This presentations is first in the series of Apache Spark tutorials and covers the basics of Spark framework.Subscribe to my youtube channel for more updates https://www.youtube.com/channel/UCNCbLAXe716V2B7TEsiWcoA
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.
Teaching Apache Spark: Demonstrations on the Databricks Cloud PlatformYao Yao
Yao Yao Mooyoung Lee
https://github.com/yaowser/learn-spark/tree/master/Final%20project
https://www.youtube.com/watch?v=IVMbSDS4q3A
https://www.academia.edu/35646386/Teaching_Apache_Spark_Demonstrations_on_the_Databricks_Cloud_Platform
https://www.slideshare.net/YaoYao44/teaching-apache-spark-demonstrations-on-the-databricks-cloud-platform-86063070/
Apache Spark is a fast and general engine for big data analytics processing with libraries for SQL, streaming, and advanced analytics
Cloud Computing, Structured Streaming, Unified Analytics Integration, End-to-End Applications
Spark SQL provides relational data processing capabilities in Spark. It introduces a DataFrame API that allows both relational operations on external data sources and Spark's built-in distributed collections. The Catalyst optimizer improves performance by applying database query optimization techniques. It is highly extensible, making it easy to add data sources, optimization rules, and data types for domains like machine learning. Spark SQL evaluation shows it outperforms alternative systems on both SQL query processing and Spark program workloads involving large datasets.
Apache Spark is a fast and general engine for large-scale data processing. It was originally developed in 2009 and is now supported by Databricks. Spark provides APIs in Java, Scala, Python and can run on Hadoop, Mesos, standalone or in the cloud. It provides high-level APIs like Spark SQL, MLlib, GraphX and Spark Streaming for structured data processing, machine learning, graph analytics and stream processing.
Jump Start into Apache® Spark™ and DatabricksDatabricks
These are the slides from the Jump Start into Apache Spark and Databricks webinar on February 10th, 2016.
---
Spark is a fast, easy to use, and unified engine that allows you to solve many Data Sciences and Big Data (and many not-so-Big Data) scenarios easily. Spark comes packaged with higher-level libraries, including support for SQL queries, streaming data, machine learning, and graph processing. We will leverage Databricks to quickly and easily demonstrate, visualize, and debug our code samples; the notebooks will be available for you to download.
In this talk, we’ll discuss technical designs of support of HBase as a “native” data source to Spark SQL to achieve both query and load performance and scalability: near-precise execution locality of query and loading, fine-tuned partition pruning, predicate pushdown, plan execution through coprocessor, and optimized and fully parallelized bulk loader. Point and range queries on dimensional attributes will benefit particularly well from the techniques. Preliminary test results vs. established SQL-on-HBase technologies will be provided. The speaker will also share the future plan and real-world use cases, particularly in the telecom industry.
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.
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.
Teaching Apache Spark: Demonstrations on the Databricks Cloud PlatformYao Yao
Yao Yao Mooyoung Lee
https://github.com/yaowser/learn-spark/tree/master/Final%20project
https://www.youtube.com/watch?v=IVMbSDS4q3A
https://www.academia.edu/35646386/Teaching_Apache_Spark_Demonstrations_on_the_Databricks_Cloud_Platform
https://www.slideshare.net/YaoYao44/teaching-apache-spark-demonstrations-on-the-databricks-cloud-platform-86063070/
Apache Spark is a fast and general engine for big data analytics processing with libraries for SQL, streaming, and advanced analytics
Cloud Computing, Structured Streaming, Unified Analytics Integration, End-to-End Applications
Spark SQL provides relational data processing capabilities in Spark. It introduces a DataFrame API that allows both relational operations on external data sources and Spark's built-in distributed collections. The Catalyst optimizer improves performance by applying database query optimization techniques. It is highly extensible, making it easy to add data sources, optimization rules, and data types for domains like machine learning. Spark SQL evaluation shows it outperforms alternative systems on both SQL query processing and Spark program workloads involving large datasets.
