Apache Spark - Dataframes & Spark SQL - Part 1 | Big Data Hadoop Spark Tutorial | CloudxLab

The convergence of big data technology towards traditional database domain has became an industry trend. At present, open source big data processing engines, such as Apache Spark, Apache Hadoop, Apache Flink, etc., already support SQL interfaces, and the usage of SQL basically occupies a dominant position. Companies use above open source software to build their own ETL framework and OLAP technology. However, in terms of OLTP technology, it is still a strong point of traditional databases. One of the main reasons is the support of ACID by traditional databases.

Spark is an open source cluster computing framework for large-scale data processing. It provides high-level APIs and runs on Hadoop clusters. Spark components include Spark Core for execution, Spark SQL for SQL queries, Spark Streaming for real-time data, and MLlib for machine learning. The core abstraction in Spark is the resilient distributed dataset (RDD), which allows data to be partitioned across nodes for parallel processing. A word count example demonstrates how to use transformations like flatMap and reduceByKey to count word frequencies from an input file in Spark.

This document provides an overview and introduction to NoSQL databases. It begins with an agenda that explores key-value, document, column family, and graph databases. For each type, 1-2 specific databases are discussed in more detail, including their origins, features, and use cases. Key databases mentioned include Voldemort, CouchDB, MongoDB, HBase, Cassandra, and Neo4j. The document concludes with references for further reading on NoSQL databases and related topics.

View video of this presentation here: https://www.youtube.com/watch?v=vxeLcoELaP4 Introducing DataFrames in Spark for Large-scale Data Science

Spark Streaming allows processing of live data streams in Spark. It integrates streaming data and batch processing within the same Spark application. Spark SQL provides a programming abstraction called DataFrames and can be used to query structured data in Spark. Structured Streaming in Spark 2.0 provides a high-level API for building streaming applications on top of Spark SQL's engine. It allows running the same queries on streaming data as on batch data and unifies streaming, interactive, and batch processing.

Jim Boriotti presents an overview and demo of Azure Synapse Analytics, an integrated data platform for business intelligence, artificial intelligence, and continuous intelligence. Azure Synapse Analytics includes Synapse SQL for querying with T-SQL, Synapse Spark for notebooks in Python, Scala, and .NET, and Synapse Pipelines for data workflows. The demo shows how Azure Synapse Analytics provides a unified environment for all data tasks through the Synapse Studio interface.

This document discusses CQRS and event sourcing patterns for Java developers. It begins with an overview of classical monolithic architectures versus modern microservice architectures. It then contrasts CRUD with CQRS, explaining that CQRS separates reads from writes by using commands for writes and queries for reads. Events evolve from commands and represent things that occurred in the past. The document provides an example implementation using the Lagom framework that demonstrates separating the write side from the read side and persisting events. It emphasizes that with this approach, all state changes are stored as events and the current state can be recreated by replaying events. The document encourages the use of Lagom due to benefits like asynchronous programming, developer productivity, and production readiness.

This document proposes using support vector machines (SVMs) to model high-frequency limit order book dynamics and predict metrics like mid-price movement and price spread crossing. It describes representing each limit order book entry as a vector of attributes, then using multi-class SVMs to build models for each metric. Experiments on real data show the selected features are effective for short-term price forecasts. The document provides background on SVMs, describing how they find an optimal separating hyperplane to classify data points into labels.

This document provides an overview and summary of Amazon S3 best practices and tuning for Hadoop/Spark in the cloud. It discusses the relationship between Hadoop/Spark and S3, the differences between HDFS and S3 and their use cases, details on how S3 behaves from the perspective of Hadoop/Spark, well-known pitfalls and tunings related to S3 consistency and multipart uploads, and recent community activities related to S3. The presentation aims to help users optimize their use of S3 storage with Hadoop/Spark frameworks.

This session covers how to work with PySpark interface to develop Spark applications. From loading, ingesting, and applying transformation on the data. The session covers how to work with different data sources of data, apply transformation, python best practices in developing Spark Apps. The demo covers integrating Apache Spark apps, In memory processing capabilities, working with notebooks, and integrating analytics tools into Spark Applications.

