Apache Beam: A unified model for batch and stream processing data

Presenter: Kenn Knowles, Software Engineer, Google & Apache Beam (incubating) PPMC member Apache Beam (incubating) is a programming model and library for unified batch & streaming big data processing. This talk will cover the Beam programming model broadly, including its origin story and vision for the future. We will dig into how Beam separates concerns for authors of streaming data processing pipelines, isolating what you want to compute from where your data is distributed in time and when you want to produce output. Time permitting, we might dive deeper into what goes into building a Beam runner, for example atop Apache Apex.

It covers a brief introduction to Apache Kafka Connect, giving insights about its benefits,use cases, motivation behind building Kafka Connect.And also a short discussion on its architecture.

Change Data Capture CDC is a typical use case in Real-Time Data Warehousing. It tracks the data change log -binlog- of a relational database [OLTP], and replay these change log timely to an external storage to do Real-Time OLAP, such as delta/kudu. To implement a robust CDC streaming pipeline, lots of factors should be concerned, such as how to ensure data accuracy , how to process OLTP source schema changed, whether it is easy to build for variety databases with less code.

Kafka is an open-source distributed commit log service that provides high-throughput messaging functionality. It is designed to handle large volumes of data and different use cases like online and offline processing more efficiently than alternatives like RabbitMQ. Kafka works by partitioning topics into segments spread across clusters of machines, and replicates across these partitions for fault tolerance. It can be used as a central data hub or pipeline for collecting, transforming, and streaming data between systems and applications.

Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark’s built-in functions make it easy for developers to express complex computations. Delta Lake, on the other hand, is the best way to store structured data because it is a open-source storage layer that brings ACID transactions to Apache Spark and big data workloads Together, these can make it very easy to build pipelines in many common scenarios. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem that needs to be solved. Apache Spark, being a unified analytics engine doing both batch and stream processing, often provides multiples ways to solve the same problem. So understanding the requirements carefully helps you to architect your pipeline that solves your business needs in the most resource efficient manner. In this talk, I am going examine a number common streaming design patterns in the context of the following questions. WHAT are you trying to consume? What are you trying to produce? What is the final output that the business wants? What are your throughput and latency requirements? WHY do you really have those requirements? Would solving the requirements of the individual pipeline actually solve your end-to-end business requirements? HOW are going to architect the solution? And how much are you willing to pay for it? Clarity in understanding the ‘what and why’ of any problem can automatically much clarity on the ‘how’ to architect it using Structured Streaming and, in many cases, Delta Lake.

Google Cloud Dataflow is a fully managed cloud service for batch and streaming data processing. It provides a unified programming model and DSL that can process both batch and streaming data on platforms like Spark, Flink, and Google's managed Dataflow service. Dataflow uses concepts like pipelines, PCollections, transforms, and windowing to process data according to event time while controlling latency through triggers. The Dataflow service on Google Cloud provides integration with Google Cloud services and monitoring at a cost based on the number and type of workers used.

Kafka Streams is a client library for building distributed applications that process streaming data stored in Apache Kafka. It provides a high-level streams DSL that allows developers to express streaming applications as set of processing steps. Alternatively, developers can use the lower-level processor API to implement custom business logic. Kafka Streams handles tasks like fault-tolerance, scalability and state management. It represents data as streams for unbounded data or tables for bounded state. Common operations include transformations, aggregations, joins and table operations.

Introduction to Kafka streaming platform. Covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example. Lastly, we added some simple Java client examples for a Kafka Producer and a Kafka Consumer.

A brief introduction to Apache Kafka and describe its usage as a platform for streaming data. It will introduce some of the newer components of Kafka that will help make this possible, including Kafka Connect, a framework for capturing continuous data streams, and Kafka Streams, a lightweight stream processing library.

Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.

The document discusses the history and development of Apache Arrow, an open-source cross-language development platform for in-memory data. Some key points: - Arrow started in 2016 to optimize data transfer between systems using a standardized columnar memory format. - It has since expanded to include libraries in many languages, file formats like Arrow and Parquet, and distributed computing capabilities like Arrow Flight for RPC. - Over time, more projects have adopted Arrow as an internal data structure for improved performance, including Spark, DuckDB, and Streamlit. - Today Arrow is an ecosystem of interoperable components, with continued work on higher-level tools around databases, machine learning, and geospatial data.

