This Edureka Spark Hadoop Tutorial will help you understand how to use Spark and Hadoop together. This Spark Hadoop tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
1) Spark Overview
2) Hadoop Overview
3) Spark vs Hadoop
4) Why Spark Hadoop?
5) Using Hadoop With Spark
6) Use Case - Sports Analytics (NBA)
Apache Spark Tutorial | Spark Tutorial for Beginners | Apache Spark Training ...Edureka!
This Edureka Spark Tutorial will help you to understand all the basics of Apache Spark. This Spark tutorial is ideal for both beginners as well as professionals who want to learn or brush up Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Introduction
2) Batch vs Real Time Analytics
3) Why Apache Spark?
4) What is Apache Spark?
5) Using Spark with Hadoop
6) Apache Spark Features
7) Apache Spark Ecosystem
8) Demo: Earthquake Detection Using Apache Spark
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
Spark SQL Tutorial | Spark Tutorial for Beginners | Apache Spark Training | E...Edureka!
This Edureka Spark SQL Tutorial will help you to understand how Apache Spark offers SQL power in real-time. This tutorial also demonstrates an use case on Stock Market Analysis using Spark SQL. Below are the topics covered in this tutorial:
1) Limitations of Apache Hive
2) Spark SQL Advantages Over Hive
3) Spark SQL Success Story
4) Spark SQL Features
5) Architecture of Spark SQL
6) Spark SQL Libraries
7) Querying Using Spark SQL
8) Demo: Stock Market Analysis With Spark SQL
Hyperspace is a recently open-sourced (https://github.com/microsoft/hyperspace) indexing sub-system from Microsoft. The key idea behind Hyperspace is simple: Users specify the indexes they want to build. Hyperspace builds these indexes using Apache Spark, and maintains metadata in its write-ahead log that is stored in the data lake. At runtime, Hyperspace automatically selects the best index to use for a given query without requiring users to rewrite their queries. Since Hyperspace was introduced, one of the most popular asks from the Spark community was indexing support for Delta Lake. In this talk, we present our experiences in designing and implementing Hyperspace support for Delta Lake and how it can be used for accelerating queries over Delta tables. We will cover the necessary foundations behind Delta Lake’s transaction log design and how Hyperspace enables indexing support that seamlessly works with the former’s time travel queries.
This presentation is an introduction to Apache Spark. It covers the basic API, some advanced features and describes how Spark physically executes its jobs.
This slide deck is used as an introduction to the internals of Apache Spark, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom.
Course website:
http://michiard.github.io/DISC-CLOUD-COURSE/
Sources available here:
https://github.com/michiard/DISC-CLOUD-COURSE
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.
Apache Spark Tutorial | Spark Tutorial for Beginners | Apache Spark Training ...Edureka!
This Edureka Spark Tutorial will help you to understand all the basics of Apache Spark. This Spark tutorial is ideal for both beginners as well as professionals who want to learn or brush up Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Introduction
2) Batch vs Real Time Analytics
3) Why Apache Spark?
4) What is Apache Spark?
5) Using Spark with Hadoop
6) Apache Spark Features
7) Apache Spark Ecosystem
8) Demo: Earthquake Detection Using Apache Spark
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
Spark SQL Tutorial | Spark Tutorial for Beginners | Apache Spark Training | E...Edureka!
This Edureka Spark SQL Tutorial will help you to understand how Apache Spark offers SQL power in real-time. This tutorial also demonstrates an use case on Stock Market Analysis using Spark SQL. Below are the topics covered in this tutorial:
1) Limitations of Apache Hive
2) Spark SQL Advantages Over Hive
3) Spark SQL Success Story
4) Spark SQL Features
5) Architecture of Spark SQL
6) Spark SQL Libraries
7) Querying Using Spark SQL
8) Demo: Stock Market Analysis With Spark SQL
Hyperspace is a recently open-sourced (https://github.com/microsoft/hyperspace) indexing sub-system from Microsoft. The key idea behind Hyperspace is simple: Users specify the indexes they want to build. Hyperspace builds these indexes using Apache Spark, and maintains metadata in its write-ahead log that is stored in the data lake. At runtime, Hyperspace automatically selects the best index to use for a given query without requiring users to rewrite their queries. Since Hyperspace was introduced, one of the most popular asks from the Spark community was indexing support for Delta Lake. In this talk, we present our experiences in designing and implementing Hyperspace support for Delta Lake and how it can be used for accelerating queries over Delta tables. We will cover the necessary foundations behind Delta Lake’s transaction log design and how Hyperspace enables indexing support that seamlessly works with the former’s time travel queries.
