This presentation about Spark SQL will help you understand what is Spark SQL, Spark SQL features, architecture, data frame API, data source API, catalyst optimizer, running SQL queries and a demo on Spark SQL. Spark SQL is an Apache Spark's module for working with structured and semi-structured data. It is originated to overcome the limitations of Apache Hive. Now, let us get started and understand Spark SQL in detail.
Below topics are explained in this Spark SQL presentation:
1. What is Spark SQL?
2. Spark SQL features
3. Spark SQL architecture
4. Spark SQL - Dataframe API
5. Spark SQL - Data source API
6. Spark SQL - Catalyst optimizer
7. Running SQL queries
8. Spark SQL demo
This Apache Spark and Scala certification training is designed to advance your expertise working with the Big Data Hadoop Ecosystem. You will master essential skills of the Apache Spark open source framework and the Scala programming language, including Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark. This Scala Certification course will give you vital skillsets and a competitive advantage for an exciting career as a Hadoop Developer.
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
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Pyspark Tutorial | Introduction to Apache Spark with Python | PySpark Trainin...Edureka!
** PySpark Certification Training: https://www.edureka.co/pyspark-certification-training**
This Edureka tutorial on PySpark Tutorial will provide you with a detailed and comprehensive knowledge of Pyspark, how it works, the reason why python works best with Apache Spark. You will also learn about RDDs, data frames and mllib.
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
Slides for Data Syndrome one hour course on PySpark. Introduces basic operations, Spark SQL, Spark MLlib and exploratory data analysis with PySpark. Shows how to use pylab with Spark to create histograms.
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...Databricks
Spark SQL is a highly scalable and efficient relational processing engine with ease-to-use APIs and mid-query fault tolerance. It is a core module of Apache Spark. Spark SQL can process, integrate and analyze the data from diverse data sources (e.g., Hive, Cassandra, Kafka and Oracle) and file formats (e.g., Parquet, ORC, CSV, and JSON). This talk will dive into the technical details of SparkSQL spanning the entire lifecycle of a query execution. The audience will get a deeper understanding of Spark SQL and understand how to tune Spark SQL performance.
Pyspark Tutorial | Introduction to Apache Spark with Python | PySpark Trainin...Edureka!
** PySpark Certification Training: https://www.edureka.co/pyspark-certification-training**
This Edureka tutorial on PySpark Tutorial will provide you with a detailed and comprehensive knowledge of Pyspark, how it works, the reason why python works best with Apache Spark. You will also learn about RDDs, data frames and mllib.
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
Slides for Data Syndrome one hour course on PySpark. Introduces basic operations, Spark SQL, Spark MLlib and exploratory data analysis with PySpark. Shows how to use pylab with Spark to create histograms.
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...Databricks
Spark SQL is a highly scalable and efficient relational processing engine with ease-to-use APIs and mid-query fault tolerance. It is a core module of Apache Spark. Spark SQL can process, integrate and analyze the data from diverse data sources (e.g., Hive, Cassandra, Kafka and Oracle) and file formats (e.g., Parquet, ORC, CSV, and JSON). This talk will dive into the technical details of SparkSQL spanning the entire lifecycle of a query execution. The audience will get a deeper understanding of Spark SQL and understand how to tune Spark SQL performance.
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
Making Apache Spark Better with Delta LakeDatabricks
Delta Lake is an open-source storage layer that brings reliability to data lakes. Delta Lake offers ACID transactions, scalable metadata handling, and unifies the streaming and batch data processing. It runs on top of your existing data lake and is fully compatible with Apache Spark APIs.
In this talk, we will cover:
* What data quality problems Delta helps address
* How to convert your existing application to Delta Lake
* How the Delta Lake transaction protocol works internally
* The Delta Lake roadmap for the next few releases
* How to get involved!
Apache Spark Data Source V2 with Wenchen Fan and Gengliang WangDatabricks
As a general computing engine, Spark can process data from various data management/storage systems, including HDFS, Hive, Cassandra and Kafka. For flexibility and high throughput, Spark defines the Data Source API, which is an abstraction of the storage layer. The Data Source API has two requirements.
1) Generality: support reading/writing most data management/storage systems.
2) Flexibility: customize and optimize the read and write paths for different systems based on their capabilities.
Data Source API V2 is one of the most important features coming with Spark 2.3. This talk will dive into the design and implementation of Data Source API V2, with comparison to the Data Source API V1. We also demonstrate how to implement a file-based data source using the Data Source API V2 for showing its generality and flexibility.