XStream is Facebook's unified stream processing platform that provides a fully managed stream processing service. It was built using the Stylus C++ stream processing framework and uses a common SQL dialect called CoreSQL. XStream employs an interpretive execution model using the new Velox vectorized SQL evaluation engine for high performance. This provides a consistent and high efficiency stream processing platform to support diverse real-time use cases at planetary scale for Facebook.

Delta Lake is an open source storage layer that sits on top of data lakes and brings ACID transactions and reliability to Apache Spark. It addresses challenges with data lakes like lack of schema enforcement and transactions. Delta Lake provides features like ACID transactions, scalable metadata handling, schema enforcement and evolution, time travel/data versioning, and unified batch and streaming processing. Delta Lake stores data in Apache Parquet format and uses a transaction log to track changes and ensure consistency even for large datasets. It allows for updates, deletes, and merges while enforcing schemas during writes.

Real-time data beats slow data. That’s true for almost every use case. Nevertheless, enterprise architects build new infrastructures with the Lambda architecture that includes separate batch and real-time layers. This video explores why a single real-time pipeline, called Kappa architecture, is the better fit for many enterprise architectures. Real-world examples from companies such as Disney, Shopify, Uber, and Twitter explore the benefits of Kappa but also show how batch processing fits into this discussion positively without the need for a Lambda architecture. The main focus of the discussion is on Apache Kafka (and its ecosystem) as the de facto standard for event streaming to process data in motion (the key concept of Kappa), but the video also compares various technologies and vendors such as Confluent, Cloudera, IBM Red Hat, Apache Flink, Apache Pulsar, AWS Kinesis, Amazon MSK, Azure Event Hubs, Google Pub Sub, and more. Video recording of this presentation: https://youtu.be/j7D29eyysDw Further reading: https://www.kai-waehner.de/blog/2021/09/23/real-time-kappa-architecture-mainstream-replacing-batch-lambda/ https://www.kai-waehner.de/blog/2021/04/20/comparison-open-source-apache-kafka-vs-confluent-cloudera-red-hat-amazon-msk-cloud/ https://www.kai-waehner.de/blog/2021/05/09/kafka-api-de-facto-standard-event-streaming-like-amazon-s3-object-storage/

Apache Kafka becoming the message bus to transfer huge volumes of data from various sources into Hadoop. It's also enabling many real-time system frameworks and use cases. Managing and building clients around Apache Kafka can be challenging. In this talk, we will go through the best practices in deploying Apache Kafka in production. How to Secure a Kafka Cluster, How to pick topic-partitions and upgrading to newer versions. Migrating to new Kafka Producer and Consumer API. Also talk about the best practices involved in running a producer/consumer. In Kafka 0.9 release, we’ve added SSL wire encryption, SASL/Kerberos for user authentication, and pluggable authorization. Now Kafka allows authentication of users, access control on who can read and write to a Kafka topic. Apache Ranger also uses pluggable authorization mechanism to centralize security for Kafka and other Hadoop ecosystem projects. We will showcase open sourced Kafka REST API and an Admin UI that will help users in creating topics, re-assign partitions, Issuing Kafka ACLs and monitoring Consumer offsets.

Hive on Tez with LLAP (Late Loading Application) can achieve query processing speeds of over 100,000 queries per hour. Tuning various Hive and YARN parameters such as increasing the number of executor and I/O threads, memory allocation, and disabling consistent splits between LLAP daemons and data nodes was needed to reach this performance level on a test cluster of 45 nodes. Future work includes adding a web UI for monitoring LLAP clusters and implementing column-level access controls while allowing other frameworks like Spark to still access data through HiveServer2 and prevent direct access to HDFS for security reasons.

Kubernetes As of Spark 2.3, Spark can run on clusters managed by Kubernetes. we will describes the best practices about running Spark SQL on Kubernetes upon Tencent cloud includes how to deploy Kubernetes against public cloud platform to maximum resource utilization and how to tune configurations of Spark to take advantage of Kubernetes resource manager to achieve best performance. To evaluate performance, the TPC-DS benchmarking tool will be used to analysis performance impact of queries between configurations set. Speakers: Junjie Chen, Junping Du

This document summarizes a presentation about Apache Twill, which provides abstractions for building large-scale applications on Apache Hadoop YARN. It discusses why Twill was created to simplify developing on YARN, Twill's architecture and components, key features like real-time logging and elastic scaling, real-world uses at CDAP, and the Twill roadmap.

