Applying DevOps to Databricks can be a daunting task. In this talk this will be broken down into bite size chunks. Common DevOps subject areas will be covered, including CI/CD (Continuous Integration/Continuous Deployment), IAC (Infrastructure as Code) and Build Agents.
We will explore how to apply DevOps to Databricks (in Azure), primarily using Azure DevOps tooling. As a lot of Spark/Databricks users are Python users, will will focus on the Databricks Rest API (using Python) to perform our tasks.
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
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
Applying DevOps to Databricks can be a daunting task. In this talk this will be broken down into bite size chunks. Common DevOps subject areas will be covered, including CI/CD (Continuous Integration/Continuous Deployment), IAC (Infrastructure as Code) and Build Agents.
We will explore how to apply DevOps to Databricks (in Azure), primarily using Azure DevOps tooling. As a lot of Spark/Databricks users are Python users, will will focus on the Databricks Rest API (using Python) to perform our tasks.
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
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
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
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.
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
Data Build Tool (DBT) is an open source technology to set up your data lake using best practices from software engineering. This SQL first technology is a great marriage between Databricks and Delta. This allows you to maintain high quality data and documentation during the entire datalake life-cycle. In this talk I’ll do an introduction into DBT, and show how we can leverage Databricks to do the actual heavy lifting. Next, I’ll present how DBT supports Delta to enable upserting using SQL. Finally, we show how we integrate DBT+Databricks into the Azure cloud. Finally we show how we emit the pipeline metrics to Azure monitor to make sure that you have observability over your pipeline.
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.
The landscape for storing your big data is quite complex, with several competing formats and different implementations of each format. Understanding your use of the data is critical for picking the format. Depending on your use case, the different formats perform very differently. Although you can use a hammer to drive a screw, it isn’t fast or easy to do so.
The use cases that we’ve examined are:
* reading all of the columns
* reading a few of the columns
* filtering using a filter predicate
* writing the data
Furthermore, different kinds of data have distinct properties. We've used three real schemas:
* the NYC taxi data http://tinyurl.com/nyc-taxi-analysis
* the Github access logs http://githubarchive.org
* a typical sales fact table with generated data
Finally, the value of having open source benchmarks that are available to all interested parties is hugely important and all of the code is available from Apache.
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
Hive Bucketing in Apache Spark with Tejas PatilDatabricks
Bucketing is a partitioning technique that can improve performance in certain data transformations by avoiding data shuffling and sorting. The general idea of bucketing is to partition, and optionally sort, the data based on a subset of columns while it is written out (a one-time cost), while making successive reads of the data more performant for downstream jobs if the SQL operators can make use of this property. Bucketing can enable faster joins (i.e. single stage sort merge join), the ability to short circuit in FILTER operation if the file is pre-sorted over the column in a filter predicate, and it supports quick data sampling.
In this session, you’ll learn how bucketing is implemented in both Hive and Spark. In particular, Patil will describe the changes in the Catalyst optimizer that enable these optimizations in Spark for various bucketing scenarios. Facebook’s performance tests have shown bucketing to improve Spark performance from 3-5x faster when the optimization is enabled. Many tables at Facebook are sorted and bucketed, and migrating these workloads to Spark have resulted in a 2-3x savings when compared to Hive. You’ll also hear about real-world applications of bucketing, like loading of cumulative tables with daily delta, and the characteristics that can help identify suitable candidate jobs that can benefit from bucketing.
A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...Databricks
Of all the developers’ delight, none is more attractive than a set of APIs that make developers productive, that are easy to use, and that are intuitive and expressive. Apache Spark offers these APIs across components such as Spark SQL, Streaming, Machine Learning, and Graph Processing to operate on large data sets in languages such as Scala, Java, Python, and R for doing distributed big data processing at scale. In this talk, I will explore the evolution of three sets of APIs-RDDs, DataFrames, and Datasets-available in Apache Spark 2.x. In particular, I will emphasize three takeaways: 1) why and when you should use each set as best practices 2) outline its performance and optimization benefits; and 3) underscore scenarios when to use DataFrames and Datasets instead of RDDs for your big data distributed processing. Through simple notebook demonstrations with API code examples, you’ll learn how to process big data using RDDs, DataFrames, and Datasets and interoperate among them. (this will be vocalization of the blog, along with the latest developments in Apache Spark 2.x Dataframe/Datasets and Spark SQL APIs: https://databricks.com/blog/2016/07/14/a-tale-of-three-apache-spark-apis-rdds-dataframes-and-datasets.html)
"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.
