Data Engineering Training Hyderabad | AWS Data Engineering Training

DURATION
6 Months

MODE OF TRAINING
Online/offline

LEVEL
Advanced

What is AWS Data Engineering?

AWS Data Engineering is a specialized domain focused on designing, building, and managing scalable data pipelines, storage solutions, and data transfer processes on the AWS platform. This course equips learners with the skills to efficiently process, store, and analyze large-scale datasets using AWS tools and services.

Why Choose AWS for Data Engineering?

Scalability:
Scale up or down as per your demand with ease.
Cost-Effectiveness:
Benefit from AWS's pay-as-you-go pricing model, paying only for what you use.
Security & Compliance:
AWS ensures advanced security features and meets regulatory compliance standards.
Real-Time & Batch Processing:
Supports both real-time and batch data processing to meet diverse needs.
Seamless Integration:
AWS services easily integrate with third-party tools to enhance functionality

Data Engineering Course Curriculum

Introduction to Data Engineering +

Overview of Data Engineering: Role, Importance, Implementation
Understanding Data Engineering, Data Analysis and Data Science
Data Engineering for (i) Data Analytics (ii) Machine Learning
Different Types of Data - 5Vs
Data Lifecycle Details: Ingestion, Storage, Processing, Analysis, and Visualization
Landscape of Tools and Technologies
Role/Job Areas of Data Engineering
Modular Case Study: 1
Formative Assessment: 1

Business Data Preparation +

Understanding Business Data and its requirements
KPIs and Metrics
Analysis of Data from different domains like Health Care
Education, Human Resource, Retail Business Chain and FMCG, Media
Hospitality Industry and more
Modular Case Study: 2
Formative Assessment: 2

Implementation Fundamentals: Tools and Technologies +

Python Programming: Programming Essentials
Data Types, Control Structures, Functions
Modules, OO Concepts
Data Operations using Pandas: Data Cleaning
Munging/Wrangling, Manipulation, EDA using Pandas
Working with Different Data Sources and Structures
Programming using Scala: Programming Fundamentals
using Scala 2, Concepts of Parallel Programming using Scala
Modular Case Study: 3
Formative Assessment: 3

Data Storage Technologies +

Relational Databases/Object Relational
Databases and its implementation
NoSQL Databases: Features and Characteristics
Types of NoSQL data models like Key Value
Column-oriented, Document, Graph
Implementation of NoSQL Databases
Data Warehouses: Concepts, Star and Snowflake
Schemas, OLAP, and Data Marts. Implementation and Design
Distributed File Systems: Hadoop HDFS, Google Cloud Storage
Amazon S3. Implementation of Hadoop Cluster and AWS S3
Data Processing using Hive. Implementation of Hive in Hadoop Cluster
Working with open Data Lakehouse with Presto and Apache Iceberg
Scalable Query Handling using Presto
Modular Case Study: 4
Formative Assessment: 4

Data Ingestion Technologies +

Different types of Data Sources and its destinations
Architecture of Data Ingestion Mechanism
Types of Ingestion Process: (i) Batch (ii) Streaming
Data Ingestion Pipleline
Data Ingestion Technologies like Apache Kafka, Flume
Amazon Kinesis and more
Dynamic Pipeline Generation, ETL, Developing, Scheduling
and Monitoring Batch-oriented Workflows, DAGs using Apache Airflow
Working with No-code ETL/ELT Tools
Introduction to the concepts of Data Lakehouse and its implementation
Modular Case Study: 5
Formative Assessment: 5

DataOps +

Understanding DataOps Vs. DevOps
Review of Operating System Concepts
Basic Principles of Containerization
Understanding the concepts of DataOps
Applying CI/CD concepts in Data Pipelines
Introduction to Docker
Dockerfiles, Images, and Containers
Docker Networking
Docker Compose
Orchestration
Introduction to Kubernetes
Running Data Orchestration Tools on Kubernetes
Modular Case Study: 6
Formative Assessment: 6

Big Data Processing +

Essentials of Apache Spark:Spark Architecture
Spark Resilient Distributed Datasets (RDD) and
its operations, Dataframe Basics, Dataframe
Transformation and Execution, Dataframe Joining
Implementation using PySpark/ScalaSpark
Ingesting Data into Spark: Spark SQL, Spark Data
and Stream Processing, Implementation using PySpark/ScalaSpark
Working with Databricks
Working with Ray - Ray with Databricks, Spark and more
No Code/Low Code Big Data Processing Platforms
Introduction to Google Cloud BigQuery, Working with Amazon EMR
Modular Case Study: 7
Formative Assessment: 7

Data Analytics and Visualization +

Introduction to Reports for Data Analysis
Descriptive Analysis and its Reports
Key Performance Indicator (KPI) Dashboards and
Periodic Reports, Diagnostic Analysis and
Detailed Drill Down Reports, Predictive Analysis
and Reports based on Predictive Models, Prescriptive
Analysis and Reports based on AI/ML Models
Implementation of Reports and Dashboards using Apache
Superset, Microsoft Power BI, and Salesforce Tableau
Modular Case Study: 8
Formative Assessment: 8

Cloud Platform and Services +

Introduction to Different Cloud Platforms
AWS, Azure and Google Cloud Architecture
Working with Amazon RedShift, BigQuery
Managed Data Services: AWS Glue, Google
Dataflow, Azure Data Factory
Modular Case Study: 9
Formative Assessment: 9

Basic ML Techniques +

Predictive Modeling: Regression and
Classification Algorithms, Supervised
and Unsupervised Algorithms, Performance
Measures and Metrics
Introduction to Deep Learning, NLP and RL
Introduction to Convolutional Neural Networks
(CNN), RNN and LSTM, Introduction to Natural
Language Processing (NLP) and Toolkit Application
in Natural Language Processing (NLP), Introduction
Introduction to LLMs: Understanding LLM (Large Language Models)
performance, Tools to implement LLMs and GenAI
Implementation of ML/DL Algorithms using Python centric
libraries and Spark ML
Modular Case Study: 10
Formative Assessment: 10

Introduction to ML Automation Process and Pipelines +

Introduction to MLOps
Working with MLOps Platforms
AWS SageMaker, MLFlow, Pipelines
for Model Building, Distributed Model
Training, LLM Training Pipelines
Pipelines for Real-time ML Inference
Pipeline for RAG, RLHF Training Pipeline
for LLMs using Python centric libraries
Modular Case Study: 11
Formative Assessment: 11

Enquiry Form

Only alphabets are allowed.

Email must start with alphabets followed by numbers.