Apache Spark is a fast and general engine for large-scale data processing. It was originally developed in 2009 and is now supported by Databricks. Spark provides APIs in Java, Scala, Python and can run on Hadoop, Mesos, standalone or in the cloud. It provides high-level APIs like Spark SQL, MLlib, GraphX and Spark Streaming for structured data processing, machine learning, graph analytics and stream processing.
Jump Start into Apache® Spark™ and DatabricksDatabricks
These are the slides from the Jump Start into Apache Spark and Databricks webinar on February 10th, 2016.
---
Spark is a fast, easy to use, and unified engine that allows you to solve many Data Sciences and Big Data (and many not-so-Big Data) scenarios easily. Spark comes packaged with higher-level libraries, including support for SQL queries, streaming data, machine learning, and graph processing. We will leverage Databricks to quickly and easily demonstrate, visualize, and debug our code samples; the notebooks will be available for you to download.
In this talk, we’ll discuss technical designs of support of HBase as a “native” data source to Spark SQL to achieve both query and load performance and scalability: near-precise execution locality of query and loading, fine-tuned partition pruning, predicate pushdown, plan execution through coprocessor, and optimized and fully parallelized bulk loader. Point and range queries on dimensional attributes will benefit particularly well from the techniques. Preliminary test results vs. established SQL-on-HBase technologies will be provided. The speaker will also share the future plan and real-world use cases, particularly in the telecom industry.
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.
This document provides an overview and comparison of Apache Hadoop and Apache Spark for big data analytics. It discusses the architectures and functionality of Hadoop MapReduce and HDFS, as well as Spark's RDDs, transformations, and actions. The document demonstrates K-means clustering in both Spark and Hadoop MapReduce and shows that Spark outperforms Hadoop MapReduce, especially for iterative algorithms. While Hadoop remains useful for its features, the combination of Spark and HDFS can achieve high performance for both batch and interactive analytics.
In this one day workshop, we will introduce Spark at a high level context. Spark is fundamentally different than writing MapReduce jobs so no prior Hadoop experience is needed. You will learn how to interact with Spark on the command line and conduct rapid in-memory data analyses. We will then work on writing Spark applications to perform large cluster-based analyses including SQL-like aggregations, machine learning applications, and graph algorithms. The course will be conducted in Python using PySpark.
Spark SQL Deep Dive @ Melbourne Spark MeetupDatabricks
This document summarizes a presentation on Spark SQL and its capabilities. Spark SQL allows users to run SQL queries on Spark, including HiveQL queries with UDFs, UDAFs, and SerDes. It provides a unified interface for reading and writing data in various formats. Spark SQL also allows users to express common operations like selecting columns, joining data, and aggregation concisely through its DataFrame API. This reduces the amount of code users need to write compared to lower-level APIs like RDDs.
Founding committer of Spark, Patrick Wendell, gave this talk at 2015 Strata London about Apache Spark.
These slides provides an introduction to Spark, and delves into future developments, including DataFrames, Datasource API, Catalyst logical optimizer, and Project Tungsten.
This document introduces Spark SQL and the Catalyst query optimizer. It discusses that Spark SQL allows executing SQL on Spark, builds SchemaRDDs, and optimizes query execution plans. It then provides details on how Catalyst works, including its use of logical expressions, operators, and rules to transform query trees and optimize queries. Finally, it outlines some interesting open issues and how to contribute to Spark SQL's development.
End-to-end Data Pipeline with Apache SparkDatabricks
This document discusses Apache Spark, a fast and general cluster computing system. It summarizes Spark's capabilities for machine learning workflows, including feature preparation, model training, evaluation, and production use. It also outlines new high-level APIs for data science in Spark, including DataFrames, machine learning pipelines, and an R interface, with the goal of making Spark more similar to single-machine libraries like SciKit-Learn. These new APIs are designed to make Spark easier to use for machine learning and interactive data analysis.
