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Big data on AWS (Amazon Web Services) refers to the utilization of AWS services and infrastructure for storing, processing, and analyzing large volumes of data. AWS provides a comprehensive set of tools and services specifically designed to handle big data workloads efficiently and effectively.

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Duration of Training  :  50 hrs

Batch type  :  Weekdays/Weekends

Mode of Training  :  Classroom/Online/Corporate Training


BigData on AWS Training & Certification in Pune

Highly Experienced Trainer with 11+ yrs Exp. in Industry

Realtime Projects, Scenarios & Assignments




Module 1 : Introduction to Big Data on AWS

• Overview of big data concepts and challenges
• Introduction to AWS cloud computing services
• Understanding the AWS big data ecosystem
• Architectural considerations for big data on AWS

Module 2: Data Storage on AWS
• Overview of AWS storage services (S3, EBS, EFS, Glacier)
• Designing data storage solutions for big data workloads
• Data ingestion and data transfer methods
• Data lifecycle management and versioning

Module 3: Data Processing with AWS
• Introduction to AWS compute services (EC2, EMR, Lambda)
• Batch processing with AWS Elastic MapReduce (EMR)
• Real-time processing with AWS Lambda and Kinesis
• Serverless computing for big data workloads

Module 4: Data Warehousing and Analytics on AWS
• Introduction to AWS data warehousing services (Redshift, Athena, Glue)
• Designing and optimizing data warehouse architectures
• Querying and analyzing big data with AWS services
• Integration with business intelligence (BI) tools

Module 5: Streaming and Real-Time Analytics
• Introduction to AWS streaming services (Kinesis, Kafka)
• Real-time data ingestion and processing pipelines
• Real-time analytics with AWS services (Kinesis Analytics, Amazon Managed Streaming
for Apache Kafka)
• Monitoring and scaling real-time analytics solutions

Module 6: Big Data Orchestration and Workflow
• Introduction to AWS orchestration services (Step Functions, Data Pipeline)
• Designing and managing big data workflows on AWS
• Automating data pipelines and ETL processes
• Error handling and fault tolerance in data workflows

Module 7: Data Governance and Security
• Understanding data governance challenges in big data
• Data security and compliance considerations on AWS
• Identity and access management (IAM) for big data workloads
• Encryption and data protection mechanisms on AWS

Module 8: Data Visualization and Reporting
• Overview of data visualization tools and services
• Integrating AWS big data solutions with visualization tools (QuickSight, Tableau)
• Designing interactive dashboards and reports
• Data storytelling and effective visualization practices

Module 9: Big Data Cost Optimization and Performance
• Cost optimization strategies for big data workloads on AWS
• Selecting the right AWS services based on cost and performance requirements
• Monitoring and optimizing resource utilization
• Scalability and performance tuning techniques

Module 10: Advanced Topics and Emerging Trends
• Advanced analytics with AWS machine learning services (SageMaker, Comprehend,
• Big data processing with AWS serverless technologies (Glue, Athena, Lambda)
• Exploring emerging trends in big data and AWS services
• Industry use cases and best practices

Big data on AWS (Amazon Web Services) refers to the utilization of AWS services and infrastructure for storing, processing, and analyzing large volumes of data. AWS provides a comprehensive set of tools and services specifically designed to handle big data workloads efficiently and effectively. Some of the key services offered by AWS for big data include:

  1. Amazon S3 (Simple Storage Service): AWS S3 is a highly scalable object storage service that allows you to store and retrieve large amounts of unstructured data. It is often used as a data lake to store raw data before processing.
  2. Amazon EMR (Elastic MapReduce): EMR is a managed big data processing service that enables you to run distributed frameworks such as Apache Hadoop, Spark, and Presto on AWS. It simplifies the deployment and management of these frameworks and enables processing of large datasets in a scalable manner.
  3. Amazon Redshift: Redshift is a fully managed data warehousing service that provides high-performance analytics for large-scale data sets. It is optimized for online analytical processing (OLAP) workloads and allows you to query and analyze data using SQL.
  4. AWS Glue: Glue is a fully managed extract, transform, and load (ETL) service that helps you prepare and transform your data for analytics. It automatically generates ETL code and provides a serverless environment for data preparation tasks.
  5. AWS Athena: Athena is an interactive query service that allows you to analyze data directly from Amazon S3 using standard SQL queries. It eliminates the need to set up and manage infrastructure and enables ad-hoc querying of large datasets.
  6. Amazon Kinesis: Kinesis is a platform for real-time streaming data processing. It allows you to ingest, process, and analyze streaming data at any scale. Kinesis offers multiple services like Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics for different streaming use cases.
  7. AWS Lambda: Lambda is a serverless computing service that allows you to run code without provisioning or managing servers. It can be used for data processing and integration tasks, such as transforming and enriching data as it flows through various AWS services.
  8. AWS Data Pipeline: Data Pipeline is a web service for orchestrating and automating the movement and transformation of data between different AWS services and on-premises data sources. It simplifies the creation, scheduling, and management of data workflows.

These are just a few examples of the many AWS services available for handling big data. AWS provides a scalable and flexible platform for storing, processing, and analyzing large datasets, allowing organizations to leverage the power of big data for various purposes, including business intelligence, machine learning, and predictive analytics

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