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About SPARK Online Training:
Spark Online Training may be a unique framework for giant data analytics which provides one unique integrated API by developers for the aim of knowledge scientists and analysts to perform separate tasks. It supports a good range of popular languages like Python, R, SQL, Java and Scala. Apache Spark main aim is to supply hands-on experience to make real-time Data Stream Analysis and large-scale learning solutions for data scientists, data analysts and software developers.
Prerequisites:
- Basic knowledge of object-oriented programming is enough Knowledge of Scala will be an added advantage
- Learners who have basic knowledge on Database, SQL Query will be an added advantage for Learning this Course
Terms AND Conditions
- We will Provide Supporting to resolve Student practical Issues.
- We will provide server Access and 100% Lab Facility.
- Resume Preparation.
- Interview Questions & Answers.
- We will conduct mock interviews. Student also gets 100% supporting before and after getting job.
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APACHE SPARK ONLINE TRAINING COURSE CONTENT
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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 Methods and Pattern Matching
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Scala Environment Set Up
Scala set up on Windows and UNIX
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Functional Programming
- What is Functional Programming?
- Differences between OOPS and FPP
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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
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Object-Oriented Programming
- Classes and Properties
- Objects, Packaging, and Imports
- Traits
- Objects, classes, inheritance, Lists with multiple related types, apply
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Integrations
- What is SBT?
- Integration of Scala in Eclipse IDE
- Integration of SBT with Eclipse
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SPARK CORE
- Batch versus real-time data processing
- Introduction to Spark, Spark versus Hadoop
- The architecture of Spark
- Coding Spark jobs in Scala
- Exploring the Spark shell to Creating Spark Context
- RDD Programming
- Operations on RDD
- Transformations
- Actions
- Loading Data and Saving Data
- Key Value Pair RDD
- Broadcast variables
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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
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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
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Spark Integration With NoSQL (CASSANDRA) And Amazon EC2
- Introduction to Spark and Cassandra Connectors
- Spark With Cassandra to Set up
- Creating Spark Context to connect the Cassandra
- Creating Spark RDD on the Cassandra Database
- 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
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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
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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
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Spark MLLib
- 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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