This document provides an overview and introduction to Spark, including:
- Spark is a general purpose computational framework that provides more flexibility than MapReduce while retaining properties like scalability and fault tolerance.
- Spark concepts include resilient distributed datasets (RDDs), transformations that create new RDDs lazily, and actions that run computations and return values to materialize RDDs.
- Spark can run on standalone clusters or as part of Cloudera's Enterprise Data Hub, and examples of its use include machine learning, streaming, and SQL queries.
Join operations in Apache Spark is often the biggest source of performance problems and even full-blown exceptions in Spark. After this talk, you will understand the two most basic methods Spark employs for joining DataFrames – to the level of detail of how Spark distributes the data within the cluster. You’ll also find out how to work out common errors and even handle the trickiest corner cases we’ve encountered! After this talk, you should be able to write performance joins in Spark SQL that scale and are zippy fast!
This session will cover different ways of joining tables in Apache Spark.
Speaker: Vida Ha
This talk was originally presented at Spark Summit East 2017.
A concentrated look at Apache Spark's library Spark SQL including background information and numerous Scala code examples of using Spark SQL with CSV, JSON and databases such as mySQL.
The document provides an agenda and overview for a Big Data Warehousing meetup hosted by Caserta Concepts. The meetup agenda includes an introduction to SparkSQL with a deep dive on SparkSQL and a demo. Elliott Cordo from Caserta Concepts will provide an introduction and overview of Spark as well as a demo of SparkSQL. The meetup aims to share stories in the rapidly changing big data landscape and provide networking opportunities for data professionals.
Apache® Spark™ 1.6 presented by Databricks co-founder Patrick WendellDatabricks
In this webcast, Patrick Wendell from Databricks will be speaking about Apache Spark's new 1.6 release.
Spark 1.6 will include (but not limited to) a type-safe API called Dataset on top of DataFrames that leverages all the work in Project Tungsten to have more robust and efficient execution (including memory management, code generation, and query optimization) [SPARK-9999], adaptive query execution [SPARK-9850], and unified memory management by consolidating cache and execution memory [SPARK-10000].
This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
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.
Spark Application Carousel: Highlights of Several Applications Built with SparkDatabricks
This talk from 2015 Spark Summit East covers 3 applications built with Apache Spark:
1. Web Logs Analysis: Basic Data Pipeline - Spark & Spark SQL
2. Wikipedia Dataset Analysis: Machine Learning
3. Facebook API: Graph Algorithms
SparkSQL is a Spark component that allows SQL queries to be executed on Spark. It uses Catalyst, which provides an execution planning framework for relational operations like SQL parsing, logical optimization, and physical planning. Catalyst defines logical and physical operators, expressions, data types and provides rule-based optimizations of the logical query plan. The SQL core in SparkSQL converts logical plans to physical plans and enables reading/writing to data sources like Parquet files and in-memory columnar tables.
A Tale of Three Apache Spark APIs: RDDs, DataFrames and Datasets by Jules DamjiData Con LA
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.
2021 04-20 apache arrow and its impact on the database industry.pptxAndrew Lamb
The talk will motivate why Apache Arrow and related projects (e.g. DataFusion) is a good choice for implementing modern analytic database systems. It reviews the major components in most databases and explains where Apache Arrow fits in, and explains additional integration benefits from using Arrow.
What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial ...Simplilearn
This presentation about Apache Spark covers all the basics that a beginner needs to know to get started with Spark. It covers the history of Apache Spark, what is Spark, the difference between Hadoop and Spark. You will learn the different components in Spark, and how Spark works with the help of architecture. You will understand the different cluster managers on which Spark can run. Finally, you will see the various applications of Spark and a use case on Conviva. Now, let's get started with what is Apache Spark.