This document discusses Apache Twill, which aims to simplify developing distributed applications on YARN. Twill provides a Java thread-like programming model for YARN applications, avoiding the complexity of directly using YARN APIs. Key features of Twill include real-time logging, resource reporting, state recovery, elastic scaling, command messaging between tasks, service discovery, and support for executing bundled JAR applications on YARN. Twill handles communication with YARN and the Application Master while providing an easy-to-use API for application developers.

Apache Beam (unified Batch and strEAM processing!) is a new Apache incubator project. Originally based on years of experience developing Big Data infrastructure within Google (such as MapReduce, FlumeJava, and MillWheel), it has now been donated to the OSS community at large. Come learn about the fundamentals of out-of-order stream processing, and how Beam’s powerful tools for reasoning about time greatly simplify this complex task. Beam provides a model that allows developers to focus on the four important questions that must be answered by any stream processing pipeline: What results are being calculated? Where in event time are they calculated? When in processing time are they materialized? How do refinements of results relate? Furthermore, by cleanly separating these questions from runtime characteristics, Beam programs become portable across multiple runtime environments, both proprietary (e.g., Google Cloud Dataflow) and open-source (e.g., Flink, Spark, et al).

Apache Beam's new State API brings scalability and consistency to fine-grained stateful processing while remaining portable to any Beam runner. Aljoscha Krettek introduces the new state and timer features in Beam and shows how to use them to express common real-world use cases in a backend-agnostic manner.

Hadoop or Spark: is it an either-or proposition? An exodus away from Hadoop to Spark is picking up steam in the news headlines and talks! Away from marketing fluff and politics, this talk analyzes such news and claims from a technical perspective. In practical ways, while referring to components and tools from both Hadoop and Spark ecosystems, this talk will show that the relationship between Hadoop and Spark is not of an either-or type but can take different forms such as: evolution, transition, integration, alternation and complementarity.

Presentation that I gave at the '2014 Open-BDA Hadoop Summit' on November 18th, 2014 on Modern Data Architecture with Enterprise Hadoop

This talk given at the Hadoop Summit in San Jose on June 28, 2016, analyzes a few major trends in Big Data analytics. These are a few takeaways from this talk: - Adopt Apache Beam for easier development and portability between Big Data Execution Engines. - Adopt stream analytics for faster time to insight, competitive advantages and operational efficiency. - Accelerate your Big Data applications with In-Memory open source tools. - Adopt Rapid Application Development of Big Data applications: APIs, Notebooks, GUIs, Microservices… - Have Machine Learning part of your strategy or passively watch your industry completely transformed! - How to advance your strategy for hybrid integration between cloud and on-premise deployments?

Kafka, Apache Kafka evolved from an enterprise messaging system to a fully distributed streaming data platform (Kafka Core + Kafka Connect + Kafka Streams) for building streaming data pipelines and streaming data applications. This talk, that I gave at the Chicago Java Users Group (CJUG) on June 8th 2017, is mainly focusing on Kafka Streams, a lightweight open source Java library for building stream processing applications on top of Kafka using Kafka topics as input/output. You will learn more about the following: 1. Apache Kafka: a Streaming Data Platform 2. Overview of Kafka Streams: Before Kafka Streams? What is Kafka Streams? Why Kafka Streams? What are Kafka Streams key concepts? Kafka Streams APIs and code examples? 3. Writing, deploying and running your first Kafka Streams application 4. Code and Demo of an end-to-end Kafka-based Streaming Data Application 5. Where to go from here?

This introductory level talk is about Apache Flink: a multi-purpose Big Data analytics framework leading a movement towards the unification of batch and stream processing in the open source. With the many technical innovations it brings along with its unique vision and philosophy, it is considered the 4 G (4th Generation) of Big Data Analytics frameworks providing the only hybrid (Real-Time Streaming + Batch) open source distributed data processing engine supporting many use cases: batch, streaming, relational queries, machine learning and graph processing. In this talk, you will learn about: 1. What is Apache Flink stack and how it fits into the Big Data ecosystem? 2. How Apache Flink integrates with Hadoop and other open source tools for data input and output as well as deployment? 3. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark. 4. Who is using Apache Flink? 5. Where to learn more about Apache Flink?

