Overview
Apache Spark & Scala Certification
The course will enable learners to understand how Spark facilitates in-memory data processing. It helps in NRT (Near Real Time) analytics while running much faster than Hadoop MapReduce. Students will also learn about RDDs, different APIs and components which Spark offers such as Spark Streaming, MLlib, SparkSQL, GraphX.
About Apache Spark & Scala Training
Cognixia’s Apache Spark & Scala Training helps participants develop an understanding of the Spark framework. The training will educate you on in-memory data processing of Spark, which makes it run much faster than Hadoop MapReduce. Spark & Scala Training helps you learn about RDDs and different APIs such as Spark Streaming, MLlib, SparkSQL, and GraphX. Apache Spark & Scala Training proves to be a significant contributor in a developer’s learning curve.
Who is this course for?
The primary beneficiary of this training can be someone who wishes to make a career in big data. someone who wants to be updated with the latest advancements in efficient processing of consistently growing data using Spark-related projects. The following professionals can reap the maximum benefits from this training:
- Big Data Professionals
- Software Engineers and Software Developers
- Data Scientists and Data Analysts
Why should you learn Spark?
Apache Spark and Scala Certification is an integral certification for a developer to have. There is a high requirement of analyzing this data to gain business insights and devise consequential strategies in today’s data is growing world. Cognixia’s Spark and Scala Certification will help you to comprehend the environment and its nuances with respect to several big data processing frameworks such as Hadoop, Spark, Storm, etc. Spark, however, has the capability of working a hundred times faster than Hadoop when it comes to streaming and processing data. This makes it a preferred choice among developers for fast big data analysis.
Curriculum
- What is Scala?
- Why Scala for Spark?
- Scala in Other Frameworks
- Introduction to Scala REPL
- Basic Scala operations
- Variable Types in Scala
- Control Structures in Scala
- Foreach loop, Functions, Procedures, Collections in Scala- Array, ArrayBuffer, Map, Tuples, Lists, and more
- Class in Scala
- Getters and Setters
- Custom Getters and Setters
- Properties with only Getters
- Auxiliary Constructor
- Primary Constructor
- Singletons
- Companion Objects
- Extending a Class
- Overriding Methods
- Traits as Interfaces
- Layered Traits
- Functional Programming
- Higher Order Functions
- Anonymous Functions and more.
- Introduction to Big Data
- Challenges with Big Data
- Batch vs. Real-Time Big Data Analytics
- Batch Analytics – Hadoop Ecosystem Overview
- Real-time Analytics Options
- Streaming Data – Spark
- In-memory Data – Spark
- What is Spark?
- Spark Ecosystem
- Modes of Spark
- Spark Installation Demo
- Overview of Spark on a Cluster
- Spark Standalone Cluster
- Spark Web UI
- Invoking Spark Shell
- Creating the Spark Context
- Loading a file in Shell
- Performing Basic Operations on Files in Spark Shell
- Overview of SBT
- Building a Spark Project with SBT
- Running a Spark Project with SBT
- Local Mode
- Spark Mode
- Caching Overview
- Distributed Persistence
- RDDs
- Transformations in RDD
- Actions in RDD
- Loading Data in RDD
- Saving Data through RDD
- Key-Value Pair RDD
- MapReduce and Pair RDD Operations
- Spark and Hadoop Integration – HDFS
- Spark and Hadoop Integration – Yarn
- Handling Sequence Files
- Partitioner
- Spark Streaming Architecture
- First Spark Streaming Program
- Transformations in Spark Streaming
- Fault Tolerance in Spark Streaming
- Checkpointing
- Parallelism Level
- Machine Learning with Spark
- Data Types
- Algorithms – Statistics
- Classification and Regression
- Clustering
- Collaborative Filtering
- Analyze Hive and Spark SQL Architecture
- SQLContext in Spark SQL
- Working with DataFrames
- Implementing an Example for Spark SQL
- Integrating Hive and Spark SQL
- Support for JSON and Parquet File Formats
- Implement Data Visualization in Spark
- Loading of Data
- Hive Queries through Spark
- Testing Tips in Scala
- Performance Tuning Tips in Spark
- Shared Variables: Broadcast Variables
- Shared Variables: Accumulators
Prerequisites
FAQs