Below topics are explained in this Spark presentation:
1. History of Spark
2. What is Spark
3. Hadoop vs Spark
4. Components of Apache Spark
5. Spark architecture
6. Applications of Spark
7. Spark usecase
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Apache Spark is a lightning-fast cluster computing technology, designed for fast computation. It extends the MapReduce model of Hadoop to efficiently use it for more types of computations, which includes interactive queries and stream processing.
Spark is one of Hadoop's subproject developed in 2009 in UC Berkeley's AMPLab by Matei Zaharia. It was Open Sourced in 2010 under a BSD license. It was donated to Apache software foundation in 2013, and now Apache Spark has become a top-level Apache project from Feb-2014.
This document shares some basic knowledge about Apache Spark.
This document provides an overview and comparison of Apache Hadoop and Apache Spark for big data analytics. It discusses the architectures and functionality of Hadoop MapReduce and HDFS, as well as Spark's RDDs, transformations, and actions. The document demonstrates K-means clustering in both Spark and Hadoop MapReduce and shows that Spark outperforms Hadoop MapReduce, especially for iterative algorithms. While Hadoop remains useful for its features, the combination of Spark and HDFS can achieve high performance for both batch and interactive analytics.
In this one day workshop, we will introduce Spark at a high level context. Spark is fundamentally different than writing MapReduce jobs so no prior Hadoop experience is needed. You will learn how to interact with Spark on the command line and conduct rapid in-memory data analyses. We will then work on writing Spark applications to perform large cluster-based analyses including SQL-like aggregations, machine learning applications, and graph algorithms. The course will be conducted in Python using PySpark.
Spark SQL Deep Dive @ Melbourne Spark MeetupDatabricks
This document summarizes a presentation on Spark SQL and its capabilities. Spark SQL allows users to run SQL queries on Spark, including HiveQL queries with UDFs, UDAFs, and SerDes. It provides a unified interface for reading and writing data in various formats. Spark SQL also allows users to express common operations like selecting columns, joining data, and aggregation concisely through its DataFrame API. This reduces the amount of code users need to write compared to lower-level APIs like RDDs.
Founding committer of Spark, Patrick Wendell, gave this talk at 2015 Strata London about Apache Spark.
These slides provides an introduction to Spark, and delves into future developments, including DataFrames, Datasource API, Catalyst logical optimizer, and Project Tungsten.
This document introduces Spark SQL and the Catalyst query optimizer. It discusses that Spark SQL allows executing SQL on Spark, builds SchemaRDDs, and optimizes query execution plans. It then provides details on how Catalyst works, including its use of logical expressions, operators, and rules to transform query trees and optimize queries. Finally, it outlines some interesting open issues and how to contribute to Spark SQL's development.
End-to-end Data Pipeline with Apache SparkDatabricks
This document discusses Apache Spark, a fast and general cluster computing system. It summarizes Spark's capabilities for machine learning workflows, including feature preparation, model training, evaluation, and production use. It also outlines new high-level APIs for data science in Spark, including DataFrames, machine learning pipelines, and an R interface, with the goal of making Spark more similar to single-machine libraries like SciKit-Learn. These new APIs are designed to make Spark easier to use for machine learning and interactive data analysis.
This document provides an overview and introduction to Spark, including:
- Spark is a general purpose computational framework that provides more flexibility than MapReduce while retaining properties like scalability and fault tolerance.
- Spark concepts include resilient distributed datasets (RDDs), transformations that create new RDDs lazily, and actions that run computations and return values to materialize RDDs.
- Spark can run on standalone clusters or as part of Cloudera's Enterprise Data Hub, and examples of its use include machine learning, streaming, and SQL queries.
Join operations in Apache Spark is often the biggest source of performance problems and even full-blown exceptions in Spark. After this talk, you will understand the two most basic methods Spark employs for joining DataFrames – to the level of detail of how Spark distributes the data within the cluster. You’ll also find out how to work out common errors and even handle the trickiest corner cases we’ve encountered! After this talk, you should be able to write performance joins in Spark SQL that scale and are zippy fast!
This session will cover different ways of joining tables in Apache Spark.