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
"The common use cases of Spark SQL include ad hoc analysis, logical warehouse, query federation, and ETL processing. Spark SQL also powers the other Spark libraries, including structured streaming for stream processing, MLlib for machine learning, and GraphFrame for graph-parallel computation. For boosting the speed of your Spark applications, you can perform the optimization efforts on the queries prior employing to the production systems. Spark query plans and Spark UIs provide you insight on the performance of your queries. This talk discloses how to read and tune the query plans for enhanced performance. It will also cover the major related features in the recent and upcoming releases of Apache Spark.
"
In Spark SQL the physical plan provides the fundamental information about the execution of the query. The objective of this talk is to convey understanding and familiarity of query plans in Spark SQL, and use that knowledge to achieve better performance of Apache Spark queries. We will walk you through the most common operators you might find in the query plan and explain some relevant information that can be useful in order to understand some details about the execution. If you understand the query plan, you can look for the weak spot and try to rewrite the query to achieve a more optimal plan that leads to more efficient execution.
The main content of this talk is based on Spark source code but it will reflect some real-life queries that we run while processing data. We will show some examples of query plans and explain how to interpret them and what information can be taken from them. We will also describe what is happening under the hood when the plan is generated focusing mainly on the phase of physical planning. In general, in this talk we want to share what we have learned from both Spark source code and real-life queries that we run in our daily data processing.
*** Apache Spark and Scala Certification Training: https://www.edureka.co/apache-spark-scala-training ***
This Edureka PPT on "RDD Using Spark" will provide you the detailed and comprehensive knowledge about RDD, which are considered to be the backbone of Apache Spark. You will learn about the various Transformations and Actions that can be performed on RDDs. This PPT will cover the following topics:
Need for RDDs
What are RDDs?
Features of RDDs
Creation of RDDs using Spark
Operations performed on RDDs
RDDs using Spark: Pokemon Use Case
Blog Series: http://bit.ly/2VRogGx
Complete Apache Spark and Scala playlist: http://bit.ly/2In8IXD
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Video of the presentation can be seen here: https://www.youtube.com/watch?v=uxuLRiNoDio
The Data Source API in Spark is a convenient feature that enables developers to write libraries to connect to data stored in various sources with Spark. Equipped with the Data Source API, users can load/save data from/to different data formats and systems with minimal setup and configuration. In this talk, we introduce the Data Source API and the unified load/save functions built on top of it. Then, we show examples to demonstrate how to build a data source library.
Tech talk on what Azure Databricks is, why you should learn it and how to get started. We'll use PySpark and talk about some real live examples from the trenches, including the pitfalls of leaving your clusters running accidentally and receiving a huge bill ;)
After this you will hopefully switch to Spark-as-a-service and get rid of your HDInsight/Hadoop clusters.
This is part 1 of an 8 part Data Science for Dummies series:
Databricks for dummies
Titanic survival prediction with Databricks + Python + Spark ML
Titanic with Azure Machine Learning Studio
Titanic with Databricks + Azure Machine Learning Service
Titanic with Databricks + MLS + AutoML
Titanic with Databricks + MLFlow
Titanic with DataRobot
Deployment, DevOps/MLops and Operationalization
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
Large Scale Lakehouse Implementation Using Structured StreamingDatabricks
Business leads, executives, analysts, and data scientists rely on up-to-date information to make business decision, adjust to the market, meet needs of their customers or run effective supply chain operations.
Come hear how Asurion used Delta, Structured Streaming, AutoLoader and SQL Analytics to improve production data latency from day-minus-one to near real time Asurion’s technical team will share battle tested tips and tricks you only get with certain scale. Asurion data lake executes 4000+ streaming jobs and hosts over 4000 tables in production Data Lake on AWS.
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.
Presto: Fast SQL-on-Anything (including Delta Lake, Snowflake, Elasticsearch ...Databricks
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Comcast, GrubHub, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
Building a SIMD Supported Vectorized Native Engine for Spark SQLDatabricks
Spark SQL works very well with structured row-based data. Vectorized reader and writer for parquet/orc can make I/O much faster. It also used WholeStageCodeGen to improve the performance by Java JIT code. However Java JIT is usually not working very well on utilizing latest SIMD instructions under complicated queries. Apache Arrow provides columnar in-memory layout and SIMD optimized kernels as well as a LLVM based SQL engine Gandiva. These native based libraries can accelerate Spark SQL by reduce the CPU usage for both I/O and execution.