Spark started at Facebook as an experiment when the project was still in its early phases. Spark's appeal stemmed from its ease of use and an integrated environment to run SQL, MLlib, and custom applications. At that time the system was used by a handful of people to process small amounts of data. However, we've come a long way since then. Currently, Spark is one of the primary SQL engines at Facebook in addition to being the primary system for writing custom batch applications. This talk will cover the story of how we optimized, tuned and scaled Apache Spark at Facebook to run on 10s of thousands of machines, processing 100s of petabytes of data, and used by 1000s of data scientists, engineers and product analysts every day. In this talk, we'll focus on three areas: * *Scaling Compute*: How Facebook runs Spark efficiently and reliably on tens of thousands of heterogenous machines in disaggregated (shared-storage) clusters. * *Optimizing Core Engine*: How we continuously tune, optimize and add features to the core engine in order to maximize the useful work done per second. * *Scaling Users:* How we make Spark easy to use, and faster to debug to seamlessly onboard new users. Speakers: Ankit Agarwal, Sameer Agarwal

This document provides an overview of Apache Spark, an open-source unified analytics engine for large-scale data processing. It discusses Spark's core APIs including RDDs and transformations/actions. It also covers Spark SQL, Spark Streaming, MLlib, and GraphX. Spark provides a fast and general engine for big data processing, with explicit operations for streaming, SQL, machine learning, and graph processing. The document includes installation instructions and examples of using various Spark components.

This document provides an overview and introduction to NoSQL databases. It discusses key-value stores like Dynamo and BigTable, which are distributed, scalable databases that sacrifice complex queries for availability and performance. It also explains column-oriented databases like Cassandra that scale to massive workloads. The document compares the CAP theorem and consistency models of these databases and provides examples of their architectures, data models, and operations.

This document describes the MapReduce programming model for processing large datasets in a distributed manner. MapReduce allows users to write map and reduce functions that are automatically parallelized and run across large clusters. The input data is split and the map tasks run in parallel, producing intermediate key-value pairs. These are shuffled and input to the reduce tasks, which produce the final output. The system handles failures, scheduling and parallelization transparently, making it easy for programmers to write distributed applications.

This document provides an overview of a talk on Apache Spark. It introduces the speaker and their background. It acknowledges inspiration from a previous Spark training. It then outlines the structure of the talk, which will include: a brief history of big data; a tour of Spark including its advantages over MapReduce; and explanations of Spark concepts like RDDs, transformations, and actions. The document serves to introduce the topics that will be covered in the talk.

The document introduces data engineering and provides an overview of the topic. It discusses (1) what data engineering is, how it has evolved with big data, and the required skills, (2) the roles of data engineers, data scientists, and data analysts in working with big data, and (3) the structure and schedule of an upcoming meetup on data engineering that will use an agile approach over monthly sprints.

This document provides a summary of improvements made to Hive's performance through the use of Apache Tez and other optimizations. Some key points include: - Hive was improved to use Apache Tez as its execution engine instead of MapReduce, reducing latency for interactive queries and improving throughput for batch queries. - Statistics collection was optimized to gather column-level statistics from ORC file footers, speeding up statistics gathering. - The cost-based optimizer Optiq was added to Hive, allowing it to choose better execution plans. - Vectorized query processing, broadcast joins, dynamic partitioning, and other optimizations improved individual query performance by over 100x in some cases.

Of all the developers’ delight, none is more attractive than a set of APIs that make developers productive, that are easy to use, and that are intuitive and expressive. Apache Spark offers these APIs across components such as Spark SQL, Streaming, Machine Learning, and Graph Processing to operate on large data sets in languages such as Scala, Java, Python, and R for doing distributed big data processing at scale. In this talk, I will explore the evolution of three sets of APIs-RDDs, DataFrames, and Datasets-available in Apache Spark 2.x. In particular, I will emphasize three takeaways: 1) why and when you should use each set as best practices 2) outline its performance and optimization benefits; and 3) underscore scenarios when to use DataFrames and Datasets instead of RDDs for your big data distributed processing. Through simple notebook demonstrations with API code examples, you’ll learn how to process big data using RDDs, DataFrames, and Datasets and interoperate among them. (this will be vocalization of the blog, along with the latest developments in Apache Spark 2.x Dataframe/Datasets and Spark SQL APIs: https://databricks.com/blog/2016/07/14/a-tale-of-three-apache-spark-apis-rdds-dataframes-and-datasets.html)

This session of the workshop introduces Spark SQL along with DataFrames, Datasets. Datasets give us the ability to easily intermix relational and functional style programming. So that we can explore the new Dataset API this iteration will be focused in Scala.

This document provides an agenda for a 3+ hour workshop on Apache Spark 2.x on Databricks. It includes introductions to Databricks, Spark fundamentals and architecture, new features in Spark 2.0 like unified APIs, and workshops on DataFrames/Datasets, Spark SQL, and structured streaming concepts. The agenda covers lunch and breaks and is divided into hour and half hour segments.

