Big Data on AWS - Design Scalable Data Solutions

Big Data on AWS

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Big Data on AWS is a thorough course that shows you how to build and implement big data solutions using AWS services. Using platforms like Amazon S3, EMR, Redshift, Kinesis, and Athena, it covers important subjects including data collection, storage, processing, and analysis. You will gain knowledge to improve big data workflows on AWS and learn how to create scalable, secure, and economical data pipelines through practical labs.

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Overview

Big Data on AWS is an in-depth course designed to help professionals leverage AWS services for big data solutions. The course focuses on key aspects of big data workflows, including data collection, ingestion, storage, processing, and analysis. Participants will learn how to design and implement scalable, secure, and cost-effective big data applications tailored to business requirements.

Throughout the course, you will explore essential AWS services such as Amazon S3, EMR, Redshift, Kinesis, Athena, and Glue. These tools are used to collect and store large datasets, process them efficiently, and perform advanced analytics. The course also highlights best practices for handling massive datasets, optimizing workflows, and managing costs effectively.

Hands-on labs and real-world scenarios are integrated into the course to provide practical experience. Participants will build data pipelines, set up streaming data workflows, and analyze data using visualization and reporting tools. Additionally, the course covers security considerations for big data applications, ensuring data integrity and compliance.

By the end of this course, you will be equipped with the knowledge and skills to create robust big data solutions on AWS, making it ideal for data engineers, architects, and analysts looking to enhance their expertise in cloud-based big data processing.

Key Features

  • 1. Comprehensive Coverage: Learn to design and implement scalable big data applications using AWS services.
  • 2. Core AWS Tools: Explore services like Amazon S3, EMR, Redshift, Kinesis, Glue, and Athena for big data workflows.
  • 3. Data Pipelines: Build end-to-end data pipelines for collection, storage, processing, and analysis.
  • 4. Streaming and Batch Processing: Implement real-time data processing with Kinesis and batch workflows with EMR.
  • 5. Advanced Analytics: Use tools like Redshift and Athena for querying and analyzing large datasets.
  • 6. Hands-On Labs: Gain practical experience through hands-on exercises and real-world scenarios.
  • 7. Cost Optimization: Learn best practices to manage and optimize costs for big data applications.
  • 8. Security: Understand how to secure big data solutions and ensure compliance with AWS standards.
  • 9. Scalable Solutions: Design architectures to handle growing data needs efficiently and effectively.

Curriculum

Study Plan

1Module 1: Overview of Big Data
2Module 2: Big Data Ingestion and Transfer
3Module 3: Big Data Streaming and Amazon Kinesis
4Module 4: Big data Storage Solutions
5Module 5: Big Data Processing and Analytics
6Module 6: Apache Hadoop and Amazon EMR
7Module 7: Using Amazon EMR
8Module 8: Hadoop Programming Frameworks
9Module 9: Web Interfaces on Amazon EMR
10Module 10: Apache Spark on Amazon EMR
11Module 11: Using AWS Glue to Automate ETL Workloads
12Module 12: Amazon Redshift and Big Data
13Module 13: Securing your Amazon Deployments
14Module 14: Managing Big Data Costs
15Module 15: Visualizing and Orchestrating Big Data

About Instructor

  • 3000+ Learner Trained
  • 42+ Corporate Recruiting Partners & 20+ College Partners
  • 1000+ Review
  • 200+ Classes/Month
  • aws solution architect certified
  • 3000+ Learner Trained
  • 42+ Corporate Recruiting Partners & 20+ College Partners
  • 1000+ Review
  • 200+ Classes/Month
  • aws solution architect certified
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