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Masters Program in Data Engineering

Live classes | 16 Weeks | Certificate Course

( 4.8/5 )
LIVE online Data Engineering course covering Data Warehousing, Data Lakes, Data Processing, Big Data & Hadoop, Advanced SQL, Data Visualization & many more
  • Delivered by Global Experts

  • 100+ hrs of LIVE Classes

  • 10+ Real-World Projects

  • 20+ Tools hands-on experience

  • 1-on-1 Mentorship

  • Focus on Soft Skills

Expert Speak
Shashank Mishra
Data Engineer, Amazon

Know Your Instructors

We have handpicked the best Data Engineering Professionals to deliver the program

Shashank Mishra

Data Engineer
Amazon | McKinsey | Paytm
Know More

Anurag Singh

Senior Artificial Intelligence Engineer
Kimberly Clark | DIAGEO India | AB InBev
IIM Lucknow
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Gururajan Govindan

Data Scientist
OneMagnify | Great Learning | IBM
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Abhinav Khandelwal

CD Lead
Optus | Teradata | Mahindra Satyam
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Master key tools

What you will learn?

Introduction to Data Engineering
Module 1
Concepts you will learn
  • What is data engineering?
  • Tasks of the data engineer
  • Data engineering problems
  • Tools of the data engineer
  • Kinds of databases
  • Processing tasks
  • Scheduling tools
  • Data Engineering use cases
Learning Outcome
Class 1

Quiz

Case Study

Class Participation

After this class you will
  • Understand the modern data ecosystem and its key entities
  • Learn what data engineering is and the key tasks in a data engineering lifecycle
  • Know about the responsibilities and skills of a data engineer
  • Understand the core concepts, processes, and tools to get a foundational knowledge of data engineering
WoW Factor
A data engineer with a deep passion for technology comes with extensive experience in working with Spatial Data Analysis and setting up data engineering infrastructure.
Data Engineering is recognized as one of the fastest-growing fields today. You will get enough context to start exploring the world of data engineering.
Data Engineering With Python
Module 2
Concepts you will learn
  • Jupyter Notebook
  • Python Anaconda
  • Python Variables and Basic Syntax
  • Conditional Expressions
  • Dictionaries
  • Core Python objects and operations
  • Numpy for statistical and matrix operations
  • Python modules
  • Matplotlib and Plotly
  • Pandas
Learning Outcome
Class 1

Quiz

Case Study

Class Participation

After this class, you will be able to:
  • Work on Jupyter Notebook
  • Run Python script & program
  • Work with Python variables, data types, and keywords
  • USe data structures, logic, working with files
  • Incorporate the OOP concepts
Class 2

Quiz

Case Study

Class Participation

After this class, you will be able to:
  • Import NumPy & SciPy module
  • Create an array using ND-array
  • Calculate standard deviation & correlation
  • Apply the Bayes theorem to the given dataset
  • Do data manipulation with Pandas
  • Clean & Manipulate dataset
  • Deploy Matplotlib for visualization
Tools you will master
  • Jupyter - Perfect web-based environment for performing exploratory analysis and visualization using python libraries.
Project
Building an ETL Pipeline in Python
In this project you will:
  • Implement data collection, web-scraping, and use APIs to extract data in Python
  • Extract, transform and load data using Jupyter Notebook
WoW Factor
An experienced data professional with an excellent understanding of Data Science & Analysis and a strong knowledge of Python, SQL, SOQL, Tableau, SAS, Statistics, ML, etc.
Python is an easy, simple, powerful, and innovative language. It is broadly used in Data Engineering operation in companies such as Instagram, Spotify, Amazon, Facebook, and many more.
Data Processing in Shell & Advance SQL
Module 3
Concepts you will learn
  • Superuser permissions: Sudo, Su
  • Linux file system
  • Read, write, execute permissions: chmod
  • Basic commands like ls, mkdir, touch
  • Vim Editor
  • Data exploration-related command: grep, awk, cut. corresponding switches
  • Copy, move & remove commands: cp, mv, rm
  • Bash scripting
  • Automation: setting up CRON job
  • Window Functions
  • Views & Materialized Views
  • Stored Procedures
  • Loading CSV files in database (for easy understanding try it in MySQL)
Learning Outcome
Class 1