This presentation is an introduction to Apache Spark. It covers the basic API, some advanced features and describes how Spark physically executes its jobs.
This slide deck is used as an introduction to the internals of Apache Spark, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom.
Course website:
http://michiard.github.io/DISC-CLOUD-COURSE/
Sources available here:
https://github.com/michiard/DISC-CLOUD-COURSE
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.
In KDD2011, Vijay Narayanan (Yahoo!) and Milind Bhandarkar (Greenplum Labs, EMC) conducted a tutorial on "Modeling with Hadoop". This is the first half of the tutorial.
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.
Delta Lake, an open-source innovations which brings new capabilities for transactions, version control and indexing your data lakes. We uncover how Delta Lake benefits and why it matters to you. Through this session, we showcase some of its benefits and how they can improve your modern data engineering pipelines. Delta lake provides snapshot isolation which helps concurrent read/write operations and enables efficient insert, update, deletes, and rollback capabilities. It allows background file optimization through compaction and z-order partitioning achieving better performance improvements. In this presentation, we will learn the Delta Lake benefits and how it solves common data lake challenges, and most importantly new Delta Time Travel capability.
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
Every business today wants to leverage data to drive strategic initiatives with machine learning, data science and analytics — but runs into challenges from siloed teams, proprietary technologies and unreliable data.
That’s why enterprises are turning to the lakehouse because it offers a single platform to unify all your data, analytics and AI workloads.
Join our How to Build a Lakehouse technical training, where we’ll explore how to use Apache SparkTM, Delta Lake, and other open source technologies to build a better lakehouse. This virtual session will include concepts, architectures and demos.
Here’s what you’ll learn in this 2-hour session:
How Delta Lake combines the best of data warehouses and data lakes for improved data reliability, performance and security
How to use Apache Spark and Delta Lake to perform ETL processing, manage late-arriving data, and repair corrupted data directly on your lakehouse
Kafka for Real-Time Replication between Edge and Hybrid CloudKai Wähner
Not all workloads allow cloud computing. Low latency, cybersecurity, and cost-efficiency require a suitable combination of edge computing and cloud integration.
This session explores architectures and design patterns for software and hardware considerations to deploy hybrid data streaming with Apache Kafka anywhere. A live demo shows data synchronization from the edge to the public cloud across continents with Kafka on Hivecell and Confluent Cloud.
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
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.
Cruise Control: Effortless management of Kafka clustersPrateek Maheshwari
Kafka has become the de facto standard for streaming data with high-throughput, low-latency, and fault-tolerance. However, its rising adoption raises new challenges. In particular, the growing cluster sizes, increasing volume and diversity of user traffic, and aging network and server components induce an overhead in managing the system. This overhead makes it infeasible for human operators to constantly monitor, identify, and mitigate issues. The resulting utilization imbalance across brokers leads to unpredictable client performance due to the high variation in their throughput and latency. Finally, properly expanding, shrinking, or upgrading clusters also incurs a management overhead. Hence, adopting a principled approach to manage Kafka clusters is integral to the sustainability of the infrastructure.
This talk will describe how LinkedIn alleviates the management overhead of large-scale Kafka clusters using Cruise Control. To this end, first, we will discuss the reactive and proactive techniques that Cruise Control uses to support admin operations for cluster maintenance, enable anomaly detection with self-healing, and provide real-time monitoring for Kafka clusters. Next, we will examine how Cruise Control performs in production. Finally, we will conclude with questions and further discussion.
The Rise of ZStandard: Apache Spark/Parquet/ORC/AvroDatabricks
Zstandard is a fast compression algorithm which you can use in Apache Spark in various way. In this talk, I briefly summarized the evolution history of Apache Spark in this area and four main use cases and the benefits and the next steps:
1) ZStandard can optimize Spark local disk IO by compressing shuffle files significantly. This is very useful in K8s environments. It’s beneficial not only when you use `emptyDir` with `memory` medium, but also it maximizes OS cache benefit when you use shared SSDs or container local storage. In Spark 3.2, SPARK-34390 takes advantage of ZStandard buffer pool feature and its performance gain is impressive, too.
2) Event log compression is another area to save your storage cost on the cloud storage like S3 and to improve the usability. SPARK-34503 officially switched the default event log compression codec from LZ4 to Zstandard.
