Knowledge of Scala will definitely be a plus point for learning Spark, but is not mandatory. Knowledge of sql,Big Data, Hadoop developer is mandatory
* Software developers/Engineers
* Project leads, Architects and Project Managers
* Analysts, Data analysts, Java Architects, DBA, and Database related professionals
* Graduates and Professionals aspiring for making a career in Big data and Hadoop “Learn 2 Succeed” Spark Certification Course has helped thousands of Big Data Hadoop professionals around the globe to bag top jobs in the industry. Our Spark Training Course includes lifetime access, 24X7 support and class recordings.
Apache Spark Certification Training Course at “Learn 2 Succeed“is designed to provide knowledge and skills to become a successful Big Data Developer.
You will understand basics of Big Data and Hadoop. You will learn how Spark enables in-memory data processing and runs much faster than Hadoop MapReduce. You will also learn about RDDs, different APIs, which Spark offers such as Spark Streaming, MLlib, Clustering, and Spark SQL. This course is an integral part of a Big Data Developer's Career path. It will also encompass the fundamental concepts like data capturing using Flume, data loading using Sqoop, Kafka cluster, Kafka API.
This course is designed to provide knowledge and skills to become a successful Spark and Hadoop Developer and would help to clear the CCA Spark and Hadoop Developer (CCA175) Examination.
Introduction to Scala for Apache Spark
Objectives - In this module, you will understand the basics of Scala that are required for programming Spark applications. You will learn about the basic constructs of Scala such as variable types, control structures, collections, and more.
OOPS and functional programming in Scala
Objectives - In this module, you will learn about object oriented programming and functional programming techniques in Scala.
Introduction to Big Data and Hadoop
Objectives - In this module, you will understand Big Data, the limitations of the existing solutions for Big Data problem, how Hadoop solves the Big Data problem, Hadoop ecosystem components, Hadoop Architecture, HDFS, Rack Awareness and Replication. You will learn about the Hadoop Cluster Architecture, important configuration files in a Hadoop Cluster. You will get an overview of Apache Sqoop and how it is used in importing and exporting tables from RDBMS to HDFS & vice versa.
Apache Spark Framework
Objectives - In this module, you will understand different frameworks available for Big Data Analytics and the module also includes a first-hand introduction to Spark, demo on Building and Running a Spark Application and Web UI.
Playing with RDD’s
Objectives -In this module, you will learn one of the fundamental building blocks of Spark - RDDs and related manipulations for implementing business logics (Transformations, Actions and Functions performed on RDD). You will learn about Spark applications, how it is developed and configuring Spark properties.
Data frames and Spark SQL
Objectives -In this module, you will learn about Spark SQL which is used to process structured data with SQL queries. You will learn about data-frames and datasets in Spark SQL and perform SQL operations on data-frames.
Machine learning using Spark MLlib
Objectives –In this module you will learn about what is the need for machine learning, types of ML concepts, clustering and MLlib (i.e. Spark’s machine learning library), various algorithms supported by MLlib and implement K-Means Clustering.
Understanding Apache Kafka and Kafka Cluster
Objectives –In this module, you will understand Kafka and Kafka Architecture. Afterwards you will go through the details of Kafka Cluster and you will also learn how to configure different types of Kafka Cluster.
Capturing Data with Apache Flume and integration with Kafka
Objectives –In this module you will get an introduction to Apache Flume and its basic architecture and how it is integrated with Apache Kafka for event processing.
Apache Spark Streaming
Objectives –In this module you will get an opportunity to work on Spark streaming which is used to build scalable fault-tolerant streaming applications. You will learn about DStreams and various Transformations performed on it. You will get to know about main streaming operators, Sliding Window Operators and Stateful Operators.
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