Quiz

Case Study

Class Participation

After this class, you will be able to:
  • Write shell scripts to automate tasks and organize files
  • Use Bash to read & execute command
  • Develop simple, powerful, and data-specific command-line skills
  • Create schema, tables in SQL
  • Use common query tools
  • Work with SQL commands to filter, sort, & summarize data from a single table
Class 2

Quiz

Case Study

Class Participation

After this class, you will be able to
  • Create an analysis table from multiple queries using the UNION operator
  • Work with SQL commands to filter, sort, & summarize data from multiple tables
  • Create complex queries and subqueries to perform complex data manipulation and analysis
Tools you will master
  • Vim- A console editor that works intimately with the shell; Vim key binding and shell commands enables one for data engineering operations
  • SQL- Use SQL to communicate and manage the database. Learn SQL views and how to create, modify, and remove them
Project
Data Analysis with SQL
In this project you will:
  • Use SQL to effectively write queries, creatively analyze and explore data
  • Create an end-to-end pipeline that reads data, transforms data, and saves the result
WoW Factor
An experienced Data Engineer with a demonstrated history of working in the IT industry with the brands such as Amazon, Mckinsey, and Paytm
As a Data engineer, understand how to automate frequent tasks using shell scripts. Learn SQL, the lingua franca of everything related to data, and build familiarity with PostgreSQL
Cloud & Azure Fundamentals
Module 4
Concepts you will learn
  • Principles of cloud computing
  • Cloud development model
  • Types of cloud services
  • Microsoft Azure
  • Azure architecture
  • Concepts of Azure services
  • Azure Networking
  • Security, Privacy, Compliance, and Trust
  • Azure Pricing and Support
Learning Outcome
Class 1

Quiz

Case Study

Class Participation

After this class, you will be able to
  • Navigate Azure Cloud Platform
  • Understand Data Storage Services in Azure Storage
  • Create Azure web apps
  • Deploy databases in Azure
  • Understand Azure AD, cloud computing, Azure, and Azure subscriptions
  • Create and configure VMs in Microsoft Azure
WoW Factor
A seasoned data professional comes with the experience of mentoring aspirants & building data engineering infra for the organization in Azure cloud
Having cloud knowledge is a must in today's IT ecosystem. An introduction to the cloud enables you with the skills required for leveraging Azure cloud service to build data engineering solutions
Database Design & Modeling
Module 5
Concepts you will learn
  • Introduction to databases
  • Data persistence vs ephemeral storage
  • Interacting with databases
  • Alternatives to databases
  • Legacy databases
  • Relational databases (PostgreSQL)
  • NoSQL databases (MongoDB)
  • Database schemas
  • Data Modeling
  • Tables, Tuples, Types
  • Correctness and Constraints
  • CRUD Operations
  • Indexing and Aggregation Framework
Learning Outcome
Class 1

Quiz

Case Study

Class Participation

After this class, you will be able to
  • Develop a foundational understanding of the databases & their working
  • Select databases & design data models as per specific requirement
  • Work with different data schemas
  • Use data modeling techniques to optimize query processing
Class 2

Quiz

Case Study

Class Participation

After this class, you will be able to
  • Understand the concepts of RDBMS
  • Understand the relational database objects, Referential integrity, and distributed databases
  • Create a table and work with Postgres
Class 3

