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Professional Course in Data Engineering

Master Data Engineering tools and techniques On-Premise or on Cloud Platform and gain real-time experience in designing, developing, and maintaining data pipelines.
  • 60 Hours Blended - Online and Classroom
  • 60+ Hours of Assignments and practicals
  • 1+ Capstone projects
  • Lifetime Learning Management System access
Data Engineering certification course reviews - 360digitmg

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Data Engineering certification course reviews - 360digitmg

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Academic Partners & International Accreditations
  • Data Engineering Course with Microsoft
  • Data Engineering certification with nasscomm
  • Data Engineering certification innodatatics
  • Data Engineering certification with SUNY
  • Data Engineering certification with NEF

360DigiTMG's Professional Course on Data Engineering introduces and explores the various tools needed for Data Engineers to solve modern-day issues. It expands learner's understanding of the numerous skills involved in knowing tools like Python, SQL, Big data tools, Spark, Kafka, Airflow, Databricks, Azure data factory, data lake, Redshift, BigQuery, Synapse, AWS Glue, etc. Participants get a chance to extract raw data from various data sources in multiple formats and transform them into actionable insights, and ingest data into a single, easy-to-query database. They learn to handle huge data sets and build data pipelines to optimize processes for big data analytics. Participants get a chance to dive deeper into advanced data engineering projects that will help in gaining practical experience. 

Professional Data Engineering

Program Cost

INR 80,000 65,000/-

(Excluding Taxes)

Professional Data Engineering Course Overview

The professional course in Data Engineering lets you explore various tools that help you expand your understanding of the various skills involved and the tools needed to ace the job. The students will be trained to extract raw data from various data sources in multiple formats and then transform them into actionable insights, and deploy them into a single, easy-to-query database. Learn to handle huge data sets and build data pipelines to optimize processes for Big Data. Dive deeper into advanced data engineering projects which will help you gain practical experience and skills.
What is Data Engineering?
A Data Engineer collects and transforms data to empower businesses to make data-driven decisions. He/She has to pay attention to security and compliance; reliability and fidelity; scalability and efficiency; and flexibility and portability while designing, operationalizing, and monitoring data processing systems.
360DigiTMG Advantages
Learning Management System (LMS): - Students will be provided with LMS access, which included class recordings, self–paced videos, assignment course works, and reference materials. Data sets, algorithms, etc.
Training faculty with 10+ years of average experience and trained 20,000+ professionals and 10,000+ students from 8-12 countries. Corporate clients include many Fortune 500 companies.
Carries a legacy of training 20,000+ professionals and 10,000+ students from across the globe. Our program has been approved by 3 leading international universities /accreditation bodies.
The curriculum has been meticulously designed by industry experts by considering student communities as well as working professionals.
Career Mentorship & Placement assistance:- A coordinator will be assigned to you until you complete the program for smooth delivery of your training journey with 360DigiTMG

Professional Data Engineering Training Learning Outcomes

These modules will lay out a detailed exposure for Data Engineering tools and techniques. The core of Data Engineering involves an understanding of various techniques like data modeling, building data engineering pipelines, etc. Participants will get a keen understanding of how to handle data. As the course progresses, they get to learn how to design, build as well as maintain the data pipelines and work with big data of diverse complexity and production level infrastructures. Participants will also learn to extract and gather data from multiple sources, build data processing systems, optimize processes for big data, orchestrate the pipelines and much more. Also learn to

Understand the Data Engineering Ecosystem and Lifecycle
Learn to draw data from various files and databases (SQL & NoSQL) – On-premises and Cloud
Acquire skills and techniques to clean, transform, and enrich your data
Learn to scale data pipelines in the production environment
Use of cloud services for designing, and automating the data pipelines
Work with Data warehouses and Data lakes
Understanding cloud-native tools like Redshift, BigQuery, Synapse, etc.
Learn to work with ETL tools: AWS Glue, Azure Data Factory, Google Cloud Data Fusion, etc.
Develop a real-time structured streaming data pipeline with Spark and Kafka
Orchestrate the data pipelines to automate the data ETL tasks with Apache Airflow

Block Your Time

data engineering course

60 hours

Classroom Sessions

data engineering course

60+ hours


Who Should Sign Up?