"
Big data architectures and the data lakeJames Serra
With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!
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.
Building the Enterprise Data Lake - Important Considerations Before You Jump InSnapLogic
In this webinar, learn from industry analyst and big data thought leader Mark Madsen about the future of big data and importance of the new Enterprise Data Lake reference architecture.
This webinar also covers what’s important when building a modern, multi-use data infrastructure, the difference between a Hadoop application and a Data Lake infrastructure, and an enterprise data lake reference architecture to get you started.
To learn more, visit: www.snaplogic.com/big-data
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
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.
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
Data Build Tool (DBT) is an open source technology to set up your data lake using best practices from software engineering. This SQL first technology is a great marriage between Databricks and Delta. This allows you to maintain high quality data and documentation during the entire datalake life-cycle. In this talk I’ll do an introduction into DBT, and show how we can leverage Databricks to do the actual heavy lifting. Next, I’ll present how DBT supports Delta to enable upserting using SQL. Finally, we show how we integrate DBT+Databricks into the Azure cloud. Finally we show how we emit the pipeline metrics to Azure monitor to make sure that you have observability over your pipeline.
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.
The landscape for storing your big data is quite complex, with several competing formats and different implementations of each format. Understanding your use of the data is critical for picking the format. Depending on your use case, the different formats perform very differently. Although you can use a hammer to drive a screw, it isn’t fast or easy to do so.
The use cases that we’ve examined are:
* reading all of the columns
* reading a few of the columns
* filtering using a filter predicate
* writing the data
Furthermore, different kinds of data have distinct properties. We've used three real schemas:
* the NYC taxi data http://tinyurl.com/nyc-taxi-analysis
* the Github access logs http://githubarchive.org
* a typical sales fact table with generated data
Finally, the value of having open source benchmarks that are available to all interested parties is hugely important and all of the code is available from Apache.
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
Hive Bucketing in Apache Spark with Tejas PatilDatabricks
Bucketing is a partitioning technique that can improve performance in certain data transformations by avoiding data shuffling and sorting. The general idea of bucketing is to partition, and optionally sort, the data based on a subset of columns while it is written out (a one-time cost), while making successive reads of the data more performant for downstream jobs if the SQL operators can make use of this property. Bucketing can enable faster joins (i.e. single stage sort merge join), the ability to short circuit in FILTER operation if the file is pre-sorted over the column in a filter predicate, and it supports quick data sampling.
In this session, you’ll learn how bucketing is implemented in both Hive and Spark. In particular, Patil will describe the changes in the Catalyst optimizer that enable these optimizations in Spark for various bucketing scenarios. Facebook’s performance tests have shown bucketing to improve Spark performance from 3-5x faster when the optimization is enabled. Many tables at Facebook are sorted and bucketed, and migrating these workloads to Spark have resulted in a 2-3x savings when compared to Hive. You’ll also hear about real-world applications of bucketing, like loading of cumulative tables with daily delta, and the characteristics that can help identify suitable candidate jobs that can benefit from bucketing.
A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...Databricks
Of all the developers’ delight, none is more attractive than a set of APIs that make developers productive, that are easy to use, and that are intuitive and expressive. Apache Spark offers these APIs across components such as Spark SQL, Streaming, Machine Learning, and Graph Processing to operate on large data sets in languages such as Scala, Java, Python, and R for doing distributed big data processing at scale. In this talk, I will explore the evolution of three sets of APIs-RDDs, DataFrames, and Datasets-available in Apache Spark 2.x. In particular, I will emphasize three takeaways: 1) why and when you should use each set as best practices 2) outline its performance and optimization benefits; and 3) underscore scenarios when to use DataFrames and Datasets instead of RDDs for your big data distributed processing. Through simple notebook demonstrations with API code examples, you’ll learn how to process big data using RDDs, DataFrames, and Datasets and interoperate among them. (this will be vocalization of the blog, along with the latest developments in Apache Spark 2.x Dataframe/Datasets and Spark SQL APIs: https://databricks.com/blog/2016/07/14/a-tale-of-three-apache-spark-apis-rdds-dataframes-and-datasets.html)
"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.
"
Big data architectures and the data lakeJames Serra
With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!
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.