3) Zstandard data file compression can give you more benefits when you use ORC/Parquet files as your input and output. Apache ORC 1.6 supports Zstandardalready and Apache Spark enables it via SPARK-33978. The upcoming Parquet 1.12 will support Zstandard compression.
4) Last, but not least, since Apache Spark 3.0, Zstandard is used to serialize/deserialize MapStatus data instead of Gzip.
There are more community works to utilize Zstandard to improve Spark. For example, Apache Avro community also supports Zstandard and SPARK-34479 aims to support Zstandard in Spark’s avro file format in Spark 3.2.0.
Hive Tutorial | Hive Architecture | Hive Tutorial For Beginners | Hive In Had...Simplilearn
This presentation about Hive will help you understand the history of Hive, what is Hive, Hive architecture, data flow in Hive, Hive data modeling, Hive data types, different modes in which Hive can run on, differences between Hive and RDBMS, features of Hive and a demo on HiveQL commands. Hive is a data warehouse system which is used for querying and analyzing large datasets stored in HDFS. Hive uses a query language called HiveQL which is similar to SQL. Hive issues SQL abstraction to integrate SQL queries (like HiveQL) into Java without the necessity to implement queries in the low-level Java API. Now, let us get started and understand Hadoop Hive in detail
Below topics are explained in this Hive presetntation:
1. History of Hive
2. What is Hive?
3. Architecture of Hive
4. Data flow in Hive
5. Hive data modeling
6. Hive data types
7. Different modes of Hive
8. Difference between Hive and RDBMS
9. Features of Hive
10. Demo on HiveQL
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart 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?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Native Support of Prometheus Monitoring in Apache Spark 3.0Databricks
All production environment requires monitoring and alerting. Apache Spark also has a configurable metrics system in order to allow users to report Spark metrics to a variety of sinks. Prometheus is one of the popular open-source monitoring and alerting toolkits which is used with Apache Spark together.
What is Apache Spark | Apache Spark Tutorial For Beginners | Apache Spark Tra...Edureka!
This Edureka "What is Spark" tutorial will introduce you to big data analytics framework - Apache Spark. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Analytics
2) What is Apache Spark?
3) Why Apache Spark?
4) Using Spark with Hadoop
5) Apache Spark Features
6) Apache Spark Architecture
7) Apache Spark Ecosystem - Spark Core, Spark Streaming, Spark MLlib, Spark SQL, GraphX
8) Demo: Analyze Flight Data Using Apache Spark
In KDD2011, Vijay Narayanan (Yahoo!) and Milind Bhandarkar (Greenplum Labs, EMC) conducted a tutorial on "Modeling with Hadoop". This is the first half of the tutorial.
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.
Delta Lake, an open-source innovations which brings new capabilities for transactions, version control and indexing your data lakes. We uncover how Delta Lake benefits and why it matters to you. Through this session, we showcase some of its benefits and how they can improve your modern data engineering pipelines. Delta lake provides snapshot isolation which helps concurrent read/write operations and enables efficient insert, update, deletes, and rollback capabilities. It allows background file optimization through compaction and z-order partitioning achieving better performance improvements. In this presentation, we will learn the Delta Lake benefits and how it solves common data lake challenges, and most importantly new Delta Time Travel capability.
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
Every business today wants to leverage data to drive strategic initiatives with machine learning, data science and analytics — but runs into challenges from siloed teams, proprietary technologies and unreliable data.
That’s why enterprises are turning to the lakehouse because it offers a single platform to unify all your data, analytics and AI workloads.
Join our How to Build a Lakehouse technical training, where we’ll explore how to use Apache SparkTM, Delta Lake, and other open source technologies to build a better lakehouse. This virtual session will include concepts, architectures and demos.
Here’s what you’ll learn in this 2-hour session:
How Delta Lake combines the best of data warehouses and data lakes for improved data reliability, performance and security
How to use Apache Spark and Delta Lake to perform ETL processing, manage late-arriving data, and repair corrupted data directly on your lakehouse
Kafka for Real-Time Replication between Edge and Hybrid CloudKai Wähner
Not all workloads allow cloud computing. Low latency, cybersecurity, and cost-efficiency require a suitable combination of edge computing and cloud integration.
This session explores architectures and design patterns for software and hardware considerations to deploy hybrid data streaming with Apache Kafka anywhere. A live demo shows data synchronization from the edge to the public cloud across continents with Kafka on Hivecell and Confluent Cloud.
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
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.