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
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
Making Apache Spark Better with Delta LakeDatabricks
Delta Lake is an open-source storage layer that brings reliability to data lakes. Delta Lake offers ACID transactions, scalable metadata handling, and unifies the streaming and batch data processing. It runs on top of your existing data lake and is fully compatible with Apache Spark APIs.
In this talk, we will cover:
* What data quality problems Delta helps address
* How to convert your existing application to Delta Lake
* How the Delta Lake transaction protocol works internally
* The Delta Lake roadmap for the next few releases
* How to get involved!
Apache Spark Data Source V2 with Wenchen Fan and Gengliang WangDatabricks
As a general computing engine, Spark can process data from various data management/storage systems, including HDFS, Hive, Cassandra and Kafka. For flexibility and high throughput, Spark defines the Data Source API, which is an abstraction of the storage layer. The Data Source API has two requirements.
1) Generality: support reading/writing most data management/storage systems.
2) Flexibility: customize and optimize the read and write paths for different systems based on their capabilities.
Data Source API V2 is one of the most important features coming with Spark 2.3. This talk will dive into the design and implementation of Data Source API V2, with comparison to the Data Source API V1. We also demonstrate how to implement a file-based data source using the Data Source API V2 for showing its generality and flexibility.
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
"The common use cases of Spark SQL include ad hoc analysis, logical warehouse, query federation, and ETL processing. Spark SQL also powers the other Spark libraries, including structured streaming for stream processing, MLlib for machine learning, and GraphFrame for graph-parallel computation. For boosting the speed of your Spark applications, you can perform the optimization efforts on the queries prior employing to the production systems. Spark query plans and Spark UIs provide you insight on the performance of your queries. This talk discloses how to read and tune the query plans for enhanced performance. It will also cover the major related features in the recent and upcoming releases of Apache Spark.
"
In Spark SQL the physical plan provides the fundamental information about the execution of the query. The objective of this talk is to convey understanding and familiarity of query plans in Spark SQL, and use that knowledge to achieve better performance of Apache Spark queries. We will walk you through the most common operators you might find in the query plan and explain some relevant information that can be useful in order to understand some details about the execution. If you understand the query plan, you can look for the weak spot and try to rewrite the query to achieve a more optimal plan that leads to more efficient execution.
The main content of this talk is based on Spark source code but it will reflect some real-life queries that we run while processing data. We will show some examples of query plans and explain how to interpret them and what information can be taken from them. We will also describe what is happening under the hood when the plan is generated focusing mainly on the phase of physical planning. In general, in this talk we want to share what we have learned from both Spark source code and real-life queries that we run in our daily data processing.
*** Apache Spark and Scala Certification Training: https://www.edureka.co/apache-spark-scala-training ***
This Edureka PPT on "RDD Using Spark" will provide you the detailed and comprehensive knowledge about RDD, which are considered to be the backbone of Apache Spark. You will learn about the various Transformations and Actions that can be performed on RDDs. This PPT will cover the following topics:
Need for RDDs
What are RDDs?
Features of RDDs
Creation of RDDs using Spark
Operations performed on RDDs
RDDs using Spark: Pokemon Use Case
Blog Series: http://bit.ly/2VRogGx
Complete Apache Spark and Scala playlist: http://bit.ly/2In8IXD
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Video of the presentation can be seen here: https://www.youtube.com/watch?v=uxuLRiNoDio
The Data Source API in Spark is a convenient feature that enables developers to write libraries to connect to data stored in various sources with Spark. Equipped with the Data Source API, users can load/save data from/to different data formats and systems with minimal setup and configuration. In this talk, we introduce the Data Source API and the unified load/save functions built on top of it. Then, we show examples to demonstrate how to build a data source library.
Tech talk on what Azure Databricks is, why you should learn it and how to get started. We'll use PySpark and talk about some real live examples from the trenches, including the pitfalls of leaving your clusters running accidentally and receiving a huge bill ;)
After this you will hopefully switch to Spark-as-a-service and get rid of your HDInsight/Hadoop clusters.
This is part 1 of an 8 part Data Science for Dummies series:
Databricks for dummies
Titanic survival prediction with Databricks + Python + Spark ML
Titanic with Azure Machine Learning Studio
Titanic with Databricks + Azure Machine Learning Service
Titanic with Databricks + MLS + AutoML
Titanic with Databricks + MLFlow
Titanic with DataRobot
Deployment, DevOps/MLops and Operationalization
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
Large Scale Lakehouse Implementation Using Structured StreamingDatabricks
Business leads, executives, analysts, and data scientists rely on up-to-date information to make business decision, adjust to the market, meet needs of their customers or run effective supply chain operations.
