Home  >   Courses  > Popular Courses  > AWS data engineering and data analytics Course

AWS data engineering in

Currently, there is a very high demand in the market for qualified professionals in AWS data engineering because of the rapidly changing technological environment. Our complete AWS Data Engineer training in Hyderabad helps you gain the necessary know-how for a competitive edge in today’s fast-paced field.

21 Modules

with Certifications


After Completion



Why do we call it AWS data engineering?

The profession of skilled data handling and processing is known as Data Engineering for AWS. Such a field is crucial to organizations that would like to make sense out of collected information, conserve space for storage, and employ a data-driven approach. With more companies moving their operations to the cloud platforms, it is imperative that the work of AWS data engineers be effective for streamlined managing and utilizing of the data.  Industry professionals design AWS Data Engineering training in Hyderabad. The participants are taught basics of AWS services such as provisioning of database functionality, storage and data analysis comprising of AWS glue, Amazon Redshift, and ML tool integration respectively. The theory is integrated with practice through practical projects and case studies that prepare learners to professionally integrate.

Why should you attend Version IT AWS data engineer training in Hyderabad?

The modern-day AWS Data Engineers’ course is offered by Hyderabad’s IT Version at a Center that offers quality, industry-relevant material and a networked platform for knowledge sharing. Participation in this course equips individuals with the skills they need as competent professionals who will address data-oriented challenges hence leading to career promotion or new paths in data engineering.

Topics You will Learn

  • The rise of big data as a corporate asset
  • The challenges of ever-growing datasets
  • Data engineers – the big data enablers
  • Understanding the role of the data engineer
  • Understanding the role of the data scientist
  • Understanding the role of the data analyst
  • Understanding other common data-related roles
  • The benefits of the cloud when building big data analytic solutions
  • The evolution of data management for analytics
  • Databases and data warehouses
  • Dealing with big, unstructured data
  • A lake on the cloud and a house on that lake
  • Understanding data warehouses and data marts –fountains of truth
  • Distributed storage and massively parallel processing
  • Columnar data storage and efficient data compression
  • Dimensional modeling in data warehouses
  • Understanding the role of data marts
  • Feeding data into the warehouse – ETL and ELT pipelines
  • Building data lakes to tame the variety and volume of big data.
  • Data lake logical architecture
  • Bringing together the best of both worlds with the lake house architecture
  • Data lakehouse implementations
  • Building a data lakehouse on AWS
  • Hands-on – configuring the AWS.
  • Command Line Interface tool and creating an S3 bucket.
  • Installing and configuring the AWS CLI
  • Creating a new Amazon S3 bucket
  • Overview of Amazon Database Migration Service (DMS)
  • Overview of Amazon Kinesis for streaming data ingestion
  • Overview of Amazon MSK for streaming data ingestion
  • Overview of Amazon AppFlow for ingesting data from SaaS services
  • Overview of Amazon Transfer Family for ingestion using FTP/SFTP protocols
  • Overview of Amazon DataSync for ingesting from on-premises storage
  • Overview of the AWS Snow family of devices for large data transfers
  • Overview of AWS Lambda for light transformations
  • Overview of AWS Glue for serverless Spark processing
  • Overview of Amazon EMR for Hadoop ecosystem processing
  • Overview of AWS Glue workflows for orchestrating Glue components
  • Overview of AWS Step Functions for complex workflows
  • Overview of Amazon managed workflows for Apache Airflow
  • Overview of Amazon Athena for SQL queries in the data lake
  • Overview of Amazon Redshift and Redshift Spectrum for data warehousing and data lakehouse architectures
  • Overview of Amazon Quick Sight for visualizing data
    • Understanding data sources  
    • Data variety
    • Data volume
    • Data velocity
    • Data veracity
    • Data value
    • Questions to ask.
    • Ingesting data from a relational database
    • AWS Database Migration Service (DMS)
    • AWS Glue
    • Other ways to ingest data from a database.