This face to face talk about Apache Flink in Sao Paulo, Brazil is the first event of its kind in Latin America! It explains how Apache Flink 1.0 announced on March 8th, 2016 by the Apache Software Foundation (link), marks a new era of Big Data analytics and in particular Real-Time streaming analytics. The talk maps Flink's capabilities to real-world use cases that span multiples verticals such as: Financial Services, Healthcare, Advertisement, Oil and Gas, Retail and Telecommunications. In this talk, you learn more about: 1. What is Apache Flink Stack? 2. Batch vs. Streaming Analytics 3. Key Differentiators of Apache Flink for Streaming Analytics 4. Real-World Use Cases with Flink for Streaming Analytics 5. Who is using Flink? 6. Where do you go from here?

Apache Kafka evolved from an enterprise messaging system to a fully distributed streaming data platform for building real-time streaming data pipelines and streaming data applications without the need for other tools/clusters for data ingestion, storage and stream processing. In this talk you will learn more about: 1. A quick introduction to Kafka Core, Kafka Connect and Kafka Streams: What is and why? 2. Code and step-by-step instructions to build an end-to-end streaming data application using Apache Kafka

Flink vs. Spark: this is the slide deck of my talk at the 2015 Flink Forward conference in Berlin, Germany, on October 12, 2015. In this talk, we tried to compare Apache Flink vs. Apache Spark with focus on real-time stream processing. Your feedback and comments are much appreciated.

This document discusses streaming and parallel decision trees in Flink. It motivates the need for a classifier system that can learn from streaming data and classify both the streaming training data and new streaming data. It describes the architecture of keeping the classifier model fresh as new data streams in, allowing classification during the learning process in real-time. It also outlines decision tree algorithms and their implementation using Flink streaming.

Okkam is an Italian SME specializing in large-scale data integration using semantic technologies. It provides services for public administration and restaurants by building and managing very large entity-centric knowledge bases. Okkam uses Apache Flink as its data processing framework for tasks like domain reasoning, managing the RDF data lifecycle, detecting duplicate records, entity record linkage, and telemetry analysis by combining Flink with technologies like Parquet, Jena, Sesame, ELKiBi, HBase, Solr, MongoDB, and Weka. The presenters work at Okkam and will discuss their use of Flink in more detail in their session.

Capital One is a large consumer and commercial bank that wanted to improve its real-time monitoring of customer activity data to detect and resolve issues quickly. Its legacy solution was expensive, proprietary, and lacked real-time and advanced analytics capabilities. Capital One implemented a new solution using Apache Flink for its real-time stream processing abilities. Flink provided cost-effective, real-time event processing and advanced analytics on data streams to help meet Capital One's goals. It also aligned with the company's technology strategy of using open source solutions.

Everything is designed, yet some interactions are much better than others. What does it take to make a great experience? What are the areas that UX specialists focus on? How do skills in cognitive psycology, computer science and design come together? Carol introduces basic concepts in user experience design that you can use to improve the user's expeirence and/or clearly communicate with designers.

Google Cloud Dataflow is a fully managed service that allows users to build batch or streaming parallel data processing pipelines. It provides a unified programming model and SDKs in Java and Python to process data across Google Cloud Platform services like Pub/Sub, BigQuery, and Cloud Storage. The Cloud Dataflow service automatically optimizes and runs data pipelines at scale in a reliable, cost-effective manner without requiring operational management by the user.

http://flink-forward.org/kb_sessions/no-shard-left-behind-dynamic-work-rebalancing-in-apache-beam/ The Apache Beam (incubating) programming model is designed to support several advanced data processing features such as autoscaling and dynamic work rebalancing. In this talk, we will first explain how dynamic work rebalancing not only provides a general and robust solution to the problem of stragglers in traditional data processing pipelines, but also how it allows autoscaling to be truly effective. We will then present how dynamic work rebalancing works as implemented in Google Cloud Dataflow and which path other Apache Beam runners link Apache Flink can follow to benefit from it.

The document discusses Apache Beam, a solution for next generation data processing. It provides a unified programming model for both batch and streaming data processing. Beam allows data pipelines to be written once and run on multiple execution engines. The presentation covers common challenges with historical data processing approaches, how Beam addresses these issues, a demo of running a Beam pipeline on different engines, and how to get involved with the Apache Beam community.