Building the Enterprise Data Lake - Important Considerations Before You Jump InSnapLogic
In this webinar, learn from industry analyst and big data thought leader Mark Madsen about the future of big data and importance of the new Enterprise Data Lake reference architecture.
This webinar also covers what’s important when building a modern, multi-use data infrastructure, the difference between a Hadoop application and a Data Lake infrastructure, and an enterprise data lake reference architecture to get you started.
To learn more, visit: www.snaplogic.com/big-data
Overview of Apache Flink: Next-Gen Big Data Analytics FrameworkSlim Baltagi
These are the slides of my talk on June 30, 2015 at the first event of the Chicago Apache Flink meetup. Although most of the current buzz is about Apache Spark, the talk shows how Apache Flink offers the only hybrid open source (Real-Time Streaming + Batch) distributed data processing engine supporting many use cases: Real-Time stream processing, machine learning at scale, graph analytics and batch processing.
In these slides, you will find answers to the following questions: What is Apache Flink stack and how it fits into the Big Data ecosystem? How Apache Flink integrates with Apache Hadoop and other open source tools for data input and output as well as deployment? What is the architecture of Apache Flink? What are the different execution modes of Apache Flink? Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark? Who is using Apache Flink? Where to learn more about Apache Flink?
Apache Flink: Real-World Use Cases for Streaming AnalyticsSlim Baltagi
This face to face talk about Apache Flink in Sao Paulo, Brazil is the first event of its kind in Latin America! It explains how Apache Flink 1.0 announced on March 8th, 2016 by the Apache Software Foundation (link), marks a new era of Big Data analytics and in particular Real-Time streaming analytics. The talk maps Flink's capabilities to real-world use cases that span multiples verticals such as: Financial Services, Healthcare, Advertisement, Oil and Gas, Retail and Telecommunications.
In this talk, you learn more about:
1. What is Apache Flink Stack?
2. Batch vs. Streaming Analytics
3. Key Differentiators of Apache Flink for Streaming Analytics
4. Real-World Use Cases with Flink for Streaming Analytics
5. Who is using Flink?
6. Where do you go from here?
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.
Designing a Scalable Twitter - Patterns for Designing Scalable Real-Time Web ...Nati Shalom
Twitter is a good example for next generation real-time web applications, but building such an application imposes challenges such as handling an every growing volume of tweets and responses, as well as a large number of concurrent users, who continually *listen* for tweets from users (or topics) they follow. During this session we will review some of the key design principles addressing these challenges, including alternatives *NoSQL* alternatives and blackboard patterns. We will be using Twitter as a use case, while learning how to apply these to any real-time we application
Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & AlluxioAlluxio, Inc.
Alluxio Global Online Meetup
Apr 23, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Jiao (Jennie) Wang, Intel
Tsai Louie, Intel
Bin Fan, Alluxio
Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked.
Intel Analytics Zoo is a unified data analytics and AI platform open-sourced by Intel. It seamlessly unites TensorFlow, Keras, PyTorch, Spark, Flink, and Ray programs into an integrated pipeline, which can transparently scale from a laptop to large clusters to process production big data. Alluxio, as an open-source data orchestration layer, accelerates data loading and processing in Analytics Zoo deep learning applications.
This talk, we will go over:
- What is Analytics Zoo and how it works
- How to run Analytics Zoo with Alluxio in deep learning applications
- Initial performance benchmark results using the Analytics Zoo + Alluxio stack
Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...confluent
Tinder’s Quickfire Pipeline powers all things data at Tinder. It was originally built using AWS Kinesis Firehoses and has since been extended to use both Kafka and other event buses. It is the core of Tinder’s data infrastructure. This rich data flow of both client and backend data has been extended to service a variety of needs at Tinder, including Experimentation, ML, CRM, and Observability, allowing backend developers easier access to shared client side data. We perform this using many systems, including Kafka, Spark, Flink, Kubernetes, and Prometheus. Many of Tinder’s systems were natively designed in an RPC first architecture.
Things we’ll discuss decoupling your system at scale via event-driven architectures include:
– Powering ML, backend, observability, and analytical applications at scale, including an end to end walk through of our processes that allow non-programmers to write and deploy event-driven data flows.
– Show end to end the usage of dynamic event processing that creates other stream processes, via a dynamic control plane topology pattern and broadcasted state pattern
– How to manage the unavailability of cached data that would normally come from repeated API calls for data that’s being backfilled into Kafka, all online! (and why this is not necessarily a “good” idea)
– Integrating common OSS frameworks and libraries like Kafka Streams, Flink, Spark and friends to encourage the best design patterns for developers coming from traditional service oriented architectures, including pitfalls and lessons learned along the way.