Cruise Control: Effortless management of Kafka clustersPrateek Maheshwari
Kafka has become the de facto standard for streaming data with high-throughput, low-latency, and fault-tolerance. However, its rising adoption raises new challenges. In particular, the growing cluster sizes, increasing volume and diversity of user traffic, and aging network and server components induce an overhead in managing the system. This overhead makes it infeasible for human operators to constantly monitor, identify, and mitigate issues. The resulting utilization imbalance across brokers leads to unpredictable client performance due to the high variation in their throughput and latency. Finally, properly expanding, shrinking, or upgrading clusters also incurs a management overhead. Hence, adopting a principled approach to manage Kafka clusters is integral to the sustainability of the infrastructure.
This talk will describe how LinkedIn alleviates the management overhead of large-scale Kafka clusters using Cruise Control. To this end, first, we will discuss the reactive and proactive techniques that Cruise Control uses to support admin operations for cluster maintenance, enable anomaly detection with self-healing, and provide real-time monitoring for Kafka clusters. Next, we will examine how Cruise Control performs in production. Finally, we will conclude with questions and further discussion.
The Rise of ZStandard: Apache Spark/Parquet/ORC/AvroDatabricks
Zstandard is a fast compression algorithm which you can use in Apache Spark in various way. In this talk, I briefly summarized the evolution history of Apache Spark in this area and four main use cases and the benefits and the next steps:
1) ZStandard can optimize Spark local disk IO by compressing shuffle files significantly. This is very useful in K8s environments. It’s beneficial not only when you use `emptyDir` with `memory` medium, but also it maximizes OS cache benefit when you use shared SSDs or container local storage. In Spark 3.2, SPARK-34390 takes advantage of ZStandard buffer pool feature and its performance gain is impressive, too.
2) Event log compression is another area to save your storage cost on the cloud storage like S3 and to improve the usability. SPARK-34503 officially switched the default event log compression codec from LZ4 to Zstandard.
3) Zstandard data file compression can give you more benefits when you use ORC/Parquet files as your input and output. Apache ORC 1.6 supports Zstandardalready and Apache Spark enables it via SPARK-33978. The upcoming Parquet 1.12 will support Zstandard compression.
4) Last, but not least, since Apache Spark 3.0, Zstandard is used to serialize/deserialize MapStatus data instead of Gzip.
There are more community works to utilize Zstandard to improve Spark. For example, Apache Avro community also supports Zstandard and SPARK-34479 aims to support Zstandard in Spark’s avro file format in Spark 3.2.0.
Hive Tutorial | Hive Architecture | Hive Tutorial For Beginners | Hive In Had...Simplilearn
This presentation about Hive will help you understand the history of Hive, what is Hive, Hive architecture, data flow in Hive, Hive data modeling, Hive data types, different modes in which Hive can run on, differences between Hive and RDBMS, features of Hive and a demo on HiveQL commands. Hive is a data warehouse system which is used for querying and analyzing large datasets stored in HDFS. Hive uses a query language called HiveQL which is similar to SQL. Hive issues SQL abstraction to integrate SQL queries (like HiveQL) into Java without the necessity to implement queries in the low-level Java API. Now, let us get started and understand Hadoop Hive in detail
Below topics are explained in this Hive presetntation:
1. History of Hive
2. What is Hive?
3. Architecture of Hive
4. Data flow in Hive
5. Hive data modeling
6. Hive data types
7. Different modes of Hive
8. Difference between Hive and RDBMS
9. Features of Hive
10. Demo on HiveQL
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart 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?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Native Support of Prometheus Monitoring in Apache Spark 3.0Databricks
All production environment requires monitoring and alerting. Apache Spark also has a configurable metrics system in order to allow users to report Spark metrics to a variety of sinks. Prometheus is one of the popular open-source monitoring and alerting toolkits which is used with Apache Spark together.
What is Apache Spark | Apache Spark Tutorial For Beginners | Apache Spark Tra...Edureka!
This Edureka "What is Spark" tutorial will introduce you to big data analytics framework - Apache Spark. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Analytics
2) What is Apache Spark?
3) Why Apache Spark?
4) Using Spark with Hadoop
5) Apache Spark Features
6) Apache Spark Architecture
7) Apache Spark Ecosystem - Spark Core, Spark Streaming, Spark MLlib, Spark SQL, GraphX
8) Demo: Analyze Flight Data Using Apache Spark
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 Edureka Apache Spark Interview Questions and Answers tutorial helps you in understanding how to tackle questions in a Spark interview and also gives you an idea of the questions that can be asked in a Spark Interview. The Spark interview questions cover a wide range of questions from various Spark components. Below are the topics covered in this tutorial:
1. Basic Questions
2. Spark Core Questions
3. Spark Streaming Questions
4. Spark GraphX Questions
5. Spark MLlib Questions
6. Spark SQL Questions
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.