Speaker: Vida Ha
This talk was originally presented at Spark Summit East 2017.
A concentrated look at Apache Spark's library Spark SQL including background information and numerous Scala code examples of using Spark SQL with CSV, JSON and databases such as mySQL.
The document provides an agenda and overview for a Big Data Warehousing meetup hosted by Caserta Concepts. The meetup agenda includes an introduction to SparkSQL with a deep dive on SparkSQL and a demo. Elliott Cordo from Caserta Concepts will provide an introduction and overview of Spark as well as a demo of SparkSQL. The meetup aims to share stories in the rapidly changing big data landscape and provide networking opportunities for data professionals.
Apache® Spark™ 1.6 presented by Databricks co-founder Patrick WendellDatabricks
In this webcast, Patrick Wendell from Databricks will be speaking about Apache Spark's new 1.6 release.
Spark 1.6 will include (but not limited to) a type-safe API called Dataset on top of DataFrames that leverages all the work in Project Tungsten to have more robust and efficient execution (including memory management, code generation, and query optimization) [SPARK-9999], adaptive query execution [SPARK-9850], and unified memory management by consolidating cache and execution memory [SPARK-10000].
This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
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.
Spark Application Carousel: Highlights of Several Applications Built with SparkDatabricks
This talk from 2015 Spark Summit East covers 3 applications built with Apache Spark:
1. Web Logs Analysis: Basic Data Pipeline - Spark & Spark SQL
2. Wikipedia Dataset Analysis: Machine Learning
3. Facebook API: Graph Algorithms
SparkSQL is a Spark component that allows SQL queries to be executed on Spark. It uses Catalyst, which provides an execution planning framework for relational operations like SQL parsing, logical optimization, and physical planning. Catalyst defines logical and physical operators, expressions, data types and provides rule-based optimizations of the logical query plan. The SQL core in SparkSQL converts logical plans to physical plans and enables reading/writing to data sources like Parquet files and in-memory columnar tables.
A Tale of Three Apache Spark APIs: RDDs, DataFrames and Datasets by Jules DamjiData Con LA
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.
2021 04-20 apache arrow and its impact on the database industry.pptxAndrew Lamb
The talk will motivate why Apache Arrow and related projects (e.g. DataFusion) is a good choice for implementing modern analytic database systems. It reviews the major components in most databases and explains where Apache Arrow fits in, and explains additional integration benefits from using Arrow.
What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial ...Simplilearn
This presentation about Apache Spark covers all the basics that a beginner needs to know to get started with Spark. It covers the history of Apache Spark, what is Spark, the difference between Hadoop and Spark. You will learn the different components in Spark, and how Spark works with the help of architecture. You will understand the different cluster managers on which Spark can run. Finally, you will see the various applications of Spark and a use case on Conviva. Now, let's get started with what is Apache Spark.
Below topics are explained in this Spark presentation:
1. History of Spark
2. What is Spark
3. Hadoop vs Spark
4. Components of Apache Spark
5. Spark architecture
6. Applications of Spark
7. Spark usecase
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Apache Spark is a lightning-fast cluster computing technology, designed for fast computation. It extends the MapReduce model of Hadoop to efficiently use it for more types of computations, which includes interactive queries and stream processing.
Spark is one of Hadoop's subproject developed in 2009 in UC Berkeley's AMPLab by Matei Zaharia. It was Open Sourced in 2010 under a BSD license. It was donated to Apache software foundation in 2013, and now Apache Spark has become a top-level Apache project from Feb-2014.
This document shares some basic knowledge about Apache Spark.
Apache Spark is an open-source distributed processing engine that is up to 100 times faster than Hadoop for processing data stored in memory and 10 times faster for data stored on disk. It provides high-level APIs in Java, Scala, Python and SQL and supports batch processing, streaming, and machine learning. Spark runs on Hadoop, Mesos, Kubernetes or standalone and can access diverse data sources using its core abstraction called resilient distributed datasets (RDDs).
