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Best Institutes for Apache Spark 2.4 training in International with Course Fees

List of 1+ Apache Spark 2.4 training institutes located near to you in International as on October 18, 2019. Get access to training curriculum, placement training, course fees, contact phone numbers and students reviews.

 

 

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Apache Spark 2.4 Training Institutes in International - by Location

Yet5.com Provides complete list of best Apache Spark 2.4 training institutes in International 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 International - Benefits, Advantages & Placements.

We have identified the benefits of learning apache-spark course in International.
Apache Spark 2.4 training in International is part of Hadoop training course class,

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Apache Spark 2.4 course Content / syllabus in international

Below is the Apache Spark 2.4 course content in international 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 international .

 

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.
5.3 Traits.
5.4 Objects, classes, inheritance, Lists with multiple related types, apply

6 Integrations
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
Context.
7.6 RDD Programming
7.7 Operations on RDD.
7.8 Transformations
7.9 Actions
7.10 Loading Data and Saving Data.
7.11 Key Value Pair RDD.
7.12 Broad cast variables.

8 Persistence.
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
cluster mode.
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
Connectors.
10.2 Spark With Cassandra -> Set up.
10.3 Creating Spark Context to connect the
Cassandra.
10.4 Creating Spark RDD on the Cassandra Data
base.
10.5 Performing Transformation and Actions on the
Cassandra RDD.
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
Streaming Data
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
Files
12.5 DataFrames
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
Algorithms.
13.4 Machine Learning Data Types and working with
MLLib.
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

 

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