– Why and how to avoid overloading microservices with excessive RPC calls from event-driven streaming systems
– Best practices in common data flow patterns, such as shared state via RocksDB + Kafka Streams as well as the complementary tools in the Apache Ecosystem.
– The simplicity and power of streaming SQL with microservices
This is the course that was presented by James Liddle and Adam Vile for Waters in September 2008.
The book of this course can be found at: http://www.lulu.com/content/4334860
Off-Label Data Mesh: A Prescription for Healthier DataHostedbyConfluent
"Data mesh is a relatively recent architectural innovation, espoused as one of the best ways to fix analytic data. We renegotiate aged social conventions by focusing on treating data as a product, with a clearly defined data product owner, akin to that of any other product. In addition, we focus on building out a self-service platform with integrated governance, letting consumers safely access and use the data they need to solve their business problems.
Data mesh is prescribed as a solution for _analytical data_, so that conventionally analytical results (think weekly sales or monthly revenue reports) can be more accurately and predictably computed. But what about non-analytical business operations? Would they not also benefit from data products backed by self-service capabilities and dedicated owners? If you've ever provided a customer with an analytical report that differed from their operational conclusions, then this talk is for you.
Adam discusses the resounding successes he has seen from applying data mesh _off-label_ to both analytical and operational domains. The key? Event streams. Well-defined, incrementally updating data products that can power both real-time and batch-based applications, providing a single source of data for a wide variety of application and analytical use cases. Adam digs into the common areas of success seen across numerous clients and customers and provides you with a set of practical guidelines for implementing your own minimally viable data mesh.
Finally, Adam covers the main social and technical hurdles that you'll encounter as you implement your own data mesh. Learn about important data use cases, data domain modeling techniques, self-service platforms, and building an iteratively successful data mesh."
Data Engineer's Lunch #63: Building a Cryptocurrency Data CatalogueAnant Corporation
In Data Engineer’s Lunch #63, Travis Collins, founder of the open source project DataPM, will present DataPM, how to get access to cryptocurrency, and blockchain data. This is part 1 of a series with Decodable on processing real-time crypto transactions fed by DataPM.
Accompanying YouTube: https://youtu.be/_YltwetuPK0
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Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010Bhupesh Bansal
Jan 22nd, 2010 Hadoop meetup presentation on project voldemort and how it plays well with Hadoop at linkedin. The talk focus on Linkedin Hadoop ecosystem. How linkedin manage complex workflows, data ETL , data storage and online serving of 100GB to TB of data.
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
Webinar: Ways to Succeed with Hadoop in 2015Edureka!
The webinar on Big Data and Hadoop titled " Ways to Succeed with Hadoop in 2015 " conducted by Edureka in association with TechGig.com on 29th December 2014
Hadoop is an open source software framework that supports data-intensive distributed applications. Hadoop is licensed under the Apache v2 license. It is therefore generally known as Apache Hadoop. Hadoop has been developed, based on a paper originally written by Google on MapReduce system and applies concepts of functional programming. Hadoop is written in the Java programming language and is the highest-level Apache project being constructed and used by a global community of contributors. Hadoop was developed by Doug Cutting and Michael J. Cafarella. And just don't overlook the charming yellow elephant you see, which is basically named after Doug's son's toy elephant!
The topics covered in presentation are:
1. Big Data Learning Path
2.Big Data Introduction
3. Hadoop and its Eco-system
4.Hadoop Architecture
5.Next Step on how to setup Hadoop
Hadoop simplifies your job as a Data Warehousing professional. With Hadoop, you can manage any volume, variety and velocity of data, flawlessly and comparably in less time. As a Data Warehousing professional, you will undoubtedly have troubleshooting and data processing skills. These skills are sufficient for you to be a proficient Hadoop-er.
Key Questions Answered
What is Big Data and Hadoop?
What are the limitations of current Data Warehouse solutions?
How Hadoop solves these problems?
Real World Hadoop Use-Case in Data Warehouse Solutions?
