Master Big Data Realtime Streaming

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Master Big Data Realtime Streaming
  • !!! More Courses !!!.txt (1.1 KB)
    • 01 Introduction
    • 001 Introduction.en.srt (4.2 KB)
    • 001 Introduction.mp4 (9.5 MB)
    • 002 Course Objectives.en.srt (2.2 KB)
    • 002 Course Objectives.mp4 (5.6 MB)
    • 003 Walkthrough of the Real Time Pipeline.en.srt (2.1 KB)
    • 003 Walkthrough of the Real Time Pipeline.mp4 (6.5 MB)
    • 004 Additional System Requirements Needed.html (1.8 KB)
    02 Ingesting data into Kafka
    • 001 Installing Kafka on Mac _ Linux.en.srt (9.7 KB)
    • 001 Installing Kafka on Mac _ Linux.mp4 (88.1 MB)
    • 002 Introduction to Kafka Basics.en.srt (4.0 KB)
    • 002 Introduction to Kafka Basics.mp4 (9.4 MB)
    • 003 Helpful Kafka Commands.en.srt (7.6 KB)
    • 003 Helpful Kafka Commands.mp4 (49.4 MB)
    • 004 Generating Data into Kafka.en.srt (7.5 KB)
    • 004 Generating Data into Kafka.mp4 (64.0 MB)
    • 005 KafkaInstallation.txt (0.6 KB)
    • 007 HelpfulKafkaCommands.txt (1.1 KB)
    • 008 CommandsForDataGenerator.txt (0.5 KB)
    • 008 SacremantoRealEstateTransactions.json (222.4 KB)
    • 008 realEstateTransactionsSource.py (0.6 KB)
    03 Real Time Data Processing
    • 001 Data Transformations.en.srt (2.0 KB)
    • 001 Data Transformations.mp4 (10.0 MB)
    • 002 Data Aggregations.en.srt (1.6 KB)
    • 002 Data Aggregations.mp4 (9.4 MB)
    04 Processing events using Flink
    • 001 Introduction to Apache Flink.en.srt (3.9 KB)
    • 001 Introduction to Apache Flink.mp4 (14.8 MB)
    • 002 Creating a Simple Flink Job.en.srt (4.5 KB)
    • 002 Creating a Simple Flink Job.mp4 (157.6 MB)
    • 003 Data Transformations Using Flink.en.srt (10.6 KB)
    • 003 Data Transformations Using Flink.mp4 (109.1 MB)
    • 004 Writing Output to Kafka.en.srt (7.6 KB)
    • 004 Writing Output to Kafka.mp4 (81.8 MB)
    • 005 Data Aggregations Using Flink.en.srt (13.5 KB)
    • 005 Data Aggregations Using Flink.mp4 (160.4 MB)
    • 006 Installing Flink on Mac _ Linux.en.srt (4.2 KB)
    • 006 Installing Flink on Mac _ Linux.mp4 (34.4 MB)
    • 007 Running Job On Flink Cluster.en.srt (8.0 KB)
    • 007 Running Job On Flink Cluster.mp4 (67.2 MB)
    • 017 FlinkCommands.txt (0.3 KB)
    • 017 PluginForFatJar.xml (0.5 KB)
    05 Processing events using Spark Streaming
    • 001 Introduction to Spark Streaming.en.srt (3.8 KB)
    • 001 Introduction to Spark Streaming.mp4 (17.5 MB)
    • 002 Setting up the Project.en.srt (12.3 KB)
    • 002 Setting up the Project.mp4 (129.5 MB)
    • 003 Data Transformation using Spark Streaming.en.srt (20.7 KB)
    • 003 Data Transformation using Spark Streaming.mp4 (169.5 MB)
    • 004 Data Aggregation using Spark Streaming.en.srt (4.5 KB)
    • 004 Data Aggregation using Spark Streaming.mp4 (73.9 MB)
    06 Processing events using Kafka Streams
    • 001 Introduction to Kafka Streams.en.srt (4.0 KB)
    • 001 Introduction to Kafka Streams.mp4 (12.9 MB)
    • 002 Setting up the Project.en.srt (20.2 KB)
    • 002 Setting up the Project.mp4 (190.9 MB)
    • 003 Data Transformation using Kafka Streams.en.srt (22.2 KB)
    • 003 Data Transformation using Kafka Streams.mp4 (272.0 MB)
    • 004 Data Aggregation using Kafka Streams.en.srt (16.8 KB)
    • 004 Data Aggregation using Kafka Streams.mp4 (189.6 MB)
    07 Putting data into Apache Pinot
    • 001 Introduction to Apache Pinot.en.srt (4.9 KB)
    • 001 Introduction to Apache Pinot.mp4 (15.3 MB)
    • 002 Installing Apache Pinot on Mac _ Linux.en.srt (6.3 KB)
    • 002 Installing Apache Pinot on Mac _ Linux.mp4 (46.5 MB)
    • 003 Ingesting data from Kafka into Pinot.en.srt (11.8 KB)
    • 003 Ingesting data from Kafka into Pinot.mp4 (90.7 MB)
    • 004 Querying data from Pinot.en.srt (2.1 KB)
    • 004 Querying data from Pinot.mp4 (14.2 MB)
    08 Putting data into Apache Druid
    • 001 Introduction to Apache Druid.en.srt (3.2 KB)
    • 001 Introduction to Apache Druid.mp4 (15.5 MB)
    • 002 Installing Apache Druid on Mac _ Linux.en.srt (8.3 KB)
    • 002 Installing Apache Druid on Mac _ Linux.mp4 (71.4 MB)
    • 003 Ingesting data from Kafka into Druid.en.srt (6.2 KB)
    • 003 Ingesting data from Kafka into Druid.mp4 (40.4 MB)
    • 004 Querying data from Druid.en.srt (2.0 KB)
    • 004 Querying data from Druid.mp4 (14.0 MB)
    • 032 CommandsUsed.txt (0.6 KB)
    09 Dashboarding
    • 001 Introduction to Apache Superset.en.srt (4.4 KB)
    • 001 Introduction to Apache Superset.mp4 (25.7 MB)
    • 002 Installing Superset on Mac _ Linux.en.srt (4.5 KB)
    • 002 Installing Superset on Mac _ Linux.mp4 (46.6 MB)
    • 003 Exploring Pinot data on Superset.en.srt (8.5 KB)
    • 003 Exploring Pinot data on Superset.mp4 (52.8 MB)
    • 004 Changes for Druid-Superset Connection.en.srt (7.5 KB)
    • 004 Changes for Druid-Superset Connection.mp4 (52.8 MB)
    • 005 Exploring Druid data on Superset.en.srt (7.7 KB)
    • 005 Exploring Druid data on Superset.mp4 (51.3 MB)
    • 006 Creating Dashboards on Superset.en.srt (4.3 KB)
    • 006 Creating Dashboards on Superset.mp4 (19.7 MB)
    • 035 SettingSupersetForPinot.txt (1.0 KB)
    • 037 DruidSupersetIntegration.txt (1.3 KB)
  • logo.jpg (72.1 KB)