Quiz

Case Study

Class Participation

After this class, you will be able to
  • Setup MongoDB environment
  • Design a data model in MongoDB
  • Use Mongo shell for CRUD operations
  • Export and import data from/ to a MongoDB instance
Tools you will master
  • PostgreSQL- An open-source object-relational database system with 30+ years of active development in the industry
  • MongoDB - As per Forbes MongoDB is a $36 billion to a $40 billion market growing at 8% to 9% annually
Project
In this project you will:
  • Implement data modeling
  • Complete an ETL pipeline using Python
  • Model data by creating tables to run queries
  • Model and insert data into tables from CSV files
  • Use project template to manage all the imports
WoW Factor
A results-driven IT professional with notable success in data delivery to many large customers in the areas of Analytics for 14+ years. Well-versed in technologies like SQL, NoSQL, Postgres, AWS, GCP, Data warehouse, Machine Learning, Data Modelling, Tableau.
Modeling and managing data is a central focus of all big data projects. Develop practical skills in modeling big data projects and improve the performance of analytical queries for specific business requirements.
The Ecosystem of Big Data & Hadoop
Module 6
Concepts you will learn
  • Different dimensions of Big Data
  • Big Data implementations
  • Big Data Hadoop framework
  • Hadoop architecture and design principles
  • Components of the Hadoop ecosystem
  • Setup and Installation of Hadoop
  • HDFS daemons and architecture
  • HDFS Slaves – DataNodes
  • Different HDFS APIs and terminologies
  • Components of MapReduce
  • Execution of Map and Reduce together
  • Apache Hive
  • The architecture of Hadoop Hive
  • Hive execution flow
  • Hive operations
Learning Outcome
Class 1

Quiz

Case Study

Class Participation

After this class, you will be able to
  • Create dataset, perform query operations with Bigdata
  • Work with Hadoop storage & resource management
  • Write MapReduce code to analyze datasets in parallel across multiple machines
Class 2

Quiz

Case Study

Class Participation

After this class, you will be able to:
  • Understand Hive concepts, Data types, loading and querying data in Hive
  • Run hive scripts and Hive UDF
  • Implementing Partitioning, Bucketing, and Indexing in Hive
  • Query Data & Managing Outputs
Tools you will master
  • Apache Hadoop - Storage component of Hadoop, stores data on different machines in the form of files divided into blocks of 128MB (configurable)
  • MapReduce - Works in a divide-and-conquer manner and runs the processes on the different machines to reduce traffic on the network
  • Apache Hive - A distributed data warehouse system developed by Facebook, allows for easy reading, writing, and managing files on HDFS
  • Apache HBase - A Column-based NoSQL database, runs on top of HDFS and allows for real-time processing and random read/write operations
Project
Hands-on Hadoop Based Solution
In this project you will
  • Process different data files in Hadoop
  • Migrating Data from RDBMS to HDFS
  • Create user-defined functions
  • Perform dataset analysis in HIVE
WoW Factor
Data professional and an Intrapreneur with over 7+ of experience working with big data technologies such as Hadoop, Hive, and Spark across domains such as Automobile, Insurance, and Finance.
According to Forbes Hadoop Market is expected to reach $99.31B by 2022 at a CAGR of 42.1%. Organizations are adopting Hadoop to store & analyze Big Data Hence, the demand for jobs in Big Data and Hadoop is also rising rapidly.
Azure Services & Data Lakes
Module 7
Concepts you will learn
  • Azure SQL Server
  • Azure Database in VMs
  • Azure Data Factory
  • Azure Storage
  • Azure Data Lake Storage
  • Architecting Azure Data Lake
  • Organizing Data Lake
  • Data storage solutions
  • Non-relational data stores
  • Data distribution and partitions
  • Consistency model in CosmosDB
Learning Outcome
Class 1

Quiz

Case Study

Class Participation

After this class, you will be able to:
  • Navigate data engineering solution architecture
  • Understand Data Storage & Services In Azure Storage
  • Optimize performance and costs when consuming the data at scale
  • Provide access to data to meet security requirements
Class 2