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
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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YouTube Link:https://youtu.be/CVv8zhYEjUE
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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YouTube Link:https://youtu.be/LvgqSMlIXFs
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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YouTube Link: https://youtu.be/GJQ36pIYbic
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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Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
2. LIVE On-line Class
Class Recording in LMS
24/7 Post Class Support
Module Wise Quiz and Assignment
Project Work on Large Data Set
Verifiable Certificate
How it Works?
Slide 2 www.edureka.in/apache-storm
3. Course Topics
Slide 3 www.edureka.in/apache-storm
Module 1
» Introduction to Big Data and Storm
Module 2
» Storm Technology Stack and Groupings
Module 3
» Spouts and Bolts
Module 4
» Trident Topologies
Module 5
» Real Life Storm Project -1
Module 6
» Real Life Storm Project -2
4. Objectives
Slide 4 www.edureka.in/apache-storm
At the end of this module, you will be able to:
Recall Big Data and Hadoop
Understand Batch and Real-time Analytics of Big Data
Investigate Shortcoming of Hadoop
Understand Lambda Architecture
Develop a basic knowledge of Apache Storm and its components
Explain the Use Cases and Key Differentiators of Storm
5. Big Data
Slide 5 www.edureka.in/apache-storm
Storm is a open source computing system used for processing Real-time Big Data Analytics.
Lets understand Big Data first to learn STORM.
6. Lots of Data - Terabytes or Petabytes
Big data is the term for a collection of data sets so
large and complex that it becomes difficult to process
using on-hand database management tools or
traditional data processing applications.
The challenges include capture, curation, storage,
search, sharing, transfer, analysis, and visualization.
What is Big Data?
Slide 6 www.edureka.in/apache-storm
7. Systems / Enterprises generate huge amount of data from Terabytes and even Petabytes of information.
Stock market generates about one terabyte of new trade data per day to
perform stock trading analytics to determine trends for optimal trades.
What is Big Data?
Slide 7 www.edureka.in/apache-storm
8. 2,500 exabytes of new information in 2012 with Internet as primary driver.
Digital universe grew by 62% last year to 800K petabytes and will grow to 1.2 “zettabytes” this year.
Slide 8 www.edureka.in/apache-storm
Un-structured Data is Exploding
9. IBM’s Definition – Big Data Characteristics
http://www-01.ibm.com/software/data/bigdata/
IBM’s Definition
Web
logs
Images
Videos
Sensor
Data
Audios
VOLUME VELOCITY VARIETY
Slide 9 www.edureka.in/apache-storm
10. Annie’s Introduction
Hello There!!
My name is Annie.
I love quizzes and
puzzles and I am here to
make you guys think and
answer my questions.
Slide 10 www.edureka.in/apache-storm
11. Annie’s Question
Map the following to correspolnodinTghdeatraet!y!pe:
Slide 11 www.edureka.in/apache-storm
My name is Annie.
I lo quizzes and
Data from EpnutezrpzrliseessyastnemdsI(EaRmP, CRhMereetc.)to
make you guys think and
answer my questions.
- XML files
- Word docs, PDF files, Text files
-
-
E-Mail body
12. Annie’s Answer
XML files -> Semi-structureldodTathaere!!
Slide 12 www.edureka.in/apache-storm
Word docs, PDF filesM, Tyextnfailems -e> UisnsAtrnunctiuer.ed DataE-Mail body -> Unstructured Data
Data from EnterpriseIsylostems q(EuRiPz, zCReMs eatcn.)d-> Structured Data
puzzles and I am here to
make you guys think and
answer my questions.
13. Hadoop and its primary programming model, Map-Reduce, are great for batch-oriented processing of huge amount
of data.
With growing data, Hadoop enables you to horizontally scale your cluster by adding commodity nodes and thus keep
up with query workloads.
is primary programming model
great for batch-oriented processing of huge amount of data
Big Data Batch Analytics
Slide 13 www.edureka.in/apache-storm
14. What is Hadoop?
Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of
commodity computers using a simple programming model.
It is an Open-source Data Management with scale-out storage and distributed processing.
