread out across multiple nodes in the cluster.
In Spark all work is expressed as either creating new RDDs, transforming existing RDDs, or calling operations on RDDs to compute a result. Under the hood, Spark automatically distributes the data contained in RDDs across your cluster and parallelizes the operations you perform on them.
So, Spark gives you APIs and functions that lets you do something on the whole collection in parallel using all the nodes.
actions, which return a value to the driver program after running a computation on the dataset
The chain of transformations from RDD1 to RDDn are logged, and can be
repeated in the event of data loss or the failure of a cluster node.
Your Spark Application processes the Dstream RDDs using Spark transformations like map, reduce, join … which create new RDDs.
Any operation applied on a DStream translates to operations on the underlying RDDs, which, in turn, applies the transformation to the elements of the RDD.
Streaming data is continuous and needs to be batched to process.
Spark Streaming divides the data stream into batches of X seconds called Dstreams (discretized stream), which is a sequence of RDDs.
Spark Application processes the RDDs using the Spark APIs
finally the processed results of the RDD operations are returned in batches.
Spark Streaming supports various input sources, including file-based sources and network-based sources such as socket-based sources, the Twitter API stream, Akka actors, or message queues and distributed stream and log transfer frameworks, such Flume, Kafka, and Amazon Kinesis.
Spark Streaming provides a set of transformations available on DStreams; these transformations are similar to those available on RDDs. These include map, flatMap, filter, join, and reduceByKey. Streaming also provides operators such as reduce and count. These operators return a DStream made up of a single element . Unlike the reduce and count operators on RDDs, these do not trigger computation on Dstreams , they are not actions, they return another DStream.
Stateful transformations maintain state across batches, they use data or intermediate results from previous batches to compute the results of the current batch. They include transformations based on sliding windows and on tracking state across time. updateStateByKey() is used to track state across events for each key. For example this could be used to maintain stats (state) by a key accessLogDStream.reduceByKey(SUM_REDUCER).
Actions are output operators that, when invoked, trigger computation on the DStream. They are as follows:
print: This prints the first 10 elements of each batch to the console and is typically used for debugging and testing.
saveAsObjectFile, saveAsTextFiles, and saveAsHadoopFiles: These functions output each batch to a Hadoop-compatible filesystem.
forEachRDD: This operator allows to apply any arbitrary processing to the RDDs within each batch of a DStream.
Streaming data is continuous and needs to be batched to process.
Spark Streaming divides the data stream into batches of X seconds called Dstreams (discretized stream), which is a sequence of RDDs.
Spark Application processes the RDDs using the Spark APIs
finally the processed results of the RDD operations are returned in batches.
Output operations are similar to RDD actions in that they write data to an external system, but in Spark Streaming they run periodically on each time step, producing output in batches.
Spark Streaming supports various input sources, including file-based sources and network-based sources such as socket-based sources, the Twitter API stream, Akka actors, or message queues and distributed stream and log transfer frameworks, such Flume, Kafka, and Amazon Kinesis.
Spark Streaming provides a set of transformations available on DStreams; these transformations are similar to those available on RDDs. These include map, flatMap, filter, join, and reduceByKey. Streaming also provides operators such as reduce and count. These operators return a DStream made up of a single element . Unlike the reduce and count operators on RDDs, these do not trigger computation on Dstreams , they are not actions, they return another DStream.
Stateful transformations maintain state across batches, they use data or intermediate results from previous batches to compute the results of the current batch. They include transformations based on sliding windows and on tracking state across time. updateStateByKey() is used to track state across events for each key. For example this could be used to maintain stats (state) by a key accessLogDStream.reduceByKey(SUM_REDUCER).
Actions are output operators that, when invoked, trigger computation on the DStream. They are as follows:
print: This prints the first 10 elements of each batch to the console and is typically used for debugging and testing.
saveAsObjectFile, saveAsTextFiles, and saveAsHadoopFiles: These functions output each batch to a Hadoop-compatible filesystem.
forEachRDD: This operator allows to apply any arbitrary processing to the RDDs within each batch of a DStream.
These are the basic steps for Spark Streaming code:
Initialize a Spark StreamingContext object.
Apply transformations and output operations to DStreams.
Start receiving data and processing it using streamingContext.start().
Wait for the processing to be stopped using streamingContext.awaitTermination().
We want to store every single event as it comes in, in a Hbase. We also want to filter for and store alarms. Daily Spark processing will store aggregated summary statistics
The Spark Streaming example does the following:
Reads streaming data.
Processes the streaming data.