This presentation on Spark Architecture will give an idea of what is Apache Spark, the essential features in Spark, the different Spark components. Here, you will learn about Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and Graphx. You will understand how Spark processes an application and runs it on a cluster with the help of its architecture. Finally, you will perform a demo on Apache Spark. So, let's get started with Apache Spark Architecture.
YouTube Video: https://www.youtube.com/watch?v=CF5Ewk0GxiQ
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Apache spark architecture (Big Data and Analytics)Jyotasana Bharti
A presentation/slides on Apache Spark Architecture with its features, architecture, working, etc.
Introduction
Features
Understanding Apache Spark Architecture
Working of Apache Spark Architecture
Applications
Conclusion
References
In this era of ever growing data, the need for analyzing it for meaningful business insights becomes more and more significant. There are different Big Data processing alternatives like Hadoop, Spark, Storm etc. Spark, however is unique in providing batch as well as streaming capabilities, thus making it a preferred choice for lightening fast Big Data Analysis platforms.
In this era of ever growing data, the need for analyzing it for meaningful business insights becomes more and more significant. There are different Big Data processing alternatives like Hadoop, Spark, Storm etc. Spark, however is unique in providing batch as well as streaming capabilities, thus making it a preferred choice for lightening fast Big Data Analysis platforms.
This slide introduces Hadoop Spark.
Just to help you construct an idea of Spark regarding its architecture, data flow, job scheduling, and programming.
Not all technical details are included.
This document provides an overview of Apache Spark, including its history, features, architecture and use cases. Spark started in 2009 at UC Berkeley and was later adopted by the Apache Foundation. It provides faster processing than Hadoop by keeping data in memory. Spark supports batch, streaming and interactive processing on large datasets using its core abstraction called resilient distributed datasets (RDDs).
A Master Guide To Apache Spark Application And Versatile Uses.pdfDataSpace Academy
A leading name in big data handling tasks, Apache Spark earns kudos for its ability to handle vast amounts of data swiftly and efficiently. The tool is also a major name in the development of APIs in Java, Python, and R. The blog offers a master guide on all the key aspects of Apache Spark, including versatility, fault tolerance, real-time streaming, and more. The blog also goes on to explain the operational procedure of the tool, step by step. Finally, the article wraps up with benefits and also limitations of the tool.
Streaming Big Data with Spark, Kafka, Cassandra, Akka & Scala (from webinar)Helena Edelson
This document provides an overview of streaming big data with Spark, Kafka, Cassandra, Akka, and Scala. It discusses delivering meaning in near-real time at high velocity and an overview of Spark Streaming, Kafka and Akka. It also covers Cassandra and the Spark Cassandra Connector as well as integration in big data applications. The presentation is given by Helena Edelson, a Spark Cassandra Connector committer and Akka contributor who is a Scala and big data conference speaker working as a senior software engineer at DataStax.
Demi Ben-Ari is a senior software engineer at Windward Ltd. who has a BS in computer science. They previously worked as a software team leader and senior Java engineer developing missile defense and alert systems. The presentation discusses Spark, an open-source cluster computing framework, and how Windward uses Spark for data filtering, management, predictions and more through Java applications running on YARN clusters.
Spark is an open-source cluster computing framework that can run analytics applications much faster than Hadoop by keeping data in memory rather than on disk. While Spark can access Hadoop's HDFS storage system and is often used as a replacement for Hadoop's MapReduce, Hadoop remains useful for batch processing and Spark is not expected to fully replace it. Spark provides speed, ease of use, and integration of SQL, streaming, and machine learning through its APIs in multiple languages.
Spark is a cluster computing framework designed to be fast, general-purpose, and able to handle a wide range of workloads including batch processing, iterative algorithms, interactive queries, and streaming. It is faster than Hadoop for interactive queries and complex applications by running computations in-memory when possible. Spark also simplifies combining different processing types through a single engine. It offers APIs in Java, Python, Scala and SQL and integrates closely with other big data tools like Hadoop. Spark is commonly used for interactive queries on large datasets, streaming data processing, and machine learning tasks.
