Best Institutes for Apache Spark 2.4 training in Karnataka with Course Fees
List of 1+ Apache Spark 2.4 training institutes located near to you in Karnataka as on October 19, 2019. Get access to training curriculum, placement training, course fees, contact phone numbers and students reviews.
Address: Door No 22, Siddarameshwara Marga, 2nd Cross, Kalyan Nagar(Bypass Road) Chikmagalur Karnataka-577102
Apache Spark 2.4 Training Institutes in Karnataka - by Location
Yet5.com Provides complete list of best Apache Spark 2.4 training institutes in Karnataka and training centers with contact address, phone number, training reviews, course fees, job placement, course content, special offers and trainer profile information by area.
We have identified the benefits of learning apache-spark course in Karnataka. Apache Spark 2.4 training in Karnataka is part of Hadoop training course class, Karnataka is located in the south western region of India with Bangalore as its capital. Karnataka has a literacy rate of 75.60%. Karnataka is home to some of the premier educational and research institutions of India such as the Indian Institute of Science, the IIM, the Indian Institute of Technology Dharwad the National Institute of Mental Health and Neurosciences, the NIT Karnataka and the National Law School of India University. Also, it is home to the famous universities such as Bangalore University, Gulbarga University, Karnatak University, Kuvempu University, Mangalore University and Mysore University with 481 degree colleges that are affiliated to one of these universities. There are 186 engineering, 39 medical and 41 dental colleges in Karnataka. As Bangalore is the Silicon Valley of India, a large number of IT companies such as Cognizant, Infosys, Wipro, TCS Accenture, HCL, Capgemini etc are located here that provide immense job opportunities to students all over India. You have travel connectivity to apache-spark course educational training institutes in Karnataka. Karnataka has airports at Bengaluru, Mangalore, Belgaum, Hubli, Hampi, Bellary and Mysore for domestic flights, and international flights from Bangalore and Mangalore airports. Karnataka has an excellent Railway transportation system that covers all the cities of Karnataka and other states of India. Karnataka offers 1 major port and 10 minor ports as a part of its water transportation. KSRTC manages the public buses in Karnataka. Omni bus, Taxi cabs, autos and rickshaws are other common modes of road transport for inter city and intra city commuting.
Apache Spark 2.4 course Content / syllabus in karnataka
Below is the Apache Spark 2.4 course content in karnataka 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 karnataka .
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