AWS Data Engineering with Data Analytics Online Training in Hyderabad

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AWS Data Engineering with Data Analytics Course
Overview
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• 1. Introduction to AWS Data Engineering
• In this foundational section, students are
  introduced to the core concepts of data
  engineering within the AWS ecosystem. It begins
  with an overview of the role of a data engineer
  and the importance of data engineering in modern
  organizations. The section covers key AWS
  services that are central to data engineering,
  such as Amazon S3, AWS Glue, Amazon Redshift, and
  Amazon RDS. Students will gain an understanding
  of how these services fit into the data
  lifecycle, from ingestion and storage to
  processing and analysis.

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• 2. Data Ingestion and Storage
• This module dives into the various methods and
  services available for ingesting data into AWS.
  Topics include batch ingestion using AWS Data
  Pipeline, real-time ingestion using Amazon
  Kinesis, and file-based ingestion using AWS
  Transfer Family. Students will learn how to
  design and implement data ingestion pipelines
  that are scalable, reliable, and secure. The
  module also covers best practices for data
  storage, focusing on Amazon S3 as the primary
  data lake storage solution. Students will learn
  how to structure and manage data in S3, as well
  as how to optimize it for performance and cost.

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• 3. Data Transformation and Processing
• Once data is ingested and stored, it needs to be
  transformed and processed to be useful for
  analytics. This section covers data
  transformation techniques using AWS Glue and AWS
  Lambda. Students will learn how to use AWS Glue's
  ETL (Extract, Transform, Load) capabilities to
  clean, enrich, and format data. The course also
  explores serverless data processing with AWS
  Lambda, highlighting how it can be used to
  automate and scale data workflows. The use of
  Amazon EMR for big data processing with Hadoop
  and Spark is also covered, providing students
  with hands-on experience in processing large
  datasets.

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• 4. Data Warehousing with Amazon Redshift
• Amazon Redshift is a key component in the AWS
  data engineering toolkit, designed for scalable
  and high-performance data warehousing. This
  module provides an in-depth look at how to
  design, implement, and manage a data warehouse
  using Redshift. Students will learn about data
  modeling, schema design, and query optimization.
  The course will also cover how to load data into
  Redshift from various sources, and how to
  integrate Redshift with other AWS analytics
  services such as Amazon QuickSight for data
  visualization.

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• 5. Data Analytics and Visualization
• Data engineering is closely tied to data
  analytics, and this module focuses on how to
  extract insights from data using AWS services.
  Students will explore Amazon Athena for ad-hoc
  querying of data stored in S3, and how to use
  Amazon QuickSight for creating interactive
  dashboards and visualizations. The course also
  covers machine learning integration, with an
  introduction to Amazon SageMaker for building
  predictive models based on the data processed
  through the engineering pipelines.

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• 6. Security and Compliance
• Security is a critical aspect of data
  engineering, especially when dealing with large
  volumes of sensitive data. This module emphasizes
  best practices for securing data at rest and in
  transit within the AWS environment. Topics
  include IAM roles and policies for fine-grained
  access control, encryption using AWS KMS, and
  setting up VPCs for network security. The course
  also covers compliance frameworks like GDPR and
  HIPAA, ensuring that students understand how to
  build compliant data pipelines.

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• 7. Monitoring and Optimization
• Effective data engineering requires continuous
  monitoring and optimization of data pipelines. In
  this section, students will learn how to use AWS
  CloudWatch for monitoring and alerting on the
  performance of data workflows. The course also
  covers cost management strategies, helping
  students understand how to optimize their use of
  AWS resources to minimize costs while maintaining
  high performance.

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• 8. Capstone Project
• The course culminates in a capstone project where
  students apply what they have learned to build a
  complete data engineering solution on AWS. This
  project involves designing and implementing a
  data pipeline that ingests, processes, and
  analyzes data, followed by creating a final
  report or dashboard. This hands-on experience
  solidifies the students' understanding and
  prepares them for real-world data engineering
  challenges.

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• Conclusion
• The "AWS Data Engineering with Data Analytics"
  course provides a comprehensive guide to
  mastering data engineering on AWS. By covering
  the full data lifecyclefrom ingestion and
  storage to transformation, analysis, and
  visualizationstudents will be equipped with the
  skills needed to build and manage robust data
  solutions in the cloud.

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CONTACT
For More Information About
AWS Data Engineering Online Training
Address Flat no 205, 2nd Floor

Nilagiri Block, Aditya Enclave,

Ameerpet, Hyderabad-16
Ph No 91-9989971070
Visit
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E-Mail online_at_visualpath.in
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