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+91-834 161 1955 Mon-Fri 9am - 6pm
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Data Engineering Training Key Features

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Practical Data Pipeline Labs

Get hands-on experience building and managing robust data pipelines, ETL processes, and data warehousing solutions on cloud platforms.

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Flexible Online and In-Person Classes

Learn at your convenience through our classroom sessions at Ameerpet or Kukatpally, or join live interactive online classes from anywhere in the world.

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Dedicated Data Engineering Mentorship

Receive personalized assistance for all your data engineering projects and complex infrastructure queries from our experienced instructors during and after your course.

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Robust Career & Placement Guidance

We help you prepare for data engineering interviews with mock sessions, resume optimization, and direct connections to job opportunities in leading tech companies.

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Real-World Data Platform Projects

Gain invaluable experience by developing end-to-end data ingestion, processing, and storage solutions for enterprise-level data using modern big data technologies.

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Engaging Learning Community

Collaborate with a supportive community of peers and instructors, fostering enhanced data engineering skills, knowledge sharing, and valuable networking opportunities.

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Data Engineering Training Overview

Value Learning offers comprehensive Data Engineering training courses at both Ameerpet and Kukatpally (KPHB), Hyderabad. Our programs are meticulously designed to equip you with the practical skills needed to design, build, and manage scalable and reliable data infrastructures.

Data Engineering focuses on the critical infrastructure, tools, and processes required to collect, store, and transform large and complex datasets into usable formats for analysis, reporting, and machine learning. This field covers essential areas like ETL (Extract, Transform, Load) processes, data warehousing, big data technologies (e.g., Hadoop, Spark), cloud data services (AWS, Azure, GCP), and advanced database management. Our expert-led training ensures you grasp both the theoretical foundations and hands-on implementation of robust data solutions that power modern data-driven organizations.

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Successful Learners

68k

Training Hours Delivered

540

Enterprise Projects Covered

Data Engineering Training Objectives

The Data Engineering course at Value Learning, delivered at our Ameerpet and Kukatpally (KPHB) centers in Hyderabad, is designed to give learners a robust understanding of data architecture, pipeline development, and big data technologies essential for modern data platforms.

Through this training, you will gain hands-on experience with designing and implementing robust ETL processes, data warehousing solutions, and scalable data lakes. You'll achieve proficiency in the tools and platforms required for efficient data ingestion, processing, and storage across various environments, including cloud services.

The primary goal of the training is to empower learners to build efficient, reliable, and scalable data infrastructure that effectively supports advanced analytics, business intelligence, and machine learning initiatives within any organization.

To equip learners with comprehensive, practical experience in end-to-end data platform development, from selecting appropriate technologies and designing data models to deploying and optimizing solutions for performance and maintainability, preparing them for highly specialized roles in data engineering.

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Course Curriculum - Data Engineering

Overview:
  • What is Data Engineering? Role, Responsibilities, and Skillset
  • Understanding the Data Ecosystem: Data Sources, Pipelines, Storage, Analytics
  • Importance of Data Engineering in the Age of Big Data
  • Comparison: Data Engineer vs. Data Scientist vs. Data Analyst
  • Overview of the Modern Data Stack and its Components

  • Database Fundamentals: Relational Model, Tables, Keys, Relationships
  • **Advanced SQL**: Joins, Subqueries, CTEs, Window Functions
  • Database Normalization (1NF, 2NF, 3NF) and Denormalization
  • Indexing for Performance Optimization
  • Working with Popular RDBMS (PostgreSQL, MySQL, SQL Server)

  • Introduction to NoSQL: Use Cases and Trade-offs
  • Key-Value Stores (e.g., Redis): Concepts and Applications
  • Document Databases (e.g., MongoDB): Flexible Schemas and Querying
  • Column-Family Databases (e.g., Cassandra, HBase): Scalability for Wide Columns
  • Graph Databases (e.g., Neo4j - overview): Modeling Relationships

