- 1 About the Apache Spark Training
- 2 BATCH AND REAL TIME ANALYTICS WITH APACHE SPARK.
- 2.1 SCALA (Object Oriented and Functional Programming)
- 2.2 Scala Environment Set up.
- 2.3 Functional Programming.
- 2.4 Collections (Very Important for Spark)
- 2.5 Object Oriented Programming.
- 2.6 Integrations
- 2.7 SPARK CORE.
- 2.8 Persistence.
- 2.9 CASSANDRA (N0SQL DATABASE)
- 2.10 SPARK INTEGRATION WITH NO SQL (CASSANDRA) and AMAZON EC2
- 2.11 SPARK STREAMING
- 2.12 SPARK SQL
- 2.13 SPARK MLIB.
- 2.14 Share this:
About the Apache Spark Training
Spark is a unique framework from big data analytics which gives one unique integrated API by developers for the purpose of data scientists and analysts to perform separate tasks. It supports a wide range of popular languages like Python, R, SQL, Java and Scala.
Apache Spark main aim is to provide hands-on experience to create real-time Data Stream Analysis and large scale learning solutions for data scientists, data analysts and software developers.
Objectives of the Course
- Apache Spark Architecture How to use Spark with Scala How to deploy Spark projects to the cloud Machine Learning with Spark
- Basic knowledge of object-oriented programming is enough Knowledge of Scala will be a added advantage
Who should do the course:
- Developers, Architects, IT Professionals.
- Software Engineers, Data scientists and Analysts.
BATCH AND REAL TIME ANALYTICS WITH APACHE SPARK.
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.
- 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.
- Objects, classes, inheritance, Lists with multiple related types, apply
- What is SBT?
- Integration of Scala in Eclipse IDE.
- Integration of SBT with Eclipse.
- 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.
- Loading Data and Saving Data.
- Key Value Pair RDD.
- Broad cast variables.
- 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.
- 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.
- Introduction to Apache Spark SQL
- The SQL context
- Importing and saving data
- Processing the Text files,JSON and Parquet Files
- user-defined functions
- Using Hive
- Local Hive Metastore server
- 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
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