  • Science, Maths, and Computer Graduates
  • IT professionals who want to Specialize in Digital Tech
  • SQL and related developers or software developers
  • Students/IT professionals have an interest in Data and Databases
  • Professionals working in the space of Data Analytics
  • Academicians and Researchers working with data
  • Cloud and BigData enthusiasts

Professional Data Engineering Course Modules

  • Data Science vs Data Engineering
  • Data Engineering Infrastructure and Data Pipelines
  • Data Architectures
    • Lambda
    • Kappa
    • Streaming Big Data Architectures Monitoring pipelines
  • Working with Databases and various File formats (Data Lakes)
    • SQL
      • MySQL
      • PostgreSQL
    • NoSQL
      • MongoDB
      • Neo4j
      • HBase
    • Cloud Sources
      • Microsoft Azure SQL Database
      • Amazon Relational Database Service
      • Google Cloud SQL
  • Python Programming
    • Getting started with Python programming for Data Processing
    • Data Types
    • Python Packages
    • Loops and Conditional Statements
    • Functions
    • Collections
    • String Handling
    • File handling
    • Exceptional Handling
    • MySQL Integration
    • MongoDB Integration
  • Pre-processing, Cleaning, and Transforming Data
  • Apache Hadoop
    • Pseudo Cluster Installation
    • HDFS
    • Hive
    • HBase
  • Spark Components
  • Spark Executions – Session
  • RDD
  • Spark DataFrames
  • Spark Datasets
  • Spark SQL
  • Spark MLlibs
  • Spark Streaming
  • Big Data and Apache Kafka
  • Producers and Consumers
  • Clusters Architectures
  • Kafka Streams
  • Kafka pipeline transformations
  • Building pipelines in Apache Airflow
  • Deploy and Monitor Data Pipelines
  • Production Data Pipeline
  • Data Lake Cloud offerings
  • Cloud Data Warehouse Services
  • Intro to AWS Data Warehouses, Data Marts, Data Lakes, and ETL/ELT pipelines
  • Configuring the AWS Command Line Interface tool
  • Creating an S3 bucket
  • Working with Databases and various File formats (Data Lakes)
  • Amazon Database Migration Service (DMS) for ingesting data
  • Amazon Kinesis and Amazon MSK for streaming data
  • AWS Lambda for transforming data
  • AWS Glue for orchestrating big data pipelines
  • Consuming data - Amazon Redshift & Amazon Athena for SQL queries
  • Amazon QuickSight for visualizing data
  • Hands-on - AWS Lambda function when a new file arrives in an S3 bucket
  • Azure Data Lake - Managing Data
  • Securing and Monitoring Data
  • Introduction to Azure Data Factory (ADF)
  • Building Data Ingestion Pipelines Using Azure Data Factory
  • Azure Data Factory Integration Runtime
  • Configuring Azure SQL Database
  • Processing Data with Azure Databricks
  • Introduction to Azure Synapse Analytics
  • Data Transformations with Azure Synapse Dataflows
  • Azure Synapse SQL Pool
  • Monitoring And Maintaining Azure Data Engineering Pipelines
  • Getting Started with Data Engineering with GCP
  • Bigdata Solutions with GCP Components
  • Data Warehouse - BigQuery
  • Batch Data Loading using Cloud Composer
  • Building A Data Lake using Dataproc
  • Processing Streaming Data with Pub/Sub and Dataflow
  • Visualizing Data with Data Studio
  • Architecting Data Pipelines
  • CI/CD On Google Cloud Platform for Data Engineers

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How we prepare you

  • data engineering course with placements
    Additional assignments of over 60+ hours
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    Resume and LinkedIn Review Sessions
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    Lifetime LMS Access
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    24/7 support
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    Job placements in Data Engineering fields
  • best data engineering course with USP
    Complimentary Courses
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    Unlimited Mock Interview and Quiz Session
  • best data engineering training with placements
    Hands-on experience in a live project
  • data engineering course with USP
    Offline Hiring Events

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