At improve digital we collect and store large volumes of machine generated and behavioural data from our fleet of ad servers. For some time we have performed mostly batch processing through a data warehouse that combines traditional RDBMs (MySQL), columnar stores (Infobright, impala+parquet) and Hadoop. We wish to share our experiences in enhancing this capability with systems and techniques that process the data as streams in near-realtime. In particular we will cover: • The architectural need for an approach to data collection and distribution as a first-class capability • The different needs of the ingest pipeline required by streamed realtime data, the challenges faced in building these pipelines and how they forced us to start thinking about the concept of production-ready data. • The tools we used, in particular Apache Kafka as the message broker, Apache Samza for stream processing and Apache Avro to allow schema evolution; an essential element to handle data whose formats will change over time. • The unexpected capabilities enabled by this approach, including the value in using realtime alerting as a strong adjunct to data validation and testing. • What this has meant for our approach to analytics and how we are moving to online learning and realtime simulation. This is still a work in progress at Improve Digital with differing levels of production-deployed capability across the topics above. We feel our experiences can help inform others embarking on a similar journey and hopefully allow them to learn from our initiative in this space.

Data gravity is a reality when dealing with massive amounts and globally distributed systems. Processing this data requires distributed analytics processing across InterCloud. In this presentation we will share our real world experience with storing, routing, and processing big data workloads on Cisco Cloud Services and Amazon Web Services clouds.

Benjamin Wright-Jones, Simon Lidberg. Are you interested in near real-time data processing but confused about Azure capabilities and product positioning? Spark, StreamInsight, Storm (HDInsight) and Stream Analytics offer ways to ingest data but there is uncertainty about when and how we should use these capabilities. For example, what are the differences and key solution design decision points? Come to this session to learn about current and new near real-time data processing engines. Go to https://channel9.msdn.com/ to find the recording of this session.

4.6.16 AI&BigData Lab Upcoming events: goo.gl/I2gJ4H Как устроить анализ данных 40 млн. человек за 5 лет так, чтобы это выглядело почти в реальном времени.

Onyx is a data processing framework for Clojure that allows users to define workflows, functions, and windows to process streaming and batch data across distributed clusters. It uses concepts like peers, virtual peers, and Zookeeper for scheduling and Aeron for messaging. Users can write Onyx jobs in Clojure to perform ETL, analytics, and other data processing tasks in a declarative way.

This talk explores deploying a series of small and large batch and streaming pipelines locally, to Spark and Flink clusters and to Google Cloud Dataflow services to give the audience a feel for the portability of Beam, a new portable Big Data processing framework recently submitted by Google to the Apache foundation. This talk will look at how the programming model handles late arriving data in a stream with event time, windows, and triggers.

Unbounded, unordered, global­ scale datasets are increasingly common in day-­to-­day business, and consumers of these datasets have detailed requirements for latency, cost, and completeness. Apache Beam defines a new data processing programming model that evolved from more than a decade of experience building Big Data infrastructure within Google, including MapReduce, FlumeJava, Millwheel, and Cloud Dataflow. Apache Beam handles both batch and streaming use cases, offering a powerful, unified model. It neatly separates properties of the data from run-time characteristics, allowing pipelines to be portable across multiple run-time environments, both open ­source, including Apache Apex, Apache Flink, Apache Gearpump, Apache Spark, and proprietary. Finally, Beam's model enables newer optimizations, like dynamic work rebalancing and autoscaling, resulting in an efficient execution. This talk will cover the basics of Apache Beam, touch on its evolution, and describe main concepts in its powerful programming model. We'll show how Beam unifies batch and streaming use cases, and show efficient execution in real-world scenarios. Finally, we'll demonstrate pipeline portability across Apache Apex, Apache Flink, Apache Spark and Google Cloud Dataflow in a live setting.

This document summarizes a talk on cloud computing and bioinformatics given by Angel Pizarro. The talk discusses 3 pillars of systems design for cloud computing, 3 storage implementations (S3, distributed file systems on EC2, memory grids), and 3 areas of bioinformatics affected by clouds (computational biology, software engineering, applications to life sciences). It also summarizes 3 internal projects at UPenn related to resource management, workflow management, and an RNA-Seq analysis pipeline called RUM that combines Bowtie and BLAT.