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.
What to learn during the 21 days Lockdown | EdurekaEdureka!
Register Here: https://resources.edureka.co/21-days-learning-plan-webinar/
In light of the complete national lockdown for 21 days, we invite you to join a FREE webinar by renowned Mentor and Advisor, Nitin Gupta as he helps you create a 21-day learning gameplan to maximize returns for your career.
The webinar will help freshers and experienced professionals to capitalize on these 21 days and figure out the best technologies to learn while confined to home.
You will also get all your questions and doubts resolved in real-time.
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Top 10 Dying Programming Languages in 2020 | EdurekaEdureka!
YouTube Link: https://youtu.be/LSM7hD6GM4M
Get Edureka Certified in Trending Programming Languages: https://www.edureka.co
In this highly competitive IT industry, everyone wants to learn programming languages that will keep them ahead of the game. But knowing what to learn so you gain the most out of your knowledge is a whole other ball game. So, we at Edureka have prepared a list of Top 10 Dying Programming Languages 2020 that will help you to make the right choice for your career. Meanwhile, if you ever wondered about which languages are slated for continuing uptake and possible greatness, we have a list for that, too.
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Top 5 Trending Business Intelligence Tools | EdurekaEdureka!
YouTube Link: https://youtu.be/eEwq_mPd1iI
Edureka BI Certification Training Courses: https://www.edureka.co/bi-and-visualization-certification-courses
Receiving insights and finding trends is absolutely critical for businesses to scale and adapt as the years go on. This is exactly what business intelligence does and the best thing about these software solutions is that their potential uses are practically unlimited.
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Tableau Tutorial for Data Science | EdurekaEdureka!
YouTube Link:https://youtu.be/ZHNdSKMluI0
Edureka Tableau Certification Training: https://www.edureka.co/tableau-certification-training
This Edureka's PPT on "Tableau for Data Science" will help you to utilize Tableau as a tool for Data Science, not only for engagement but also comprehension efficiency. Through this PPT, you will learn to gain the maximum amount of insight with the least amount of effort.
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Edureka Python Certification Training: https://www.edureka.co/data-science-python-certification-course
This Edureka PPT on 'Python Programming' will help you learn Python programming basics with the help of interesting hands-on implementations.
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Get Edureka Certified in Trending Project Management Certifications: https://www.edureka.co/project-management-and-methodologies-certification-courses
Whether you want to scale up your career or are trying to switch your career path, Project Management Certifications seems to be a perfect choice in either case. So, we at Edureka have prepared a list of Top 5 Project Management Certifications that you must check out in 2020 for a major career boost.
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Top Maven Interview Questions in 2020 | EdurekaEdureka!
YouTube Link: https://youtu.be/5iTcAR4fScM
**DevOps Certification Courses - https://www.edureka.co/devops-certification-training***
This video on 'Maven Interview Questions' discusses the most frequently asked Maven Interview Questions. This PPT will help give you a detailed explanation of the topics which will help you in acing the interviews.
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** Linux Administration Certification Training - https://www.edureka.co/linux-admin **
Linux Mint is the first operating system that people from Windows or Mac are drawn towards when they have to switch to Linux in their work environment. Linux Mint has been around since the year 2006 and has grown and matured into a very user-friendly OS. Do watch the PPT till the very end to see all the demonstrations.
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How to Deploy Java Web App in AWS| EdurekaEdureka!
YouTube Link:https://youtu.be/Ozc5Yu_IcaI
** Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training**
This Edureka PPT shows how to deploy a java web application in AWS using AWS Elastic Beanstalk. It also describes the advantages of using AWS for this purpose.
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*** Edureka Digital Marketing Course: https://www.edureka.co/post-graduate/digital-marketing-certification***
This Edureka PPT on "Top 10 Reasons to Learn Digital Marketing" will help you understand why you should take up Digital Marketing
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** RPA Training: https://www.edureka.co/robotic-process-automation-training**
This PPT on RPA in 2020 will provide a glimpse of the accomplishments and benefits provided by RPA. Also, it will list out the new changes and technologies that will collaborate with RPA in 2020.
