The Building Batch Data Analytics Solutions on AWS course is designed to equip learners with the practical skills necessary to design, implement, and optimize batch data analytics pipelines using key AWS services. Focused on batch processing, the course emphasizes the use of Amazon EMR, Apache Spark, Apache Hive, HBase, and serverless services like AWS Glue and AWS Step Functions.

This course enables professionals to build scalable, cost-effective, and secure analytics solutions to support enterprise-level data initiatives. Learners will gain hands-on experience with real-world data processing tools and explore architectural strategies for building robust data analytics workflows on AWS.

The course covers key areas such as:

• Introduction to Data Analytics Use Cases: Understand the importance of batch data processing in modern analytics solutions and how AWS supports scalable data pipelines.
• Amazon EMR Essentials: Learn cluster architecture, provisioning, and cost management strategies. Explore how to launch and manage EMR clusters using best practices.
• Optimizing Data Storage and Ingestion: Dive into storage optimization techniques and ingestion pipelines that feed into EMR clusters for high-performance analytics.
• High-Performance Analytics with Apache Spark: Master data transformation and analytics using Apache Spark on Amazon EMR. Includes hands-on labs using Spark Shell and Scala.
• Batch Data Processing with Hive and HBase: Leverage Apache Hive and HBase on EMR to process structured and semi-structured data efficiently.
• Serverless Data Processing and Orchestration: Use AWS Glue for ETL tasks and AWS Step Functions to automate and orchestrate batch workflows without managing servers.
• Security, Monitoring, and Troubleshooting: Implement client-side encryption, monitor performance, troubleshoot cluster issues, and track EMR history for compliance.
• Modern Data Architectures on AWS: Learn to design comprehensive and scalable data architectures using AWS analytics services to support end-to-end data processing solutions.

Course Prerequisites

To ensure success in this course, participants should meet the following prerequisites:

• Familiarity with core AWS services (Amazon S3, EC2, IAM)
• Basic knowledge of data analytics or ETL concepts
• Understanding of distributed computing frameworks (e.g., Hadoop, Spark)
• Experience with scripting languages (Python or Shell)
• Exposure to big data processing tools such as Hive, HBase, or Spark (recommended)
• Comfort using command-line interfaces and data analysis tools

These prerequisites ensure learners can effectively work with AWS services and tools used throughout the course.

Target Audience

The Building Batch Data Analytics Solutions on AWS course is ideal for data and cloud professionals looking to deepen their knowledge of big data processing on AWS, including:

• Data Engineers
• Data Scientists
• Data Analysts
• Cloud Computing Specialists
• Business Intelligence Professionals
• Solutions Architects
• DevOps Engineers deploying data pipelines
• System Administrators expanding into big data domains
• Software Developers integrating data pipelines
• IT Managers and Technical Leads overseeing data operations
• AWS Certified Professionals enhancing data analytics skills

Why Choose us

⭢ Live Online Training (Duration : 8 Hours)

⭢ Guaranteed to run classes

⭢ Experienced & certified trainers

⭢ Query Handling session

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Learning Objectives

After completing the Building Batch Data Analytics Solutions on AWS course, learners will be able to:

• Identify data analytics use cases and design appropriate data pipelines
• Launch, configure, and manage Amazon EMR clusters for batch processing
• Optimize data storage and ingestion strategies for performance and cost-efficiency
• Perform large-scale data analytics using Apache Spark, Hive, and HBase
• Utilize notebooks and command-line tools to interact with EMR clusters
• Implement serverless ETL workflows using AWS Glue and orchestrate pipelines with AWS Step Functions
• Secure data analytics environments using encryption and access control
• Monitor and troubleshoot EMR clusters using native AWS tools
• Design modern batch analytics architectures using a combination of AWS services
• Apply batch analytics skills to real-world projects across various industries