Come hear how Asurion used Delta, Structured Streaming, AutoLoader and SQL Analytics to improve production data latency from day-minus-one to near real time Asurion’s technical team will share battle tested tips and tricks you only get with certain scale. Asurion data lake executes 4000+ streaming jobs and hosts over 4000 tables in production Data Lake on AWS.
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.
Presto: Fast SQL-on-Anything (including Delta Lake, Snowflake, Elasticsearch ...Databricks
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Comcast, GrubHub, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
Building a SIMD Supported Vectorized Native Engine for Spark SQLDatabricks
Spark SQL works very well with structured row-based data. Vectorized reader and writer for parquet/orc can make I/O much faster. It also used WholeStageCodeGen to improve the performance by Java JIT code. However Java JIT is usually not working very well on utilizing latest SIMD instructions under complicated queries. Apache Arrow provides columnar in-memory layout and SIMD optimized kernels as well as a LLVM based SQL engine Gandiva. These native based libraries can accelerate Spark SQL by reduce the CPU usage for both I/O and execution.
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
Writing Apache Spark and Apache Flink Applications Using Apache BahirLuciano Resende
Big Data is all about being to access and process data in various formats, and from various sources. Apache Bahir provides extensions to distributed analytic platforms providing them access to different data sources. In this talk we will introduce you to Apache Bahir and its various connectors that are available for Apache Spark and Apache Flink. We will also go over the details of how to build, test and deploy an Spark Application using the MQTT data source for the new Apache Spark 2.0 Structure Streaming functionality.
Building data pipelines for modern data warehouse with Apache® Spark™ and .NE...Michael Rys
This presentation shows how you can build solutions that follow the modern data warehouse architecture and introduces the .NET for Apache Spark support (https://dot.net/spark, https://github.com/dotnet/spark)
Spark sql under the hood - Data KRK meetupMikołaj Kromka
In recent years Apache Spark has received a lot of hype in the Big Data community. It is seen as a silver bullet for all problems related to gathering, processing and analysing massive datasets. Due to its rapid evolution (do not forget that Spark is one the most active open source projects), some of the ideas behind it seem to be unclear and require digging into different blog posts and presentations. During this talk we will dive into the internals of Spark SQL, look how our queries are translated to the actual code executed on the nodes and find different ways to debug and optimize them.
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].
• What is Machine Learning?
• Overview to Machine Learning Algorithms
• Introduction to SparkR
• Installation of SparkR
• Getting Data with SparkR
• SQL queries in SparkR
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.
Fast and Simplified Streaming, Ad-Hoc and Batch Analytics with FiloDB and Spa...Helena Edelson
O'Reilly Webcast with Myself and Evan Chan on the new SNACK Stack (playoff of SMACK) with FIloDB: Scala, Spark Streaming, Akka, Cassandra, FiloDB and Kafka.
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How to Become Strategy Manager 2023 ? | Strategic Management | Roadmap | Simp...Simplilearn
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🔥 6 Reasons To Become A Data Engineer | Why You Should Become A Data Engineer...Simplilearn
🔥Link to watch video: https://youtu.be/m9ViGf3iPHo
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This video is based on 6 Reasons To Become A Data Engineer. In this video, we delve into the role of a Data Engineer and present 6 compelling reasons why it's an incredible career choice. From building cutting-edge solutions to unlocking valuable insights, join us as we embark on an exciting journey through the world of Data Engineering. If you're seeking a dynamic and impactful profession, don't miss out on the opportunities that await you as a Data Engineer!
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https://www.youtube.com/watch?v=9z0BNicnBjw
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Deloitte Interview Questions And Answers | Top 45 Deloitte Interview Question...Simplilearn
https://www.youtube.com/watch?v=Cfj0y6xIo48
Deloitte is one of the reputed “Big Four” accounting companies and the largest professional service provider by revenue as well as the number of professionals. With more than 263900 professionals worldwide, the organisation provides financial advising, corporate risk, consulting, tax, and audit services. Deloitte generated revenue of a record USD 38.8 billion in the financial year 2017 and is ranked as the sixth-largest private company in the United States as of 2016. In this video session on Deloitte interview questions and answers, we will go through different interview questions often asked during the interview process at Deloitte.
