Apache Spark

About Apache Spark

An open-source unified analytics engine for analyzing enormous amounts of data is Apache Spark. This allows clusters to be programmed with implicit data parallelism and fault tolerance. On single-node workstations or clusters, this multi-language engine may be used to execute data engineering, data science, and machine learning operations.

Apache Spark analytics helps companies quicken their decision making and target more customers. Apache Spark has gained immense popularity across the globe resulting in huge demand for certified professionals.

Why is Apache Spark important?

The benefits of Apache Spark are:

• Use Python, SQL, Scala, Java, or R to combine the processing of your data in batches and in real-time streaming.
• Run quick, distributed ANSI SQL queries for ad-hoc reporting and dashboarding. quicker than the majority of data warehouses.
• Without using downsampling, do exploratory data analysis (EDA) on petabyte-scale data.
• The same code can be used to scale to fault-tolerant clusters of thousands of machines after training machine learning algorithms on a laptop.
• Open source software makes analytics more accessible.
  

Apache Spark certified professionals, executives and managers are in high demand in companies across the globe.

Who should take the Apache Spark Exam?

• Big data developers and engineers
  
• Software engineers looking to improve their Big Data competencies.
• ETL developers and data engineers.
• Professionals in data analytics and data science.
• Recent graduates who want to work with big data.

Knowledge and Skills required for the Apache Spark

Specific skills are needed to excel in career of Apache Spark which includes analytical bent of mind and quick learning skills.

Apache Spark Practice Exam Objectives

Apache Spark exam focuses on assessing your skills and knowledge in concepts and application of Apache Spark

Apache Spark Practice Exam Pre-requisite

There are no prerequisites for the Apache Spark exam. Candidates who are well versed  in Apache Spark can easily clear the exam.

Apache Spark Certification Course Outline

Domain 1 - Introduction to Apache Spark

• Overview of Big Data and the Need for Spark
• Evolution of Apache Spark
• Spark Ecosystem and Components
• Comparing Spark with Hadoop MapReduce
• Spark Architecture and Cluster Modes
• Use Cases of Apache Spark

 

Domain 2 - Spark Installation and Environment Setup

• Installing Apache Spark
• Setting up Spark on Local Machine
• Configuring Spark with Cluster Managers
• Introduction to Spark Shell
• Understanding Resilient Distributed Datasets (RDDs) and DAGs

 

Domain 3 - Working with Resilient Distributed Datasets (RDDs)

• RDD Basics and Properties
• Transformations and Actions in RDDs
• Lazy Evaluation and Lineage
• Key-Value Pair Operations
• Persistence and Caching in Spark
• RDD Fault Tolerance and Recovery

 

Domain 4 - Spark SQL and DataFrames

• Introduction to Spark SQL
• Working with DataFrames
• Performing Operations on DataFrames
• Querying DataFrames using SQL
• Integrating Spark with Databases
• Spark SQL Performance Optimization

 

Domain 5 - Spark Streaming

• Basics of Spark Streaming
• Streaming Architecture and DStreams
• Transformations on DStreams
• Windowed Operations in Streaming
• Fault Tolerance and Checkpointing in Spark Streaming
• Integrating Spark Streaming with Apache Kafka
• Real-Time Data Processing with Examples

 

Domain 6 - Machine Learning with Spark MLlib

• Introduction to Spark MLlib
• Key Concepts in Spark MLlib
• Implementing ML Algorithms:
• Feature Engineering and Dimensionality Reduction
• Model Evaluation and Tuning in MLlib

 

Domain 7 - Graph Processing with GraphX

• Basics of GraphX
• Graph Representation in Spark
• Graph Operations:
• Implementing Graph Algorithms:
• Real-Life Applications of GraphX

 

Domain 8 - Advanced Apache Spark

• Spark Performance Tuning and Optimization
• Working with Broadcast Variables and Accumulators
• Debugging and Monitoring Spark Applications
• Deploying Spark Applications
• Handling Large-Scale Data with Spark

 

Domain 9 - Integrating Spark with Big Data Tools

• Integrating Spark with Hadoop (HDFS, YARN)
• Spark with Hive and HBase
• Apache Kafka Integration
• Working with NoSQL Databases (e.g., Cassandra, MongoDB)

Exam Format and Information

Certification name – Certificate in Apache Spark
Exam duration – 60 minutes
Exam type - Multiple Choice Questions
Eligibility / pre-requisite - None
Exam language - English
Exam format - Online
Passing score - 25
Exam Fees  - INR 1199