AWS Data Engineering

AWS Data Engineer Training in Hyderabad

AWS Data Engineering refers to the use of Amazon Web Services (AWS) tools and services to design, build, manage, and optimize systems that collect, store, process, and analyze large-scale data. It's a core component in building data pipelines, data lakes, and analytics platforms in the cloud.

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Key Features Of AWS Data Engineer Training In Hyderabad

Why Choose Us

  • Ingesting Data from various sources (databases, APIs)
  • Transforming and Cleaning Data
  • Weekly Test + Weekend Doubt clearing class
  • Comprehensive Curriculum
  • Hands-On Training
  • Expert Gen AI Coaches
  • Free Demo Class
  • 100% Guaranteed Placement Assistance
  • Free Interview preparation Sessions
  • Flexible Learning Modes
  • Project-Based Learning
  • Affordable Fees

Course Content Of AWS Data Engineer Training In Hyderabad

Course Content

Introduction to Data Engineering and AWS

  • What is Data Engineering?

  • Role of Data Engineers

  • Why AWS for Data Engineering?

  • Overview of AWS global infrastructure

  • Setting up AWS account and billing alarms

Core AWS Services Overview

  • Introduction to:

    • Amazon S3 (Simple Storage Service)

    • Amazon EC2 (Elastic Compute Cloud)

    • IAM (Identity and Access Management)

    • Amazon RDS (Relational Database Service)

  • Understanding AWS CLI and SDKs

  • Security best practices and IAM policies

Data Storage and Lake Formation

  • Introduction to Data Lakes

  • Amazon S3: Storage tiers, versioning, lifecycle policies

  • AWS Lake Formation:

    • Building a secure data lake

    • Creating data catalogs and permissions

  • Partitioning and organizing data in S3

Data Ingestion Techniques

  • Batch Ingestion:

    • AWS DataSync

    • AWS Snowball (large-scale data transfers)

  • Streaming Ingestion:

    • Amazon Kinesis Data Streams

    • Amazon Kinesis Firehose

    • Kafka on AWS (MSK)

  • Real-world ingestion pipelines

Data Transformation & ETL

  • Introduction to ETL vs ELT

  • AWS Glue:

    • Glue Jobs (Python, Scala)

    • Glue Crawlers and Data Catalog

    • Glue Studio and Glue Workflows

  • Using AWS Lambda for lightweight transformation

  • Introduction to Amazon EMR (Spark, Hive, Presto)

Data Warehousing

  • Introduction to data warehousing concepts

  • Amazon Redshift:

    • Architecture

    • Loading data from S3

    • Redshift Spectrum (query S3 directly)

    • Performance tuning and optimization

  • Connecting Redshift to BI tools

Querying and Analytics

    • Using Amazon Athena to query data in S3

    • Schema-on-read concepts

    • Creating partitions for faster queries

    • Query optimization techniques

    • Quick integration with Glue Catalog

Orchestration and Workflow Automation

  • Introduction to Data Orchestration

  • AWS Step Functions

  • AWS Glue Workflows

  • Introduction to Amazon MWAA (Managed Apache Airflow)

  • Event-driven ETL pipelines with EventBridge and Lambda

Business Intelligence and Visualization

  • Overview of BI tools on AWS

  • Creating dashboards using Amazon QuickSight

  • Connecting QuickSight to Redshift, Athena, S3

  • Building interactive reports

AWS Data Engineering

AWS Data Engineer Training in Hyderabad

Whether you're a student, a working professional, or a career switcher, our training programs are tailored to your needs. Join us and master data science with one of Hyderabad's top-rated institutes.

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Key AWS Services for Data Engineering

Data engineering is the discipline of designing and building systems for collecting, storing, and analyzing data at scale. The goal is to create data pipelines that clean, transform, and organize raw data into usable formats for analytics and machine learning.

  • Data Ingestion
  • Data Storage
  • Data Transformation & ETL
  • Orchestration & Workflow Management
  • Data Cataloging & Governance
  • Analytics & Machine Learning
  • Building Scalable Pipelines
  • Managing Data Storage (Data Lakes, Warehouses)
  • Scheduling and Orchestration
  • Monitoring and Optimization

Features List

Where is Used AWS Data Engineering ?

  • E-Commerce & Retail Kinesis (real-time tracking), Glue (ETL), S3 (data lake), Redshift (analytics), SageMaker (ML)
  • Healthcare & Life Sciences AWS Glue, S3 (encrypted), Lake Formation, Redshift, SageMaker
  • Financial Services (Banking, Insurance, Fintech) Kinesis (streaming), DynamoDB (fast storage), Athena (SQL queries), QuickSight (dashboards)
  • Media & Entertainment EMR (Spark processing), S3, SageMaker, Glue, QuickSight
  • Telecommunications Kinesis, S3, Redshift, Glue, ML services
  • Education & EdTech Lambda, S3, QuickSight, Redshift, Glue

Skills Required for AWS Data Engineers

  • Proficiency in SQL, Python, and Spark

  • Understanding of cloud architecture and data modeling

  • Experience with ETL, streaming, and data pipeline orchestration

  • Familiarity with DevOps tools (CI/CD, Terraform, CloudFormation)

Our Services

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  • Basic to advanced level
  • Daily recorded videos
  • Live project included
  • Course Material Dumps
  • 100% Placement assistance
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Offline

  • Basic to advanced level
  • Daily recorded videos
  • Live project included
  • Course Material Dumps
  • 100% Placement assistance
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  • Whatsapp Group Access
  • Doubt Clearing Sessions
  • Live project included
  • Course Material Dumps
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