  • **Python Fundamentals**: Data Structures, Control Flow, Functions
  • Working with Files (CSV, JSON, XML) and APIs
  • **Pandas** for Data Manipulation and Cleaning
  • Error Handling and Logging in Python
  • Introduction to Object-Oriented Programming for Data Pipelines

  • Introduction to Data Warehousing: Goals and Architecture
  • Dimensional Modeling: Fact Tables and Dimension Tables
  • Star Schema and Snowflake Schema Design
  • Slowly Changing Dimensions (SCD Type 1, 2, 3)
  • ETL (Extract, Transform, Load) vs. ELT (Extract, Load, Transform) Paradigms

  • **HDFS**: Distributed Storage, Fault Tolerance
  • **MapReduce**: Fundamentals for Distributed Processing (Conceptual)
  • **Apache Spark**: Architecture, RDDs, DataFrames, Spark SQL
  • Spark Ecosystem: Spark Streaming, MLlib (overview)
  • Optimizing Spark Jobs for Performance

  • Batch Data Ingestion: **Apache Sqoop** for RDBMS Integration
  • Real-time Data Ingestion: **Apache Flume** for Log Data
  • **Apache Kafka**: Distributed Streaming Platform (Producers, Consumers, Topics)
  • Kafka Connect for Integrating with Databases and Data Lakes
  • Stream Processing with Spark Streaming or Kafka Streams

  • Introduction to Workflow Orchestration
  • **Apache Airflow**: DAGs, Operators, Sensors, Schedulers
  • Building and Monitoring Complex Data Pipelines with Airflow
  • Error Handling and Retries in Data Workflows
  • Alternatives: Luigi, Dagster (overview)

  • Understanding Data Lakes: Raw Data Storage and Schema-on-Read
  • Building Data Lakes on Cloud Storage (S3, ADLS, GCS)
  • Introduction to Lakehouse Architecture: Combining Best of Lakes and Warehouses
  • **Delta Lake, Apache Iceberg, Apache Hudi** (overview and features)
  • Data Governance, Security, and Metadata Management in Data Lakes

  • Overview of Cloud Data Services (e.g., AWS Glue, Azure Data Factory, GCP Dataflow)
  • Cloud Data Warehouses (e.g., **Amazon Redshift, Azure Synapse, Google BigQuery**)
  • Cloud-native Object Storage (S3, ADLS Gen2, GCS)
  • Managed Spark Services (AWS EMR, Azure Databricks, GCP Dataproc)
  • Cost Management and Optimization in Cloud Data Platforms

  • Importance of Data Quality in Data Pipelines
  • Data Validation, Cleansing, and Profiling Techniques
  • **Data Governance**: Policies, Standards, and Procedures
  • Data Security: Encryption, Access Control, Compliance (GDPR, HIPAA - overview)
  • Data Lineage and Metadata Management

  • Introduction to **Docker**: Containers, Images, Dockerfile
  • Containerizing Data Engineering Applications
  • **Kubernetes** Basics: Pods, Deployments, Services (Conceptual)
  • Deploying Data Workflows on Kubernetes Clusters
  • Benefits for Scalability and Portability

  • Implementing Effective Logging for Data Pipelines
  • Monitoring Data Pipeline Health and Performance Metrics
  • Setting up Alerts for Failures and Anomalies
  • Tools for Monitoring: Prometheus, Grafana (overview)
  • Best Practices for Operationalizing Data Pipelines

  • **Git and GitHub** for Collaborative Data Engineering Projects
  • Branching Strategies and Code Reviews
  • **CI/CD (Continuous Integration/Continuous Deployment)** Concepts
  • Automating Testing and Deployment of Data Pipelines
  • Infrastructure as Code (IaC) principles (Terraform, CloudFormation - overview)

  • End-to-End Data Engineering Project Implementation
  • Designing and Building a Scalable Data Pipeline
  • Industry Best Practices and Design Patterns
  • Preparing for Data Engineering Interviews and Portfolio Building
  • Job Market for Data Engineers in Hyderabad, Telangana, India
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