Best Institutes for Apache Spark 2.4 training in West Bengal with Course Fees
List of 2+ Apache Spark 2.4 training institutes located near to you in West Bengal as on June 3, 2020. Get access to training curriculum, placement training, course fees, contact phone numbers and students reviews.
Apache Spark 2.4 Training Institutes in West Bengal - by Location
Yet5.com Provides complete list of best Apache Spark 2.4 training institutes in West Bengal and training centers with contact address, phone number, training reviews, course fees, job placement, course content, special offers and trainer profile information by area.
Learning Apache Spark 2.4 course in West Bengal - Benefits, Advantages & Placements.
We have identified the benefits of learning apache-spark course in West Bengal. Apache Spark 2.4 training in West Bengal is part of Hadoop training course class, The state is located in the eastern india of bay of Bengal. The fifth most populous city in the country, Calcutta being the capital of the state is the seventh largest city in the world. West Bengal includes darjeeling himalayan hill region, the Ganges delta, the Rarh region, and the coastal Sundarbans.the state gives more importance to education and has a lot of reputed educational institutions in the country. The state has the oldest universities in the country The University of Calcutta, St. Xaviers College, Kolkata, Indian Institute of Foreign Trade, Indian Institute of Management Calcutta, Indian Institute of Science Education and Research, Kolkata, Indian Statistical Institute, Indian Institute of Technology Kharagpur. The city encourages a lot of students by giving a quality education. there are lot of IT and MNCs core industries in the city that enhances and tempt students to educate in the state. The city provides a friendly and cultural environment. The cost of living is affordable. You have travel connectivity to apache-spark course educational training institutes in West Bengal. The city has trams that covers the areas in the state. Kolkata is the headquatress of all the three zones of railways regions and so the station has all the connecting trains to all the metro cities and neighboring states of the country. Netaji Subhas Chandra Bose International Airport at Dum Dum, Kolkata and other domestic airports connects to the states and metro cities. Bus facilities are available across the city and other states through Calcutta State Transport Corporation, the North Bengal State Transport Corporation, the South Bengal State Transport Corporation, the West Bengal Surface Transport Corporation, and the Calcutta Tramways Company.
Apache Spark 2.4 course Content / syllabus in west-bengal
Below is the Apache Spark 2.4 course content in west-bengal used by the training institutes as part of the Apache Spark 2.4 course training. The Apache Spark 2.4 course syllabus covers basic to advanced level course contents which is used by most of Apache Spark 2.4 training classes in west-bengal .
1 SCALA (Object Oriented and Functional Programming)
1.1 Getting started With Scala.
1.2 Scala Background, Scala Vs Java and Basics.
1.3 Interactive Scala – REPL, data types, variables,expressions, simple functions.
1.4 Running the program with Scala Compiler.
1.5 Explore the type lattice and use type inference
1.6 Define Methodsand Pattern Matching.
2 Scala Environment Set up.
2.1 Scala set up on Windows.
2.2 Scala set up on UNIX.
3 Functional Programming.
3.1 What is Functional Programming.
3.2 Differences between OOPS and FPP.
4 Collections (Very Important for Spark)
4.1 Iterating, mapping, filtering and counting
4.2 Regular expressions and matching with them.
4.3 Maps, Sets, group By, Options, flatten, flat Map
4.4 Word count, IO operations,file access, flatMap
5 Object Oriented Programming.
5.1 Classes and Properties.
5.2 Objects, Packaging and Imports.
5.4 Objects, classes, inheritance, Lists with multiple related types, apply
6.1 What is SBT?
6.2 Integration of Scala in Eclipse IDE.
6.3 Integration of SBT with Eclipse.
7 SPARK CORE
7.1 Batch versus real-time data processing
7.2 Introduction to Spark, Spark versus Hadoop
7.3 Architecture of Spark.
7.4 Coding Spark jobs in Scala
7.5 Exploring the Spark shell -> Creating Spark
7.6 RDD Programming
7.7 Operations on RDD.
7.10 Loading Data and Saving Data.
7.11 Key Value Pair RDD.
7.12 Broad cast variables.
8.1 Configuring and running the Spark cluster.
8.2 Exploring to Multi Node Spark Cluster.
8.3 Cluster management
8.4 Submitting Spark jobs and running in the
8.5 Developing Spark applications in Eclipse
8.6 Tuning and Debugging Spark.
9 CASSANDRA (N0SQL DATABASE)
9.1 Learning Cassandra
9.2 Getting started with architecture
9.3 Installing Cassandra.
9.4 Communicating with Cassandra.
9.5 Creating a database.
9.6 Create a table
9.7 Inserting Data
9.8 Modelling Data.
9.9 Creating an Application with Web.
9.10 Updating and Deleting Data.
10 SPARK INTEGRATION WITH NO SQL (CASSANDRA) and AMAZON EC2
10.1 Introduction to Spark and Cassandra
10.2 Spark With Cassandra -> Set up.
10.3 Creating Spark Context to connect the
10.4 Creating Spark RDD on the Cassandra Data
10.5 Performing Transformation and Actions on the
10.6 Running Spark Application in Eclipse to
access the data in the Cassandra.
10.7 Introduction to Amazon Web Services.
10.8 Building 4 Node Spark Multi Node Cluster in
Amazon Web Services.
10.9 Deploying in Production with Mesos and YARN.
11 SPARK STREAMING
11.1 Introduction of Spark Streaming.
11.2 Architecture of Spark Streaming
11.3 Processing Distributed Log Files in Real Time
11.4 Discretized streams RDD.
11.5 Applying Transformations and Actions on
11.6 Integration with Flume and Kafka.
11.7 Integration with Cassandra
11.9 Monitoring streaming jobs.
12 SPARK SQL
12.1 Introduction to Apache Spark SQL
12.2 The SQL context
12.3 Importing and saving data
12.4 Processing the Text files,JSON and Parquet
12.6 user-defined functions
12.7 Using Hive
12.8 Local Hive Metastore server
13 SPARK MLIB.
13.1 Introduction to Machine Learning
13.2 Types of Machine Learning.
13.3 Introduction to Apache Spark MLLib
13.4 Machine Learning Data Types and working with
13.5 Regression and Classification Algorithms.
13.6 Decision Trees in depth.
13.7 Classification with SVM, Naive Bayes
13.8 Clustering with K-Means
13.9 Building the Spark server