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In this SQL Injection video, we delve into the world of SQL Injection attacks, one of the most prevalent threats to databases today. Join us as we explore the inner workings of this malicious technique and understand how hackers exploit vulnerabilities in web applications to gain unauthorized access to sensitive data. With step-by-step examples and demonstrations, we provide comprehensive insights on the various types of SQL Injection attacks and their potential consequences. Moreover, we equip you with essential knowledge and countermeasures to safeguard your database against these attacks, ensuring the security of your valuable information. Don't let your data fall victim to SQL Injection—watch this video now!
Top 5 High Paying Cloud Computing Jobs in 2023 Simplilearn
This video, "Top 5 High Paying Cloud Computing Jobs In 2023" by Simplilearn will take you through 5 different job role which are the highest paid in 2023. In this Cloud Computing Jobs and salary video, we'll talk about the required skills and the average salary of various job profiles in the United States. Below are the topics covered in this Cloud Computing Jobs and Salary 2023 video.
This video, "Types of Cloud Jobs In 2024," by Simplilearn, will take you through the different types of cloud computing jobs available in the field of cloud computing in 2024. In this video, we will take you through the roles and responsibilities along with the career path and salaries of each job role available in this dynamic field. In addition, you will also understand through the video which job role matches your skills and interest in this field. Below are the topics we have covered in this video on Types of Cloud Jobs in 2024.
Top 12 AI Technologies To Learn 2024 | Top AI Technologies in 2024 | AI Trend...Simplilearn
🔥 Become An AI & ML Expert Today: https://taplink.cc/simplilearn_ai_ml
Explore the future of AI in our Top 12 AI Technologies To Learn in 2024 video. We've curated a list of the most significant AI technologies for the coming year. Whether you're new to AI or an experienced pro, these insights are valuable. Discover machine learning, natural language processing, computer vision, and more. Stay ahead of the AI curve, and ensure you're prepared for the evolving landscape. Don't miss out on the opportunity to advance your AI knowledge and career.
Here in this Top 12 AI Technologies To Learn 2024 video, we start with:
What is LSTM ?| Long Short Term Memory Explained with Example | Deep Learning...Simplilearn
In this video on What is LSTM, we will go through what is LSTM, moving forward we will learn what is RNN, and after this, we will see the types of gates in LSTM and some applications of LSTM. At the end of the video, we will see a hands-on lab demo of gold price prediction using the LSTM model in machine learning.
00:00 What is LSTM?
01:51 What is RNN?
02:29 Types of gates in LSTM
03:45 Applications of LSTM
05:40 Hands-on lab demo
Dataset link: https://drive.google.com/drive/folder...
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What is LSTM?
Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) that can capture long-term dependencies in sequential data. LSTMs are able to process and analyze sequential data, such as time series, text, and speech. They use a memory cell and gates to control the flow of information, allowing them to selectively retain or discard information as needed and thus avoid the vanishing gradient problem that plagues traditional RNNs. LSTMs are widely used in various applications such as natural language processing, speech recognition, and time series forecasting.
What is RNN?
RNNs are a type of neural network that are designed to process sequential data. They can analyze data with a temporal dimension, such as time series, speech, and text. RNNs can do this by using a hidden state passed from one timestep to the next. The hidden state is updated at each timestep based on the input and the previous hidden state. RNNs are able to capture short-term dependencies in sequential data, but they struggle with capturing long-term dependencies.
Types of Gates in LSTM
Input gate
Output gate
Forget gate
Applications of LSTM
Language Simulation
Voice Recognition
Sentiment analysis
Time series prediction
Video analysis
Handwriting recognition
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✅ About Artificial Intelligence Engineer Master's Program
The Artificial Intelligence course, created in partnership with IBM, introduces students to blended learning and prepares them to be specialists in AI and Data Science. IBM, located in Armonk, New York, is a significant cognitive services and integrated cloud solution firm that provides many technology and consulting solutions. IBM invests $6 billion in research & development every year and has won five Nobel prizes, nine US Na
Top 10 Chat GPT Use Cases | ChatGPT Applications | ChatGPT Tutorial For Begin...Simplilearn
In this video on ChatGPT Usecases, we will explore ChatGPT by OpenAI, which interacts conversationally. This ChatGPT tutorial for beginners will help you understand what chatGPT is, How it works, and the Different Usecases of chatGPT to make your life easier.
00:00 Chat GPT Usecases
01:04 What is Chat GPT?