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**DevOps Certification Courses - https://www.edureka.co/devops-certification-training **
This PPT shows how to configure Jenkins to receive email notifications. It also includes a demo that shows how to do it in 6 simple steps in the Windows machine.
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EA Algorithm in Machine Learning | EdurekaEdureka!
YouTube Link: https://youtu.be/DIADjJXrgps
** Machine Learning Certification Training: https://www.edureka.co/machine-learning-certification-training **
This Edureka PPT on 'EM Algorithm In Machine Learning' covers the EM algorithm along with the problem of latent variables in maximum likelihood and Gaussian mixture model.
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PGP in AI and Machine Learning (9 Months Online Program): https://www.edureka.co/post-graduate/machine-learning-and-ai
This Edureka PPT on "Cognitive AI" explains cognitive computing and how it helps in making better human decisions at work. Also, it explains the differences between cognitive computing and artificial intelligence.
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Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training
This Edureka PPT on AWS Cloud Practitioner will provide a complete guide to your AWS Cloud Practitioner Certification exam. It will explain the exam details, objectives, why you should get certified and also how AWS certification will help your career.
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Blue Prism Top Interview Questions | EdurekaEdureka!
YouTube Link: https://youtu.be/ykbRdUNIbyQ
** RPA Training: https://www.edureka.co/robotic-process-automation-certification-courses**
This PPT on Blue Prism Interview Questions will cover the Top 50 Blue Prism related questions asked in your interviews.
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AWS Architect Certification Training: https://www.edureka.co/aws-certification-training
This PPT will help you in understanding how AWS deals smartly with Big Data. It also shows how AWS can solve Big Data challenges with ease.
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A star algorithm | A* Algorithm in Artificial Intelligence | EdurekaEdureka!
YouTube Link: https://youtu.be/amlkE0g-YFU
** Artificial Intelligence and Deep Learning: https://www.edureka.co/ai-deep-learni... **
This Edureka PPT on 'A Star Algorithm' teaches you all about the A star Algorithm, the uses, advantages and disadvantages and much more. It also shows you how the algorithm can be implemented practically and has a comparison between the Dijkstra and itself.
Check out our playlist for more videos: http://bit.ly/2taym8X
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Kubernetes Installation on Ubuntu | EdurekaEdureka!
YouTube Link: https://youtu.be/UWg3ORRRF60
Kubernetes Certification: https://www.edureka.co/kubernetes-certification
This Edureka PPT will help you set up a Kubernetes cluster having 1 master and 1 node. The detailed step by step instructions is demonstrated in this PPT.
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DevOps Training: https://www.edureka.co/devops-certification-training
This Edureka DevOps Tutorial for Beginners talks about What is DevOps and how it works. You will learn about several DevOps tools (Git, Jenkins, Docker, Puppet, Ansible, Nagios) involved at different DevOps stages such as version control, continuous integration, continuous delivery, continuous deployment, continuous monitoring.
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Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
4. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
What is Spark?
Apache Spark is an open-source cluster-computing framework for real
time processing developed by the Apache Software Foundation
Spark provides an interface for programming entire clusters with implicit
data parallelism and fault-tolerance
It was built on top of Hadoop MapReduce and it extends the
MapReduce model to efficiently use more types of computations
Reduction
in time
Parallel
Serial
Figure: Data Parallelism In Spark
Figure: Real Time Processing In Spark
5. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Spark Overview
Polyglot: Can be programmed in Scala,
Java, Python and R
Spark is used in real-time
processing
Lazy Evaluation: Delays evaluation till needed
Real time computation & low latency because of
in-memory computation
6. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Spark Ecosystem
Used for structured
data. Can run
unmodified hive
queries on existing
Hadoop deployment
Spark Core Engine
Spark SQL
(SQL)
Spark
Streaming
(Streaming)
MLlib
(Machine
Learning)
GraphX
(Graph
Computation)
SparkR
(R on Spark)
Enables analytical
and interactive
apps for live
streaming data.
Package for R language to
enable R-users to leverage
Spark power from R shell
Machine learning
libraries being built
on top of Spark.