The document provides an overview of Apache Spark, including what it is, its ecosystem, features, and architecture. Some key points:
- Apache Spark is an open-source cluster computing framework for large-scale data processing. It is up to 100x faster than Hadoop for iterative/interactive algorithms.
- Spark features include its RDD abstraction, lazy evaluation, and use of DAGs to optimize performance. It supports Scala, Java, Python, and R.
- The Spark ecosystem includes tools like Spark SQL, MLlib, GraphX, and Spark Streaming. It can run on Hadoop YARN, Mesos, or in standalone mode.
- Spark's architecture includes the SparkContext,
In this era of ever growing data, the need for analyzing it for meaningful business insights becomes more and more significant. There are different Big Data processing alternatives like Hadoop, Spark, Storm etc. Spark, however is unique in providing batch as well as streaming capabilities, thus making it a preferred choice for lightening fast Big Data Analysis platforms.
In this era of ever growing data, the need for analyzing it for meaningful business insights becomes more and more significant. There are different Big Data processing alternatives like Hadoop, Spark, Storm etc. Spark, however is unique in providing batch as well as streaming capabilities, thus making it a preferred choice for lightening fast Big Data Analysis platforms.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
2. What is Spark?
Spark is an in-memory cluster computing framework developed by UC,Berkeley
and presently maintained by Apache software Foundation.
Cluster Computing means computations on large datasets can be done in
parallel, distributed fashion over a cluster.
Spark requires a cluster manager(spark standalone,Mesos,Yarn etc.) and
distributed storage system(Hadoop,Hive,Cassandra etc.)
3. Hadoop to Spark
Spark is build on top of Hadoop which uses MapReduce API(developed by
Google) for doing parallel,distributed computations.
Hadoop is slow as all the operations are on the disk and also coding is not
developer friendly.
As Apache Spark is an in-memory processing engine it is 10-100X faster than
hadoop.
4. Why Spark is getting popular
No doubt about the spark performance but there are other factors also which
contributes to its success.
Delight for developers as it provides language flexibility and supports
Scala,Java,Python,R
Simple yet rich API makes it easy to use
Runs everywhere
5. Spark API
This picture is taken from apache spark site and shows different components
of Spark API.
All APIs are available in scala, java and python.
6. Spark Core
Spark core is the foundation of Spark project.
This API is centered on a data structure called resilient distributed
dataset(RDD), a read only multi set of data items distributed over a cluster of
machines, that is maintained in fault tolerant way.
It provides distributed task dispatching, scheduling and basic IO functionalities.
7. Spark core continued...
Creating RDDs in scala. SC is the spark context which comes initialized in spark
shell and needs to be initialized in user created applications.
1)Existing collections
2) External Datasets
8. Spark Core continued...
RDD supports two types of operations.
1) Transformations(map,filter,union etc.)
2) Actions(reduce,collect,count etc.)
9. Spark SQL
This API let you query structured data inside spark programs using either SQL
or familiar DataFrame API.
It provide access to variety of data sources including Hive, Avro, JSON, JDBC
etc.
10. Spark SQL continued..
SQLContext is the starting point to be used for Spark SQL
Creation of Dataframes
1)Using datasource
2) Using sql
13. Spark Streaming
Spark streaming is an extension of spark core API that enables scalable, high-
throughput, fault-tolerant stream processing of live data streams.
Data can be ingested from many sources like Kafka, Flume, Twitter, ZeroMQ
etc
14. Spark Streaming continued…
It provides a high-level abstraction called DStream or Discretized stream which
represent a continuous stream of data.
DStream is represented internally as a sequence of RDDs.
Creating Dstream
15. For any queries please email me.
email-id:= pythonpeer@gmail.com
Thanks for your Time.