01:25 How does Chat GPT work?
01:58 Demo -Usecases
02:21 Explain complex subjects
04:12 Write any code
06:36 Audit/Debug any code
08:26 Create custom plans for marketing strategy
11:54 Write articles and blogs
16:00 Summarize book or article
17:35 Answer interview questions
19:38 Develop apps
21:45 Create diet plan and exercise plan
24:00 Answer general knowledge questions
What is ChatGPT
ChatGPT is a conversational language model created by OpenAI. It is a form of the Generative Pre-trained Transformer (GPT) model, which was trained on a dataset of conversational prompts, such as dialogue snippets and chat logs. The model is capable of generating human-like responses to text inputs.
How does chatGPT work?
This model is pre-trained on a large text dataset and then fine-tuned on a smaller dataset specific to the everyday task.
When the model receives input from a user, it uses the patterns it learned during fine-tuning to generate a response
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✅ About Artificial Intelligence Engineer Master's Program:
Accelerate your career with this Artificial Intelligence Course in conjunction with IBM. Industry-relevant AI courses, including Data Science with Python, Machine Learning, Deep Learning, and NLP, feature unique hackathons and Ask Me Anything sessions hosted by IBM. Get job-ready AI certification training with Capstone projects, practical labs, live sessions, and hands-on projects.
✅ What skills are covered in this Artificial Intelligence course?
You will be able to demonstrate the following ability after completing this Artificial Intelligence certification training:
- Learn Artificial Intelligence's concept, purpose, domain breadth, phases, implementations, and impacts.
- Create real-world projects, games, prediction models, logic constraint
React JS Vs Next JS - What's The Difference | Next JS Tutorial For Beginners ...Simplilearn
In this video on "React JS Vs Next JS - What's The Difference," we will start with what is React js and Next js. After that, we will see the difference between React and Next js regarding performance, development cost, community, and many more. Moving ahead, we will talk about the features of React and Next js, and then we will see where we can use react and next js.
00:00 - React JS Vs Next JS - What's The Difference
01:55 - What is React js?
02:53 - What is Next js?
03:18 - Differnce between React and Next js
03:48 - React vs Next js : Performance
04:38 - React vs Next js : Documentation
05:05 - React vs Next js : Server side rendering
05:35 - React vs Next js : Community
06:40 - React vs Next js : Configuration
07:18 - React vs Next js : Maintance
07:43 - React vs Next js : Development Cost
08:00 - React vs Next js : Features
08:37 - Where React js and Next js is used?
What is React JS?
1. React is a JavaScript library that builds fast, interactive mobile and web applications.
2. It is an open-source, reusable component-based front-end library of JavaScript.
3. React is a combination of HTML and JavaScript.
4. It provides a robust and opinionated way to build modern applications Interface.
What Next js is.
1.Next js is an open-source web framework created by Vercel.
2. Next js enables React-based web applications with server-side rendering and generating static websites.
3. In addition, next js offers additional structure, features, and optimizations for your application.
4. Next.js takes care of the tooling and settings required for React Js.
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This program will advance your career as a MEAN stack developer. You’ll learn top skills such as MongoDB, Express.js, Angular, and Node.js (“MEAN”), plus GIT, HTML, CSS, and JavaScript to build and deploy interactive applications and services throughout this Full Stack MEAN Developer program. Full Stack Web Developer Mean Stack course provides complete knowledge of software development and testing technologies such as JavaScript, Node.js, Angular, Docker, and Protractor. You'll build an end-to-end application, test and deploy code, and store data using MongoDB.
✅ What is MEAN Stack?
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Backpropagation in Neural Networks | Back Propagation Algorithm with Examples...Simplilearn
This video covers What is Backpropagation in Neural Networks? Neural Network Tutorial for Beginners includes a definition of backpropagation, working of backpropagation, benefits of backpropagation, and applications.
00:00 - What is Backpropagation?
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01:41 - What is Backpropagation in neural networks?
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02:23 - How does Backpropagation in neural networks work?
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05:28 - Benefits of Backpropagation
This content highlights the importance of backpropagation and gives you a reason to choose the same.
05:54 - Applications of Backpropagation
This phase covers applications of backpropagation in different fields.
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Backpropagation is an algorithm that is created to test errors that will travel back from input nodes to output nodes. It is applied to improve accuracy in data mining and machine learning.The concept of backpropagation in neural networks was first introduced in the 1960s. An artificial neural network is made up of bunches of connected input/output units, each of which is connected by a software program and has a certain weight. This kind of network is based on biological neural networks, which contain neurons coupled to one another across different network levels. In this instance, neurons are shown as nodes.