Description


Master Big Data Realtime Streaming
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 39 lectures (4h 51m) | Size: 2.24 GB

Learn the Core Concepts of Big data Realtime Streaming Analytics and also work with Hands On Examples

What you'll learn:
Learn designing an end-to-end Real-time Streaming pipeline for Big Data using latest technologies.
Understand the different components in Big Data streaming pipeline.
Use Kafka as the connecting tool between ETL components in the real-time streaming pipeline.
Use Apache Flink, Spark Streaming and Kafka Streams to perform different transformations and aggregations.
Use Druid and Pinot as OLAP technologies in the streaming pipeline.
Use Superset to visualize the real-time incoming data stream to explore and visualize the transformed data.
Hands-on Practicals helping you build all the components and forming a complete end-to-end pipeline.
Learn multiple technologies used in Real-time Streaming pipelines, and you can use the one that better suits your use-case.

Requirements
An exposure to Big Data world will help you better appreciate Real-time Streaming pipelines, but is completely optional.
Basic knowledge of Java and Scala will be helpful, but not mandatory

Description
Getting real-time insights from huge volumes of data is very important for a majority of companies today.

Big data Real-time streaming is used by some of the biggest companies in the world like e-commerce companies, Video streaming companies, Banks, Ride-hailing companies, etc.

Knowing about the concepts of realtime streaming and the various realtime streaming technologies will be a great addition to your skillset and will enable you to build some of the most cutting-edge solutions that exist today.

We have created this Hands-On Course so that you get a good understanding about how realtime streaming systems can be built

This course will ensure that you get a hands-on experience with Apache Kafka, Apache Flink, Spark Streaming, Kafka Streams, Apache Pinot, Apache Druid, and Apache Superset.

This course covers the following topics

An Introduction to Kafka with hands-on Kafka setup

Understanding basic transformations and aggregations which can be done in a real time system

Learn how transformations and aggregations can be done using Apache Flink with hands-on coding exercises

Learn how transformations and aggregations can be done using Spark streaming with hands-on coding exercises

Learn how Kafka streams can be used to perform transformations and aggregations with hands-on coding exercises

Ingest data into Apache Pinot which is an OLAP technology

Ingest data into Apache Druid which is also an OLAP technology

Using Apache Superset to create some insightful dashboards

If you are interested in learning how all these technologies can be connected together to build an end to end real-time streaming system, then this course is for you.

Who this course is for
Students who want to learn building real-time streaming pipelines from SCRATCH to its Live Project Implementation.
Students who want to learn latest technologies that are used in Big Data Engineering.
Developers who want to learn different well-known tools to build streaming pipelines.
Students who want to pursue and grow career in Data Engineering.



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2.4 GB
seeders:4
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Master Big Data Realtime Streaming

Torrent hash: A3C3D277E1865BF5F91659D0A6910D4E171AC221