The core engine for entire Spark framework. Provides
utilities and architecture for other components
Graph Computation
engine (Similar to
Giraph). Combines data-
parallel and graph-
parallel concepts
7. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Spark Features
Deployment
Powerful
Caching
Polyglot
Features
100x faster than
for large scale data
processing
Simple programming
layer provides powerful
caching and disk
persistence capabilities
Can be deployed through
Mesos, Hadoop via Yarn, or
Spark’s own cluster manger
Can be programmed
in Scala, Java,
Python and R
Speed
vs
8. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Spark Use Cases
Twitter Sentiment Analysis
With Spark
Trending Topics can
be used to create
campaigns and attract
larger audience
Sentiment helps in
crisis management,
service adjusting and
target marketing
NYSE: Real Time Analysis
of Stock Market Data
Banking: Credit Card
Fraud Detection
Genomic Sequencing
10. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
What is Hadoop?
Hadoop Cluster
Master
Slaves
Hadoop is a framework that allows us to store and process large
data sets in parallel and distributed fashion
HDFS
(Storage)
MapReduce
(Processing)
Allows parallel processing
of the data stored in HDFS
Allows to dump any kind of data
across the cluster
12. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Hadoop Features
Economical
Scalability
Reliability
Features
Flexible with
all kinds of data
In-built capability of
integrating seamlessly
with cloud based services
Usage of commodity
hardware minimizes
the cost of ownership
Hadoop infrastructure
has in-built fault
tolerance features
Flexibility
15. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Spark vs Hadoop
Use Cases For Real Time Analytics
Banking Government Healthcare Telecommunications Stock Market
Process data in real-time
Easy to use
Faster processing
Our Requirements:
Handle input from multiple sources
16. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Spark vs Hadoop
Spark runs upto 100x times faster than
Hadoop.
The in-memory processing in Spark is
what makes it faster than MapReduce.
Spark is not considered as a replacement
but as an extension to Hadoop.
0
20
40
60
80
100
120
140
160
180
Page Rank Performance
Iteration
Time (s)
Hadoop
Basic Spark
Spark + Controlled
Partitioning
The best case as per our chart is when Spark is used alongside
Hadoop. Let us dive in and use Hadoop with Spark.
18. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Why Spark Hadoop?
Using Spark and Hadoop together helps us to leverage Spark’s processing to utilize the
best of Hadoop’s HDFS and YARN.
Spark
StreamingCSV
Sequence
File
Avro
Parquet
HDFS Spark YARN
MapReduce
Storage Sources Input Data
Resource
Allocation
Optional
Processing
Input Data
Output Data
19. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Using Hadoop with Spark
&
Spark can be used along
with MapReduce in the
same Hadoop cluster or
separately as a processing
framework
Spark applications can also
be run on YARN (Hadoop
NextGen)
MapReduce and Spark are used
together where MapReduce is
used for batch processing and
Spark for real-time processing
Spark can run on top
of HDFS to leverage
the distributed
replicated storage
20. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
YARN Deployment With Spark
Figure: Cluster Deployment Mode Figure: Client Deployment Mode
In YARN-Cluster mode, the Spark driver runs inside an
application master process which is managed by YARN
The client can go away after initiating the application
In YARN-Client mode, the Spark driver runs in the client process
The application master is only used for requesting resources from
YARN.
YARN Cluster Mode YARN Client Mode
22. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Use Case
Kevin Durant, NBA MVP 2014Stephen Curry, NBA MVP 2015 & 2016 Joe Hassett, Highest 3 Pt Normalized LeBron James, NBA MVP ‘10, ’12 & ‘13
Problem Statement
To build a Sport Analysis system using Spark Hadoop for predicting game results and
player rankings for sports like Basketball, Football, Cricket, Soccer, etc.
We will demonstrate the same using Basketball for our use case.
23. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Use Case – Flow Diagram
Huge amount of
Sports data
1
Data Stored
in HDFS
2
Using Spark
Processing for
Analysis
3
Calculate Top
Scorers Per Season
Predict the NBA Most
Valuable Player (MVP)
Compare Teams to
Predict Winners
4
4
Query 3
Query 1
Query 2
4
5
27. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Use Case – Reading Data From HDFS
//Creating an object basketball containing our main() class
object basketball {
def main(args: Array[String]) {
val sparkConf = new SparkConf().setAppName("basketball").setMaster("local[2]")
val sc = new SparkContext(sparkConf)
for (i <- 1980 to 2016) {
println(i)
val yearStats =
sc.textFile(s"hdfs://localhost:9000/basketball/BasketballStats/leagues_NBA_$i*")
yearStats.filter(x => x.contains(",")).map(x =>
(i,x)).saveAsTextFile(s"hdfs://localhost:9000/basketball/BasketballStatsWithYear/
$i/")
}
28. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Use Case – Parsing Data And Broadcasting
//Read in all the statistics
val
stats=sc.textFile("hdfs://localhost:9000/basketball/BasketballStatsWithYear4/*/*")
.repartition(sc.defaultParallelism)
//Filter out the junk rows and clean up data for errors
val filteredStats=stats.filter(line => !line.contains("FG%")).filter(line =>
line.contains(",")).map(line => line.replace("*","").replace(",,",",0,"))
filteredStats.cache()
//Parse statistics and save as Map
val txtStat =
Array("FG","FGA","FG%","3P","3PA","3P%","2P","2PA","2P%","eFG%","FT","FTA","FT%","
ORB","DRB","TRB","AST","STL","BLK","TOV","PF","PTS")
val aggStats = processStats(filteredStats,txtStat).collectAsMap
//Collect RDD into map and broadcast it into 'broadcastStats'
val broadcastStats = sc.broadcast(aggStats)
29. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Use Case – Player Statistics Transformations
//Parse stats and normalize
val nStats =
filteredStats.map(x=>bbParse(x,broadcastStats.value,zBroadcastStats.value))
//Parse stats and track weights
val txtStatZ = Array("FG","FT","3P","TRB","AST","STL","BLK","TOV","PTS")
val zStats =
processStats(filteredStats,txtStatZ,broadcastStats.value).collectAsMap
//Collect RDD into Map and broadcast into 'zBroadcastStats'
val zBroadcastStats = sc.broadcast(zStats)
//Map RDD to RDD[Row] so that we can turn it into a DataFrame
val nPlayer = nStats.map(x =>
Row.fromSeq(Array(x.name,x.year,x.age,x.position,x.team,x.gp,x.gs,x.mp) ++ x.stats
++ x.statsZ ++ Array(x.valueZ) ++ x.statsN ++ Array(x.valueN)))
31. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Use Case – Getting All Player Statistics
//create schema for the data frame
val schemaN = StructType(
StructField("name", StringType, true) ::
StructField("year", IntegerType, true) ::
...
StructField("nTOT", DoubleType, true) :: Nil )
//Create DataFrame 'dfPlayersT' and register as 'tPlayers'
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
val dfPlayersT = sqlContext.createDataFrame(nPlayer,schemaN)
dfPlayersT.registerTempTable("tPlayers")
//Create DataFrame 'dfPlayers' and register as 'Players'
val dfPlayers = sqlContext.sql("select age-min_age as exp,tPlayers.* from tPlayers
join (select name,min(age)as min_age from tPlayers group by name) as t1 on
tPlayers.name=t1.name order by tPlayers.name, exp ")
dfPlayers.registerTempTable("Players")
32. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Use Case – Storing Best Players Into HDFS
//Calculate the best players of 2016
val mvp = sqlContext.sql("Select name, zTot from Players where year=2016
order by zTot desc").cache
mvp.show
//Storing the best players of 2016 into HDFS
mvp.write.format("csv").save("hdfs://localhost:9000/basketball/output.csv")
//Listing the full numbers of LeBron James
sqlContext.sql("Select * from Players where year=2016 and name='LeBron
James'").collect.foreach(println)
//Ranking the top 10 players on the average 3 pointers scored per game in 2016
sqlContext.sql("select name, 3p, z3p from Players where year=2016 order by
z3p desc").take(10).foreach(println)
35. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Use Case – Highest 3 Point Shooters
//All time 3 point shooting ranking
sqlContext.sql("select name, 3p, z3p from Players order by 3p
desc").take(10).foreach(println)
//All time 3 point shooting ranking normalized to their leagues
sqlContext.sql("select name, 3p, z3p from Players order by z3p
desc").take(10).foreach(println)
//Calculate the average number of 3 pointers per game in 2016
broadcastStats.value("2016_3P_avg")
//Calculate the average number of 3 pointers per game in 1981
broadcastStats.value("1981_3P_avg")
38. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Use Case – Who Will Be The 2016 NBA MVP?
LeBron James Stephen CurryJames Harden Russell WestbrookKobe BryantDwayne Wade
sqlContext.sql("select name, zTot from Players where year=2016
order by zTot desc").take(10).foreach(println)
43. www.edureka.co/apache-spark-scala-trainingEDUREKA SPARK CERTIFICATION TRAINING
Conclusion
Congrats!
We have hence demonstrated the power of Spark Hadoop in Prediction Analytics.
The hands-on examples will give you the required confidence to work on any future
projects you encounter in Apache Spark and Hadoop.