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Build data processing platforms that can take on even the hardest of your data troubles!

Fast Data Processing Systems with SMACK stack

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Build data processing platforms that can take on even the hardest of your data troubles!

  • Video Duration:07 Hours
  • Cost: $ 124.99

Course Details

SMACK is an open source full stack for big data architecture. It is a combination of Spark, Mesos, Akka, Cassandra, and Kafka. This stack is the newest technique developers have begun to use to tackle critical real-time analytics for big data. This highly practical tutorial will teach you how to integrate these technologies to create a highly efficient data analysis system for fast data processing.Wefll start off with an introduction to SMACK and show you when to use it. First youfll get to grips with functional thinking and problem solving using Scala. Next youfll come to understand the Akka architecture. Then youfll get to know how to improve the data structure architecture and optimize resources using Apache Spark. Moving forward, youfll learn how to perform linear scalability in databases with Apache Cassandra. Youfll grasp the high throughput distributed messaging systems using Apache Kafka. Wefll show you how to build a cheap but effective cluster infrastructure with Apache Mesos. Finally, you will deep dive into the different aspects of SMACK using 2 practical case studies. By the end of the video, you will be able to integrate all the components of the SMACK stack and use them together to achieve highly effective and fast data processing.

Who all can attend

If you are a developer, data architect, or a data scientist looking for information on how to integrate the Big Data stack architecture and how to choose the correct technology in every layer, this video is what you are looking for.

What you will learn from this course

  • Design and implement a fast data Pipeline architecture
  • Think and solve programming challenges in a functional way with Scala
  • Learn to use Akka, the actors model implementation for the JVM
  • Make on memory processing and data analysis with Spark to solve modern business demands
  • Build a powerful and effective cluster infrastructure with Mesos and Docker
  • Manage and consume unstructured and No-SQL data sources with Cassandra
  • Consume and produce messages in a massive way with Kafka

Course Content

  1. An Introduction to SMACK
    • The Course Overview
    • Modern Data-Processing Challenges
    • The Data-Processing Pipeline Architecture
    • SMACK Technologies
    • Understanding Data Expert Profiles and Changing the Data Center Operations

  2. The Language EScala
    • Scala Collections
    • Iterators in Scala
    • More Functions with Scala

  3. The Model EAkka
    • Actor Model In a Nutshell
    • Working with Actors

  4. The Engine EApache Spark
    • Spark Concepts
    • Resilient Distributed Datasets
    • Spark in Cluster Mode
    • Spark Streaming

  5. The Storage EApache Cassandra
    • NoSQL
    • Apache Cassandra Installation
    • Backup and Compression
    • Recovery Techniques
    • Recovery Techniques EDBMS Optimization, Bloom Filter, and More
    • The Spark Cassandra Connector

  6. Connectors ESpark, Cassandra, and Akka
    • Introduction to the Spark Cassandra Connector
    • Cassandra and Spark Streaming Basics
    • Functions with Cassandra
    • Akka and Cassandra

  7. The Broker EApache Kafka
    • Introducing Kafka
    • Installation
    • Cluster
    • Architecture
    • Producers
    • Consumers
    • Integration and Administration

  8. Connectors EAkka, Spark, Kafka, and Cassandra
    • Akka, Spark, and Kafka
    • Kafka and Cassandra

  9. The Manager EApache Mesos
    • The Apache Mesos Architecture
    • Resource Allocation
    • Running a Mesos Cluster on a Private Data Center
    • Scheduling and Managing the Frameworks
    • Apache Aurora
    • Singularity
    • Apache Spark on Apache Mesos
    • Apache Cassandra on Apache Mesos
    • Apache Kafka on Apache Mesos

Contact Us

Instructor-led online training is also available for the same course.

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