Apache Spark Training

Greens Technologys located in Adyar and OMR provides Apache Spark training in Chennai to provide knowledge and skills to become a successful Spark Developer and prepare you for the Cloudera Certified Associate Spark Hadoop Developer Certification Exam CCA175.

You will get in-depth knowledge of concepts such as HDFS, Flume, Sqoop, RDDs, Spark Streaming, MLlib, SparkSQL, Kafka cluster & API by taking this Apache Spark Course in Chennai.

The Apache Spark Training course in Chennai enables you to master the essential skills in Apache Spark & Scala such as Real-time processing, Spark SQL, Spark streaming, Machine learning programming, GraphX programming, and Shell scripting spark.

Content:

• SCALA (Object Oriented and Functional Programming)
• Getting started With Scala.
• Scala Background, Scala Vs Java and Basics.
• Interactive Scala – REPL, data types, variables,expressions, simple functions.
• Running the program with Scala Compiler.
• Explore the type lattice and use type inference
• Define Methodsand Pattern Matching.
• Scala Environment Set up.
• Scala set up on Windows.
• Scala set up on UNIX.
• Functional Programming.
• What is Functional Programming.
• Differences between OOPS and FPP.
• Collections (Very Important for Spark)
• Iterating, mapping, filtering and counting
• Regular expressions and matching with them.
• Maps, Sets, group By, Options, flatten, flat Map
• Word count, IO operations,file access, flatMap
• Object Oriented Programming.
• Classes and Properties.
• Objects, Packaging and Imports.
• Traits.
• Objects, classes, inheritance, Lists with multiple related types, apply
• Integrations
• What is SBT?
• Integration of Scala in Eclipse IDE.
• Integration of SBT with Eclipse.
• SPARK CORE.
• Batch versus real-time data processing
• Introduction to Spark, Spark versus Hadoop
• Architecture of Spark.
• Coding Spark jobs in Scala
• Exploring the Spark shell -> Creating Spark Context.
• RDD Programming
• Operations on RDD.
• Transformations
• Actions
• Loading Data and Saving Data.
• Key Value Pair RDD.
• Broad cast variables.
• Persistence.
• Configuring and running the Spark cluster.
• Exploring to Multi Node Spark Cluster.
• Cluster management
• Submitting Spark jobs and running in the cluster mode.
• Developing Spark applications in Eclipse
• Tuning and Debugging Spark.
• CASSANDRA (N0SQL DATABASE)
• Learning Cassandra
• Getting started with architecture
• Installing Cassandra.
• Communicating with Cassandra.
• Creating a database.
• Create a table
• Inserting Data
• Modelling Data.
• Creating an Application with Web.
• Updating and Deleting Data.
• SPARK INTEGRATION WITH NO SQL (CASSANDRA) and AMAZON EC2
• Introduction to Spark and Cassandra Connectors.
• Spark With Cassandra -> Set up.
• Creating Spark Context to connect the Cassandra.
• Creating Spark RDD on the Cassandra Data base.
• Performing Transformation and Actions on the Cassandra RDD.
• Running Spark Application in Eclipse to access the data in the Cassandra.
• Introduction to Amazon Web Services.
• Building 4 Node Spark Multi Node Cluster in Amazon Web Services.
• Deploying in Production with Mesos and YARN.
• SPARK STREAMING
• Introduction of Spark Streaming.
• Architecture of Spark Streaming
• Processing Distributed Log Files in Real Time
• Discretized streams RDD.
• Applying Transformations and Actions on Streaming Data
• Integration with Flume and Kafka.
• Integration with Cassandra
• Monitoring streaming jobs.
• SPARK SQL
• Introduction to Apache Spark SQL
• The SQL context
• Importing and saving data
• Processing the Text files,JSON and Parquet Files
• DataFrames
• user-defined functions
• Using Hive
• Local Hive Metastore server
• SPARK MLIB.
• Introduction to Machine Learning
• Types of Machine Learning.
• Introduction to Apache Spark MLLib Algorithms.
• Machine Learning Data Types and working with MLLib.
• Regression and Classification Algorithms.
• Decision Trees in depth.
• Classification with SVM, Naive Bayes
• Clustering with K-Means
• Building the Spark server

Apache Spark Training Objectives:

• Understand what is Apache Spark and Scala programming
• Understand the difference between Apache Spark and Hadoop
• Learn Scala and its programming implementation
• Implement Spark on a cluster
• Write Spark Applications using Python, Java and Scala
• Understand RDD and its operation along with implementation of Spark Algorithms
• Define and explain Spark Streaming
• Learn about the Scala classes concept and execute pattern matching
• Learn Scala Java Interoperability and other Scala operations
• Work on Projects using Scala to run on Spark applications