Automation Minds located in Adyar and OMR provides AI and Deep Learning with TensorFlow 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 AI and Deep Learning with TensorFlow 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.

Automation Minds Apache Spark and Scala Certification Training Course in Chennai offer you hands-on knowledge to create Spark applications using Scala programming. It gives you a clear comparison between Spark and Hadoop. The course provides you techniques to increase application performance and enable high-speed processing using Spark RDDs as well as help in customization of Spark using Scala.

AI AND DEEP LEARNING WITH TENSORFLOW TRAINING COURSE CONTENT

1. SCALA (Object Oriented and Functional Programming)
2. Scala Environment Set up.
3. Functional Programming.
4. Collections (Very Important for Spark)
5. Object Oriented Programming.
6. Integrations
7. SPARK CORE.
8. Persistence.
9. CASSANDRA (N0SQL DATABASE)
10. SPARK INTEGRATION WITH NO SQL (CASSANDRA) and AMAZON EC2
11. SPARK STREAMING
12. SPARK SQL
13. SPARK MLIB.

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