Slide 14 www.edureka.in/apache-storm
15. Hadoop Eco-System
Apache Oozie (Workflow)
HDFS (Hadoop Distributed File System)
HIVE
DW System
Pig Latin
Data Analysis Other
YARN
Frameworks
(MPI,GIRAPH)MapReduce Framework
HBase
YARN
Cluster Resource Management
Slide 15 www.edureka.in/apache-storm
16. This evolution has forced the addition of support for
Higher Level Languages (Pig & Hive) New Real-time Storage Engines (HBase)
Big Data Batch Analytics
Extensions for Streaming Data (Hadoop Streaming)
Slide 16 www.edureka.in/apache-storm
17. Due to batch processing, Hadoop should be deployed in situations such as
Index Building
Pattern Recognitions
Creating Recommendation
Engine
Sentiment Analysis
Situations
generate
huge amount of data
stored
queried
Hadoop for Batch Analytics
Slide 17 www.edureka.in/apache-storm
18. Real-time Big Data Analytics
Social Networking:
» Pick your own Big Data database (RDBMS or NoSQL)
» Measure the immediate impact to your site traffic from
social media, whether a new blog post, a tweet, a “Like”,
or even a comment.
» Knowing this information translates to better conversion
and more effective online campaigns.
Slide 18 www.edureka.in/apache-storm
19. Real-time Big Data Analytics
SaaS:
» Measuring user behaviour and acting upon it is crucial
for improving customer satisfaction and conversion rates
– which represent immediate increases in revenue.
Slide 19 www.edureka.in/apache-storm
20. Real-time Big Data Analytics
Financial Services:
» Determining in real time whether your portfolio is losing
money, or if there is fraud in your system means that you
can prevent disasters as they occur, not after the damage
is done.
» Correlating multiple sources from the market in real-time
results in a more accurate view of the market and enables
more accurate actions to maximize your profit.
Slide 20 www.edureka.in/apache-storm
21. Real Time Big Data Analytics - Options
Apache StormAmazon Kinesis
Slide 21 www.edureka.in/apache-storm
22. Problem Statement:
To find the total number of page views of Edureka’s blog over a
range of time.
Google Analytics can provide you this information.
Example: For a particular day, the data can be:
Need for Real-time Analytics
Slide 22 www.edureka.in/apache-storm
23. petabyte – scale
All Data
Slide 23 www.edureka.in/apache-storm
Need for Real-time Analytics
Challenge:
Querying huge amount of Historical Data is slow
25. Need for Real-time Analytics
Google Analytics might have to keep the historical data for each hour as precompiled view
Page view
Page view
Page view
Page view
Page view
All Data
Query
Slide 25 www.edureka.in/apache-storm
URL Hr of the
day
No. of
pageviews
edureka.in/blog/aboutapachestorm 1 250
edureka.in/blog/aboutapachestorm 2 300
edureka.in/blog/aboutapachestorm 3 455
edureka.in/blog/aboutapachestorm 4 460
edureka.in/blog/aboutapachestorm 5 320
edureka.in/blog/aboutapachestorm 6 111
edureka.in/blog/aboutapachestorm 7 129
Precomputed View
26. Need for Real-time Analytics
Precomputed
View
All Data Query
Slide 26 www.edureka.in/apache-storm
using Hadoop
27. But, what about the
data generated after
last precompiled view?
Slide 27 www.edureka.in/apache-storm
Need for Real-time Analytics
28. Compensating for last few hours of data
Need for Real-time Analytics
spout
bolt
bolt
bolt Real-time
View
Storm
Real-time
Data
Stored
Or
Slide 28 www.edureka.in/apache-storm
Or
30. Lambda Architecture
All data entering the system is dispatched to both the batch layer and the speed layer for processing.
New Data
Speed Layer
Slide 30 www.edureka.in/apache-storm
Batch Layer
1
Serving Layer
31. Lambda Architecture
Batch View
Batch View
Master
Dataset
The batch layer has two functions:
» managing the master dataset (an immutable, append-only set of raw data), and
» to pre-compute the batch views. The serving layer indexes the batch views so that they can be queried in
low-latency, ad-hoc way.
Batch Layer Serving Layer
New Data
Speed Layer
1
2
3
Slide 31 www.edureka.in/apache-storm
32. Lambda Architecture
The speed layer compensates for the high latency of updates to the serving layer and deals with recent data only.
Batch View
Batch View
Real-time
View
Master
Dataset
New Data
Speed Layer
Real-time
View
Batch Layer Serving Layer
1
2
3
Slide 32 www.edureka.in/apache-storm
4
33. Lambda Architecture
Any incoming query can be answered by merging results from batch views and real-time views.
Batch View
Batch View
Real-time
View
Master
Dataset
New Data
Query
Speed Layer
Query
Real-time
View
Batch Layer Serving Layer
1
2
3
Slide 33 www.edureka.in/apache-storm
4
5
34. Storm is a distributed, reliable, fault-tolerant system for processing streams of data.
Fault-tolerant
STORM
processing
Streams of Data
What is Storm?