  • Creating a Lambda layer containing the AWS Data Wrangler library
  • Creating new Amazon S3 buckets
  • Creating an IAM policy and role for your Lambda function
  • Creating a Lambda function
  • Configuring our Lambda function to be triggered by an S3 upload
  • Getting data security and governance right
  • Common data regulatory requirements
  • Core data protection concepts
  • Personal data
  • Encryption
  • Anonymized data
  • Pseudonymized data/tokenization
  • Authentication
  • Authorization
  • How to avoid the data swamp
    • Amazon Kinesis versus Amazon
    • Managed Streaming for Kafka (MSK)
    • Hands-on – ingesting data with AWS DMS
    • Creating a new MySQL database instance
    • Loading the demo data using an Amazon EC2 instance
    • Creating an IAM policy and role for DMS
    • Configuring DMS settings and performing a full load from MySQL to S3
    • Querying data with Amazon Athena
    • Hands-on – ingesting streaming data
    • Configuring Kinesis Data Firehose for streaming delivery to Amazon S3
    • Configuring Amazon Kinesis Data Generator (KDG)
    • Adding newly ingested data to the Glue Data Catalog
    • Querying the data with Amazon Athena
  • Understanding the impact of data democratization
  • A growing variety of data consumers  
  • Meeting the needs of business users with data visualization
  • AWS tools for business users  
  • Meeting the needs of data analysts with structured reporting  
  • AWS tools for data analysts  
  • Meeting the needs of data scientists and ML models  
  • AWS tools used by data scientists to work with data.
  • Hands-on – creating data transformations with AWS Glue DataBrew
  • Configuring new datasets for AWS Glue DataBrew  
  • Creating a new Glue DataBrew project 2
  • Building your Glue DataBrew recipe
  • Creating a Glue DataBrew job
  • What is a data pipeline, and how do you orchestrate it?
    • How do you trigger a data pipeline to run?
    • How do you handle the failures of a step in your pipeline?
    • Examining the options for orchestrating pipelines in AWS
    • AWS Data Pipeline for managing ETL between data sources.
    • AWS Glue Workflows to orchestrate Glue resources.
    • Apache Airflow as an open-source orchestration solution
    • Pros and cons of using MWAA.
    • AWS Step Function for a serverless orchestration solution
    • Pros and cons of using AWS Step Function
    • Deciding on which data pipeline orchestration tool to use
    • Hands-on – orchestrating a data pipeline using AWS Step Function
    • Creating new Lambda functions
  • Amazon Athena – in-place SQL analytics for the data lake
  • Tips and tricks to optimize Amazon Athena queries.
    • Common file format and layout optimizations
    • Writing optimized SQL queries
    • Federating the queries of external data sources with Amazon Athena Query Federation
    • Querying external data sources using Athena Federated Query
    • Managing governance and costs with Amazon Athena Workgroups
    • Athena Workgroups overview
    • Enforcing settings for groups of users
    • Enforcing data usage controls
    • Hands-on – creating an Amazon Athena workgroup and configuring Athena settings.
    • Hands-on – switching Workgroups and running queries.
  • IAM roles for Redshift
  • Creating a Redshift cluster
  • Creating external tables for querying data in S3
  • Creating a schema for a local Redshift table
  • Running complex SQL queries against our data

Representing data visually  for maximum impact

  • Benefits of data visualization
  • Popular uses of data visualizations

Understanding Amazon Quick Sight’s core concepts

  • Standard versus enterprise edition
  • SPICE – the in-memory storage and computation engine for Quick Sight

Ingesting and preparing data from a variety of sources

  • Preparing datasets in Quick Sight versus performing ETL outside of Quick Sight

Creating and sharing visuals with Quick Sight analyses and dashboards

  • Visual types in Amazon Quick Sight
  • AWS Key Management Service (KMS)
  • Amazon Macie
  • Amazon GuardDuty
  • Pass
  • Return
  • Case studies
  • AWS Identity and Access Management (IAM) service
  • Using AWS Lake Formation to manage data lake access.
  • Creating a new user with IAM permissions
  • Transitioning to managing fine-grained permissions with AWS Lake Formation
  • Creating a new user with IAM permissions
  • Transitioning to managing fine-grained permissions with AWS Lake Formation
  • Extending analytics with data warehouses/data marts
  • Cold data
  • Warm data
  • Hot data
  • What not to do – anti-patterns for a data warehouse
  • Using a data warehouse as a transactional datastore
  • Using a data warehouse as a data lake
  • Using data warehouses for real-time, record-level use cases
  • Storing unstructured data
  • Data distribution across slices
  • Redshift Zone Maps and sorting data
  • Designing a high-performance data warehouse
  • Selecting the optimal Redshift node type
  • Selecting the optimal table distribution style and sort key
  • Selecting the right data type for columns
  • Selecting the optimal table type
  • Moving data between a data lake and Redshift
  • Optimizing data ingestion in Redshift
  • Exporting data from Redshift to the data lake
  • Hands-on – loading data into an Amazon Redshift cluster and running queries Uploading our sample data to Amazon S3
  • IAM roles for Redshift
  • Creating a Redshift cluster
  • Creating external tables for querying data in S3
  • Creating a schema for a local Redshift table
  • Running complex SQL queries against our data

Let Your Certificates Speak


All You Need to Start this Course


Still Having Doubts?

An AWS Data Engineer is in charge of developing, deploying, and maintaining AWS data processing systems and infrastructure. Data ingestion, storage, transformation, and analysis are examples of such jobs.

AWS offers a variety of data engineering services, including Amazon S3 (Simple Storage Service), Amazon Redshift (data warehouse), AWS Glue (ETL service), and Amazon EMR (Elastic MapReduce).

Amazon S3 is a scalable object storage service used to store and retrieve massive volumes of data. S3 is frequently used by data engineers as a data lake for storing raw and processed data.

An entirely managed extract, transform, and load (ETL) service is AWS Glue. Data engineers use ETL operations to automate the process of preparing and converting data for analysis using Amazon Glue.

Get in Touch with Us

Quick Contact
close slider
Please enable JavaScript in your browser to complete this form.
Scroll to Top