Writes the processed data to an HBase Table.
(non Streaming) Spark code does the following:
Reads HBase Table data written by the streaming code
Calculates daily summary statistics
Writes summary statistics to the HBase table Column Family stats
The Oil Pump Sensor data comes in as comma separated value (csv) files dropped in a directory. Spark Streaming will monitor the directory and process any files created in that directory. (As stated before, Spark Streaming supports different streaming data sources; for simplicity, this example will use files.) Below is an example of the csv file with some sample data:
We use a Scala case class to define the Sensor schema corresponding to the sensor data csv files, and a parseSensor function to parse the comma separated values into the sensor case class.
The HBase Table Schema for the streaming data is as follows:
Composite row key of the pump name date and time stamp
Column Family data with columns corresponding to the input data fields Column Family alerts with columns corresponding to any filters for alarming values Note that the data and alert column families could be set to expire values after a certain amount of time.
The Schema for the daily statistics summary rollups is as follows:
Composite row key of the pump name and date
Column Family stats
These are the basic steps for Spark Streaming code:
Initialize a Spark StreamingContext object.
Using this context, create a Dstream which represents streaming data from a source
Apply transformations and/or output operations to DStreams.
Start receiving data and processing it using streamingContext.start().
Wait for the processing to be stopped using streamingContext.awaitTermination().
We will go through these steps showing code from our use case example
Spark Streaming programs are best run as standalone applications built using Maven or sbt.
First we create a StreamingContext, the main entry point for streaming functionality, with a 2 second batch interval.
Using this context, we can create a DStream that represents streaming data from a source .
In this example we use the StreamingContext textFileStream(directory) method to create an input stream that monitors a Hadoop-compatible file system for new files and processes any files created in that directory.
This ingestion type supports a workflow where new files are written to a landing directory and Spark Streaming is used to detect them, ingest them, and process the data. Only use this ingestion type with files that are moved or copied into a directory.
The linesDStream represents the stream of data, each record is a line of text. Internally a DStream is a sequence of RDDs, one RDD per batch interval.
Next we parse the lines of data into Sensor objects, with the map operation on the linesDStream.
The map operation applies the Sensor.parseSensor function on the RDDs in the linesDStream, resulting in RDDs of Sensor objects.
Any operation applied on a DStream translates to operations on the underlying RDDs. the map operation is applied on each RDD in the linesDStream to generate the RDDs of the sensorDStream.
Next we use the DStream foreachRDD method to apply processing to each RDD in this DStream. We filter the sensor objects for low psi to create an RDD of alert sensor objects
Here we join the alert data with pump vendor and maintenance information (the pump vendor and maintenance information was read in and cached before streaming . Each RDD is converted to a DataFrame , registered as a temporary table and then queried using SQL.
rdd.toDF().registerTempTable("sensor")
Next we use the DStream foreachRDD method to apply processing to each RDD in this DStream. We filter the sensor objects for low psi to create alerts, then we write the sensor and alert data to HBase by converting them to Put objects, and using the PairRDDFunctions saveAsHadoopDatasetmethod, which outputs the RDD to any Hadoop-supported storage system using a Hadoop Configuration object for that storage system (see Hadoop Configuration for HBase above).
The sensorRDD objects are converted to put objects then written to HBase.
To start receiving data, we must explicitly call start() on the StreamingContext, then call awaitTermination to wait for the streaming computation to finish.
The last way to leverage MapReduce in Hbase is to use the Hbase database as both a source and sink in our data flow. One example of this use case is to calculate summaries across the Hbase data and then store those summaries back in the Hbase database. For summary jobs where HBase is used as a source and a sink, then writes will be coming from the Reducer step.
This shows the Input and Output for the TableMapper class map method and the TableReducer class reduce method for reading from and to HBase.
A scan Result object and Row key are sent to the Mapper map method one row at a time, one or more Key Value pairs are output by the Map method . The Reducer reduce method receives a Key and an iterable list of corresponding values, and outputs a put object .
we register the DataFrame as a table. Registering it as a table allows us to use it in subsequent SQL statements.
Now we can inspect the data.
we register the DataFrame as a table. Registering it as a table allows us to use it in subsequent SQL statements.
Now we can inspect the data.
This post will help you get started using Apache Spark DataFrames with Scala on the MapR Sandbox. The new Spark DataFrames API is designed to make big data processing on tabular data easier. A Spark DataFrame is a distributed collection of data organized into named columns that provides operations to filter, group, or compute aggregates, and can be used with Spark SQL.