This document discusses building a machine learning model for real-time time series analysis on big data. It describes using Spark and Kafka to ingest streaming sensor data and train a model to identify patterns and predict failures. The training phase identifies concepts in historical data to build a knowledge base. In real-time, incoming data is processed in microbatches to identify patterns and sequences matching the concepts, triggering alerts. Challenges addressed include handling large volumes of small files and sharing data between batches for signals spanning multiple batches.

With a current zoo of technologies and different ways of their interaction it's a big challenge to architect a system (or adopt existed one) that will conform to low-latency BigData analysis requirements. Apache Kafka and Kappa Architecture in particular take more and more attention over classic Hadoop-centric technologies stack. New Consumer API put significant boost in this direction. Microservices-based streaming processing and new Kafka Streams tend to be a synergy in BigData world.

This document contains an agenda and summaries of sections from a presentation on messaging architectures and using messaging data with Splunk. The agenda includes an overview of common messaging systems like JMS, AMQP, and Kafka. It also covers customizing message handling and scaling modular inputs. Additionally, it discusses using ZeroMQ to access messages and using underutilized computers with JMS tasks.

The world of big data involves an ever changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. In a way, Apache Beam is a glue that can connect the Big Data ecosystem together; it enables users to "run-anything-anywhere". This talk will briefly cover the capabilities of the Beam model for data processing, as well as the current state of the Beam ecosystem. We'll discuss Beam architecture and dive into the portability layer. We'll offer a technical analysis of the Beam's powerful primitive operations that enable true and reliable portability across diverse environments. Finally, we'll demonstrate a complex pipeline running on multiple runners in multiple deployment scenarios (e.g. Apache Spark on Amazon Web Services, Apache Flink on Google Cloud, Apache Apex on-premise), and give a glimpse at some of the challenges Beam aims to address in the future. Speaker Davor Bonaci, Senior Software Engineer, Google

The document summarizes a meetup on data streaming and machine learning with Google Cloud Platform. The meetup consisted of two presentations: 1. The first presentation discussed using Apache Beam (Dataflow) on Google Cloud Platform to parallelize machine learning training for improved performance. It showed how Dataflow was used to reduce training time from 12 hours to under 30 minutes. 2. The second presentation demonstrated building a streaming pipeline for sentiment analysis on Twitter data using Dataflow. It covered streaming patterns, batch vs streaming processing, and a demo that ingested tweets from PubSub and analyzed them using Cloud NLP API and BigQuery.

The document summarizes a meetup on data streaming and machine learning with Google Cloud Platform. The meetup consisted of two presentations: 1. The first presentation discussed using Apache Beam and Google Cloud Dataflow to parallelize machine learning training for hyperparameter optimization. It showed how Dataflow reduced training time from 12 hours to under 30 minutes. 2. The second presentation demonstrated building a streaming Twitter sentiment analysis pipeline with Dataflow. It covered streaming patterns, batch vs streaming considerations, and a demo that ingested tweets from PubSub, analyzed sentiment with NLP, and loaded results to BigQuery.

A plataforma Windows Azure abre espaço a desenvimento de aplicações utilizando o novo paradigma: "A Nuvem". Aplicações escaláveis, redundantes, e mais próximas do utilizador final. Isto tudo utilizando como base os conhecimentos que já tem e o novo Visual Studio 2010.

The document discusses data partitioning and distribution across multiple machines in a cluster. It explains that data replication does not scale well, but data partitioning, where each record exists on only one machine, allows write latency to scale with the number of machines in the cluster. Coherence provides a distributed cache that partitions data and offers functions for server-side processing near the data through tools like entry processors.

This document summarizes a presentation about building a structured streaming connector for continuous applications using Azure Event Hubs as the streaming data source. It discusses key design considerations like representing offsets, implementing the getOffset and getBatch methods required by structured streaming sources, and challenges with testing asynchronous behavior. It also outlines issues contributed back to the Apache Spark community around streaming checkpoints and recovery.

This document discusses running Apache Spark and Apache Zeppelin in production. It begins by introducing the author and their background. It then covers security best practices for Spark deployments, including authentication using Kerberos, authorization using Ranger/Sentry, encryption, and audit logging. Different Spark deployment modes like Spark on YARN are explained. The document also discusses optimizing Spark performance by tuning executor size and multi-tenancy. Finally, it covers security features for Apache Zeppelin like authentication, authorization, and credential management.