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Spark SQL Tutorial | Spark SQL Using Scala | Apache Spark Tutorial For Beginners | Simplilearn
1.
2. What is Spark SQL?
Spark SQL Features
Spark SQL Architecture
Spark SQL – DataFrame API
Spark SQL – Data Source
API
Spark SQL – Catalyst
Optimizer
Running SQL Queries
Spark SQL Demo
What’s in it for you?
SQL
3. What is Spark SQL?
SQL
Spark SQL is Apache Spark’s module for working with structured and
semi-structured data
5. SQL
Spark SQL is Apache Spark’s module for working with structured and
semi-structured data
It originated to overcome the
limitations of Apache Hive
What is Spark SQL?
6. SQL
Spark SQL is Apache Spark’s module for working with structured and
semi-structured data
It originated to overcome the
limitations of Apache Hive
Hive lags in performance as it uses MapReduce
jobs for executing ad-hoc queries
Hive does not allow you to resume a job
processing if it fails in the middle
Limitations
What is Spark SQL?
7. SQL
Spark performs better than Hive in most scenarios
Source: https://engineering.fb.com/
Hive ~ Spark
9. SQL
Integrated
High
Compatibility
You can integrate Spark SQL
and query structured data
inside Spark programs
You can run unmodified Hive queries
on existing warehouses in Spark
SQL. With existing Hive data, queries
and UDFs, Spark SQL offers full
compatibility
Below are some essential features of Spark SQL that makes it a compelling
framework for data processing and analyzing
Spark SQL Features
Spark
SQL
Spark
programs
SQLQueries
10. SQL
Scalability
Standard
Connectivity
Spark SQL leverages RDD model as
it supports large jobs and mid-
query fault tolerance. For interactive
and long queries, it uses the same
engine
You can easily connect Spark
SQL with JDBC or ODBC. For
connectivity for business
intelligence tools, both turned as
industry norms
Spark SQL Features
SQL
SQL
RDD
Below are some essential features of Spark SQL that makes it a compelling
framework for data processing and analyzing
13. Spark SQL has three main layers
Spark SQL is Apache Spark’s module for working with structured data
Language API SchemaRDD Data Sources
Spark is very compatible as it
supports languages like Python,
HiveQL, Scala, and Java
As Spark SQL works on schema,
tables, and records, you can use
SchemaRDD or DataFrame as a
temporary table
SQL
Spark SQL supports multiple
data sources like JSON,
Cassandra database, Hive
tables
Spark SQL Architecture
15. A DataFrame is a domain-specific language (DSL) for working
with structured and semi-structured data, i.e., datasets with a schema
Spark SQL – Data Frame API
16. DataFrame API in Spark was
designed taking inspiration from
DataFrame in R programming and
Pandas in Python
Spark SQL – Data Frame API
A DataFrame is a domain-specific language (DSL) for working
with structured and semi-structured data, i.e., datasets with a schema
17. Has can process the data in the size of Kilobytes to Petabytes
on a single node cluster
Can be easily integrated with all Big Data tools and frameworks
via Spark-Core
Provides API for Python, Java, Scala, and R Programming
DataFrame features
Spark SQL – Data Frame API
DataFrame API in Spark was
designed taking inspiration from
DataFrame in R programming and
Pandas in Python
A DataFrame is a domain-specific language (DSL) for working
with structured and semi-structured data, i.e., datasets with a schema
19. Spark SQL supports operating on a variety of data sources through the
DataFrame interface
Spark SQL – Data Source API
20. Spark SQL supports operating on a variety of data sources through the
DataFrame interface
It supports different files such as
CSV, Hive, Avro, JSON, Parquet
Spark SQL – Data Source API
21. It supports different files such as
CSV, Hive, Avro, JSON, Parquet
It is lazily evaluated like Apache
Spark Transformations and can
be accessed through SQL
Context and Hive Context
ContextSQL
Spark SQL – Data Source API
Spark SQL supports operating on a variety of data sources through the
DataFrame interface
22. It can be easily integrated with all
Big Data tools and frameworks
via Spark-Core
ContextSQL
Spark SQL – Data Source API
It supports different files such as
CSV, Hive, Avro, JSON, Parquet
It is lazily evaluated like Apache
Spark Transformations and can
be accessed through SQL
Context and Hive Context
Spark SQL supports operating on a variety of data sources through the
