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Data Engineering

Data engineering involves the design, development, and management of systems and processes that enable the collection, storage, and processing of large volumes of data. It focuses on building the infrastructure and tools necessary to ensure data quality, reliability, and accessibility. Data engineering plays a crucial role in enabling organizations to extract valuable insights from their data and drive business outcomes.

Skill Sets

  • Coding - Python 

  • Containers 

  • Databases 

  • Big Data

  • Cloud

  • Data Ingestion

  • Data Integration (ETL | ELT)

  • Data Governance

Common Tool

  • Docker | k8s

  • SQL | NoSQL

  • Spark | Hadoop

  • AWS | GCP | Azure

  • Kafka | API

  • SSIS 

  • Air Flow

What do Data engineers do? 

  • Data Pipeline Development: Data engineers design and develop efficient data pipelines to move data from various sources into storage and processing systems. They create workflows and scripts to automate data extraction, transformation, and loading (ETL) processes.

  • Data Integration: Data engineers integrate data from multiple sources, such as databases, APIs, files, and streaming platforms, ensuring compatibility and consistency across different data sets. They handle data formats, data validation, and data cleansing to maintain data integrity.

  • Data Storage and Management: Data engineers select and implement appropriate data storage solutions, such as data warehouses, data lakes, or NoSQL databases. They optimize data storage structures, partitioning, and indexing for efficient data retrieval and query performance.

  • Infrastructure Design and Optimization: Data engineers design and optimize the underlying infrastructure needed to support data processing and storage. This includes setting up and managing distributed computing systems, clusters, and cloud-based infrastructure to handle big data workloads.

  • Performance Monitoring and Optimization: Data engineers monitor data pipelines and systems to identify bottlenecks, optimize performance, and troubleshoot issues. They implement monitoring tools and alerts to ensure the availability and reliability of data systems.

  • Documentation and Documentation: Data engineers document data pipelines, processes, and infrastructure to ensure clarity, reproducibility, and knowledge sharing within the team. They create documentation on data sources, data flows, and data lineage.

Data Engineering Bootcamp

3-6 month immersive learning program designed for those who want to learn data Engineering. This program goes beyond theoretical knowledge by providing a valuable opportunity to gain real industry experience. Through our partnership with a pioneer data analytics company, we offer job placement opportunities that allow you to apply your newly acquired skills in a professional setting. By completing this program, you not only gain the in-demand skills for the Data & AI industry but also the chance to work with a leading company in the field, kick-starting your career with hands-on experience and invaluable industry exposure.

  • Advanced SQL Language 

  • Data Warehouse

  • ETL  Development 

  • ELT/ETL by SSIS 

  • Azure data factory

12 Weeks - 110 Hours

Sat & Wed - 3 Hours

Online Live

  • Introduction to Big Data

  • Spark

  • Python PySpark  

  • Spark Streaming

  • Kafka

12 Weeks - 110 Hours

Sat & Wed - 3 Hours

Online Live

Job Placement
  • 4-12 weeks of job placement in a real project 

4-12 Weeks 


Get in Touch


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