Slide 34 www.edureka.in/apache-storm
35. The work is delegated to different types of components that are each responsible for a simple specific processing task.
The input stream of a Storm cluster is handled by a component called a spout.
The spout passes the data to a component called a bolt, which transforms it in some way.
A bolt either persists the data in some sort of storage, or passes it to some other bolt.
transforms data
bolt
bolt
spout
spout
bolt
bolt
passes data
passes data
data storage
Input Data
Source
What is Storm?
Slide 35 www.edureka.in/apache-storm
36. Annie’s Question
Storm can be used in:
- Real-time Processing
- Batch Processing
- Both
Slide 36 www.edureka.in/apache-storm
40. Annie’s Question
It is not possible to run Storm process along with MapReduce jobs inside a
Hadoop Cluster.
- True
- False
Slide 40 www.edureka.in/apache-storm
42. ZooKeeper
Nimbus ZooKeeper
ZooKeeper
Supervisor
Supervisor
Supervisor
Supervisor
Supervisor
Nimbus node (master node, similar to the Hadoop
JobTracker):
» Uploads computations for execution
» Distributes code across the cluster
» Launches workers across the cluster
» Monitors computation and reallocates
workers as needed
ZooKeeper nodes:
» Coordinates the Storm cluster
Supervisor nodes :
» Communicates with Nimbus through
Zookeeper, starts and stops workers
according to signals from Nimbus
Storm Components
A Storm cluster has 3 sets of nodes
1. Nimbus node
2. Zookeeper nodes
3. Supervisor nodes
Slide 42 www.edureka.in/apache-storm
43. Annie’s Question
A Nimbus Node is similar to TaskTracker Node in Hadoop Cluster.
- True
- False
Slide 43 www.edureka.in/apache-storm
44. Annie’s Answer
No. A Nimbus Node is more like a JobTracker Node in Hadoop
Slide 44 www.edureka.in/apache-storm
45. Five key abstractions help to understand how Storm
processes data:
Tuples – an ordered list of elements. For example, a
“4-tuple” might be (7, 1, 3, 7)
Streams – an unbounded sequence of tuples
Spouts – sources of streams in a computation (e.g. a
Twitter API)
Bolts – process input streams and produce output
streams. They can: run functions; filter, aggregate, or
join data; or talk to databases
Topologies – the overall calculation, represented
visually as a network of spouts and bolts
spout
spout
bolt
bolt
bolt
bolt
Storm users define topologies for how to process the data when it comes streaming in from the spout.
Slide 45 www.edureka.in/apache-storm
Storm Components
46. Annie’s Question
A Storm topology is defined in terms of
- Nimbus, Zookeeper, Supervisor nodes
- Spout, Bolt
- Spout, Bolt, Nimbus, Zookeeper, Supervisor nodes
- Spout, Bolt, Zookeeper node
Slide 46 www.edureka.in/apache-storm
48. Use Cases of Storm
Processing Streams
Distributed Remote
Procedure Call
Unlike other stream
processing systems,
with Storm there’s no
need for intermediate
queues.
Send data to clients
continuously so they
can update and show
results in real time,
such as site metrics.
Easily parallelize CPU-
intensive operations.
Continuous
Computation
Use Cases of Storm
Slide 48 www.edureka.in/apache-storm
49. Use Cases of Storm
Slide 49 www.edureka.in/apache-storm
Financial Services
» Securities Fraud
» Compliance Violations
» Order Routing
» Pricing
Telecom
» Security Breaches
» Network Outages
» Bandwidth Allocation
» Customer Service
Retail
» Shrinkage
» Stock outs
» Offers
» Pricing
Web
» Application Failure
» Operational Issues
» Personalized Content
Use Storm to prevent certain outcomes or to optimize their objectives.
50. Key Differentiators
Simple to Program Fault-tolerant
It’s painful to do real-
time processing from
scratch.
With storm,
complexity is reduced
drastically.
It’s easier to develop
in a JVM-based
language, but Storm
supports any
language
as long as you use or
implement a small
intermediary library.
The Storm cluster
takes care of workers
going down,
reassigning tasks
when
necessary.
Support for Multiple
Programming
Languages
Key Differentiators
Slide 50 www.edureka.in/apache-storm