Spark Datasets are an evolution of Spark DataFrames which allow us to work with both functional and relational transformations on big data with the speed of Spark.

This chapter discusses Spark SQL, which allows querying Spark data with SQL. It covers initializing Spark SQL, loading data from sources like Hive, Parquet, JSON and RDDs, caching data, writing UDFs, and performance tuning. The JDBC server allows sharing cached tables and queries between programs. SchemaRDDs returned by queries or loaded from data represent the data structure that SQL queries operate on.

This document discusses how Spark and Cassandra can be used together. It begins with an introduction to Spark and Cassandra individually, explaining their architectures and key features. It then details the Spark-Cassandra connector, describing how Cassandra tables can be exposed as Spark RDDs and DataFrames. Various use cases for Spark and Cassandra are presented, including data cleaning, schema migration, and analytics. The document emphasizes the importance of data locality when performing joins and writes between Spark and Cassandra. Code examples are provided for common tasks like data cleaning, migration, and analytics.

This document provides an introduction and overview of Apache Spark with Python (PySpark). It discusses key Spark concepts like RDDs, DataFrames, Spark SQL, Spark Streaming, GraphX, and MLlib. It includes code examples demonstrating how to work with data using PySpark for each of these concepts.

Introduction and background Spark RDD API Introduction to Scala Spark DataFrames API + SparkSQL Spark Execution Model Spark Shell & Application Deployment Spark Extensions (Spark Streaming, MLLib, ML) Spark & DataStax Enterprise Integration Demos

The document discusses a meetup about building modern applications with DataFrames in Spark. It provides an agenda for the meetup that includes an introduction to Spark and DataFrames, a discussion of the Catalyst internals, and a demo. The document also provides background on Spark, noting its open source nature and large-scale usage by many organizations.

The document discusses a meetup about building modern applications with DataFrames in Spark. It provides an agenda for the meetup that includes an introduction to Spark and DataFrames, a discussion of the Catalyst internals, and a demo. The document also provides background on Spark, noting its open source nature and large-scale usage by many organizations.

The document discusses loading data into Spark SQL and the differences between DataFrame functions and SQL. It provides examples of loading data from files, cloud storage, and directly into DataFrames from JSON and Parquet files. It also demonstrates using SQL on DataFrames after registering them as temporary views. The document outlines how to load data into RDDs and convert them to DataFrames to enable SQL querying, as well as using SQL-like functions directly in the DataFrame API.

- Apache Spark is an open-source cluster computing framework that provides fast, in-memory processing for large-scale data analytics. It can run on Hadoop clusters and standalone. - Spark allows processing of data using transformations and actions on resilient distributed datasets (RDDs). RDDs can be persisted in memory for faster processing. - Spark comes with modules for SQL queries, machine learning, streaming, and graphs. Spark SQL allows SQL queries on structured data. MLib provides scalable machine learning. Spark Streaming processes live data streams.

- Apache Spark is an open-source cluster computing framework that provides fast, general engine for large-scale data processing. It introduces Resilient Distributed Datasets (RDDs) that allow in-memory processing for speed. - The document discusses Spark's key concepts like transformations, actions, and directed acyclic graphs (DAGs) that represent Spark job execution. It also summarizes Spark SQL, MLlib, and Spark Streaming modules. - The presenter is a solutions architect who provides an overview of Spark and how it addresses limitations of Hadoop by enabling faster, in-memory processing using RDDs and a more intuitive API compared to MapReduce.

Apache Spark 2.0 and subsequent releases of Spark 2.1 and 2.2 have laid the foundation for many new features and functionality. Its main three themes—easier, faster, and smarter—are pervasive in its unified and simplified high-level APIs for Structured data. In this introductory part lecture and part hands-on workshop, you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas: Agenda: • Overview of Spark Fundamentals & Architecture • What’s new in Spark 2.x • Unified APIs: SparkSessions, SQL, DataFrames, Datasets • Introduction to DataFrames, Datasets and Spark SQL • Introduction to Structured Streaming Concepts • Four Hands-On Labs

This document provides an overview of Spark and using Spark on HDInsight. It discusses Spark concepts like RDDs, transformations, and actions. It also covers Spark extensions like Spark SQL, Spark Streaming, and MLlib. Finally, it highlights benefits of using Spark on HDInsight like integration with Azure services, scalability, and support.