DataFrame interface
24. Catalyst optimizer leverages advanced programming language features
(such as Scala’s pattern matching and quasi quotes) in a novel way to build
an extensible query optimizer
Spark SQL – Catalyst Optimizer
25. It works in 4 phases:
1 Analyzing a logical plan to
resolve references
2 Logical plan optimization
3 Physical planning 4
Code generation to compile parts of
the query to Java bytecode
Spark SQL – Catalyst Optimizer
Catalyst optimizer leverages advanced programming language features
(such as Scala’s pattern matching and quasi quotes) in a novel way to build
an extensible query optimizer
26. SQL
Query
SQL
Query
Spark SQL – Catalyst Optimizer
Catalyst optimizer leverages advanced programming language features
(such as Scala’s pattern matching and quasi quotes) in a novel way to build
an extensible query optimizer
27. SQL
Query
SQL
Query
Unresolved
Logical plan
Spark SQL – Catalyst Optimizer
Catalyst optimizer leverages advanced programming language features
(such as Scala’s pattern matching and quasi quotes) in a novel way to build
an extensible query optimizer
29. SQL
Query
SQL
Query
Unresolved
Logical plan
Logical plan
Optimized
Logical plan
Catalog
Analysis
Logical
Optimization
Spark SQL – Catalyst Optimizer
Catalyst optimizer leverages advanced programming language features
(such as Scala’s pattern matching and quasi quotes) in a novel way to build
an extensible query optimizer
30. SQL
Query
SQL
Query
Unresolved
Logical plan
Logical plan
Optimized
Logical plan
Physical
plans
Catalog
Analysis
Logical
Optimization
Physical
Planning
Spark SQL – Catalyst Optimizer
Catalyst optimizer leverages advanced programming language features
(such as Scala’s pattern matching and quasi quotes) in a novel way to build
an extensible query optimizer
31. SQL
Query
SQL
Query
Unresolved
Logical plan
Logical plan
Optimized
Logical plan
Physical
plans
Cost Model
Catalog
Analysis
Logical
Optimization
Physical
Planning
Spark SQL – Catalyst Optimizer
Catalyst optimizer leverages advanced programming language features
(such as Scala’s pattern matching and quasi quotes) in a novel way to build
an extensible query optimizer
32. SQL
Query
SQL
Query
Unresolved
Logical plan
Logical plan
Optimized
Logical plan
Physical
plans
Cost Model
Selected
Physical
Plan
Catalog
Analysis
Logical
Optimization
Physical
Planning
Spark SQL – Catalyst Optimizer
Catalyst optimizer leverages advanced programming language features
(such as Scala’s pattern matching and quasi quotes) in a novel way to build
an extensible query optimizer
33. SQL
Query
SQL
Query
Unresolved
Logical plan
Logical plan
Optimized
Logical plan
Physical
plans
Cost Model
Selected
Physical
Plan
RDDs
Catalog
Analysis
Logical
Optimization
Physical
Planning
Code Generation
Spark SQL – Catalyst Optimizer
Catalyst optimizer leverages advanced programming language features
(such as Scala’s pattern matching and quasi quotes) in a novel way to build
an extensible query optimizer
36. SparkContext class object (sc) is required for initializing SQLContext class object
The following command initializes
SparkContext through spark-shell
$ spark-shell
Spark SQLContext
SQLContext is a class used for initializing the functionalities of Spark SQL
37. The following command creates a SQLContext
scala> val sqlcontext = new
org.apache.sql.SQLContext(sc)
Spark SQLContext
SparkContext class object (sc) is required for initializing SQLContext class object
SQLContext is a class used for initializing the functionalities of Spark SQL
The following command initializes
SparkContext through spark-shell
$ spark-shell
39. It is the entry point to any functionality in Spark. To create a basic
SparkSession, use SparkSession.builder()
Source: https://spark.apache.org/
SparkSession
40. Applications can create DataFrames with the help of an existing RDD using a
Hive table, or from Spark data sources
The following creates a DataFrame based on the content of a JSON file:
https://spark.apache.org/Source:
Creating DataFrames
42. Structured data can be manipulated using domain-specific language provided
by DataFrames
https://spark.apache.org/Source:
DataFrame Operations
Below are some examples of structured data processing:
46. The sql function on a SparkSession allows applications to run SQL queries
programmatically and returns the result in the form of a DataFrame
https://spark.apache.org/Source:
Running SQL Queries