Apache Spark is an open-source parallel processing framework that supports in-memory processing to boost the performance of big-data analytic applications. We will cover approaches of processing Big Data on Spark cluster for real time analytic, machine learning and iterative BI and also discuss the pros and cons of using Spark in Azure cloud.

Abstract:- Of all the developers delight, none is more attractive than a set of APIs that make developers productive, that are easy to use, and that are intuitive and expressive. Apache Spark offers these APIs across components such as Spark SQL, Streaming, Machine Learning, and Graph Processing to operate on large data sets in languages such as Scala, Java, Python, and R for doing distributed big data processing at scale. In this talk, I will explore the evolution of three sets of APIs - RDDs, DataFrames, and Datasets available in Apache Spark 2.x. In particular, I will emphasize why and when you should use each set as best practices, outline its performance and optimization benefits, and underscore scenarios when to use DataFrames and Datasets instead of RDDs for your big data distributed processing. Through simple notebook demonstrations with API code examples, you'll learn how to process big data using RDDs, DataFrames, and Datasets and interoperate among them.

This document provides a summary of Apache Spark, including: - Spark is a framework for large-scale data processing across clusters that is faster than Hadoop by relying more on RAM and minimizing disk IO. - Spark transformations operate on resilient distributed datasets (RDDs) to manipulate data, while actions return results to the driver program. - Spark can receive data from various sources like files, databases, sockets through its datasource APIs and process both batch and streaming data. - Spark streaming divides streaming data into micro-batches called DStreams and integrates with messaging systems like Kafka. Structured streaming is a newer API that works on DataFrames/Datasets.

SparkR provides an R interface to Apache Spark that allows R developers to leverage Spark's distributed processing capabilities through DataFrames. DataFrames impose a schema on data from RDDs, making the data easier to access and manipulate compared to raw RDDs. DataFrames in SparkR allow R-like syntax and interactions with data while leveraging Spark's optimizations by passing operations to the JVM. The speaker demonstrated SparkR DataFrame features and discussed the project's roadmap, including expanding SparkR to support Spark machine learning capabilities.

The document describes the steps to configure Oracle sharding in an Oracle 12c environment. It includes installing Oracle software on shardcat, shard1, and shard2 nodes, creating an SCAT database, installing the GSM software, configuring the shard catalog, registering the shard nodes, creating a shard group and adding shards, deploying the shards to create databases on shard1 and shard2, verifying the shard configuration, creating a global service, and creating a sample schema and shard table to verify distribution across shards.

Computer vision is a branch of computer science which deals with recognising objects, people and identifying patterns in visuals. It is basically analogous to the vision of an animal. Topics covered: 1. Overview of Machine Learning 2. Basics of Deep Learning 3. What is computer vision and its use-cases? 4. Various algorithms used in Computer Vision (mostly CNN) 5. Live hands-on demo of either Auto Cameraman or Face recognition system 6. What next?

This document provides an agenda for an introduction to deep learning presentation. It begins with an introduction to basic AI, machine learning, and deep learning terms. It then briefly discusses use cases of deep learning. The document outlines how to approach a deep learning problem, including which tools and algorithms to use. It concludes with a question and answer section.

This document discusses recurrent neural networks (RNNs) and their applications. It begins by explaining that RNNs can process input sequences of arbitrary lengths, unlike other neural networks. It then provides examples of RNN applications, such as predicting time series data, autonomous driving, natural language processing, and music generation. The document goes on to describe the fundamental concepts of RNNs, including recurrent neurons, memory cells, and different types of RNN architectures for processing input/output sequences. It concludes by demonstrating how to implement basic RNNs using TensorFlow's static_rnn function.

Natural Language Processing (NLP) is a field of artificial intelligence that deals with interactions between computers and human languages. NLP aims to program computers to process and analyze large amounts of natural language data. Some common NLP tasks include speech recognition, text classification, machine translation, question answering, and more. Popular NLP tools include Stanford CoreNLP, NLTK, OpenNLP, and TextBlob. Vectorization is commonly used to represent text in a way that can be used for machine learning algorithms like calculating text similarity. Tf-idf is a common technique used to weigh words based on their frequency and importance.

- Naive Bayes is a classification technique based on Bayes' theorem that uses "naive" independence assumptions. It is easy to build and can perform well even with large datasets. - It works by calculating the posterior probability for each class given predictor values using the Bayes theorem and independence assumptions between predictors. The class with the highest posterior probability is predicted. - It is commonly used for text classification, spam filtering, and sentiment analysis due to its fast performance and high success rates compared to other algorithms.