Quiz

Case Study

Class Participation

After this class, you will be able to:
  • Understand data lifecycle and architecture around Data Lake
  • Create table partitioning in Azure Data Lake
  • Use different tools and scenarios to ingest data into Data Lake
Tools you will master
  • Azure Data Factory - A cloud-based ETL and data integration service, allows the creation of data-driven workflows for
  • Azure SQL Database - It helps streamline the efficiency of data storage by allowing quick query, processing, and storage of data
Project
In this project you will:
  • Use Azure services to develop data processing, monitoring, and optimization solution
  • Ingest and organize data into the Data Lake
  • Implementing business logic and security in the Data Lake solution
  • Use Azure Databricks and HDInsight to process data in ADLS
  • Monitor the performance of the Data lake
WoW Factor
AI/ML Architect with 12 years of experience, skilled in Azure Data Factory/Azure Data Flow, Azure Databricks, Azure Data Lake, Azure Cosmos DB, Azure SQL DataWarehouse/Synapse Analytics for Database and DataWarehouse, etc.
Azure provides future-ready services and resources for big data engineering needs, a hands-on practice of these resources builds a competitive advantage.
Data Processing and Batch Processing
Module 8
Concepts you will learn
  • Batch processing solutions
  • Integration runtime for Data Factory
  • Linked services and datasets
  • ETL in Azure DataBricks
  • Databricks Delta
  • Configuration input and output
  • Monitor data storage
  • Stream analytics monitoring
  • HDInsight processing
  • Apache Spark
Learning Outcome
Class 1

Quiz

Case Study

Class Participation

After this class, you will be able to:
  • Implement scalable, performant, and accurate data processing
  • Run batch processing jobs in Azure SQL Data Warehouse
  • Understand HDInsight enabled cloud-hosted Hadoop clusters
Class 2

Quiz

Case Study

Class Participation

After this class, you will be able to
  • Understand the basics of Apache Spark
  • Transform data using Spark SQL and DataFrames
  • Create dynamic visualizations from real-time analytics on merged streaming and historical data
Tools you will master
  • Azure HDInsight - A cloud distribution of Hadoop components, makes the processing of massive amounts of data easy, fast, and cost-effective to process
  • Databricks- It provides data science and data engineering teams with a fast, easy, and collaborative Spark-based platform on Azure
Project
In this project you will:
  • Implementing data processing solution
  • Working with big data storage & querying it with Spark
  • Connecting to Kinesis as a streaming data source
  • Using the DataFrame API to transform streaming data
WoW Factor
AI/ML Architect with 12 years of experience, skilled in building big data processing and analysis infrastructure on top of solutions like Databricks, Azure Data Factory/Data Flow, and Apache Spark
Data processing and ingestion pipelines sit at the heart of every data engineering solution, it gives you a complete end to end understanding of the processing and analysis of different datasets
Real Time-Stream Data Processing
Module 9
Concepts you will learn
  • Installation, Configuration of Airflow
  • Executors like Local, Celery, and Sequential
  • Xcoms, Branching, and Variables
  • DAG Chaining
  • DAG's Workflow
  • Airflow Architecture
  • Authentication and Log storage
Learning Outcome
Class 1

Quiz

Case Study

Class Participation

After this class, you will be able to
  • Define and orchestrate data pipeline components
  • Get familiar with the usefulness of data pipelines
  • Work with the Components of Apache Airflow
  • Implement Airflow DAGs using operators, tasks, and scheduling
  • Work with data pipelines in production
  • Ingest data, perform cleaning and transforming data
  • Implement quality control measures in your data pipelines
  • Execute best practices for data pipelines development with Airflow
Tools you will master
  • Apache Airflow is an open-source scheduler to manage your regular jobs. It is an excellent tool to organize, execute, and monitor your workflows so that they work seamlessly.
Project
Automatic Data Pipeline Development
In this project you will:
  • Utilize the core concept learned to gather data from several different data sources
  • Transform, combine, and summarize the data
  • Building a successful, scalable, elastic, and distributed pipeline using a lean approach
WoW Factor
Senior Data Engineer with extensive experience in working with Spatial Data Analysis and setting up data infrastructure for global organizations in the consumer and e-commerce sector using key technologies like Spark, Apache Airflow, Apache Lucene & ES, NoSQL Databases, Hive, Postgres.
When it comes to managing data collection, munging, and consumption, data pipeline frameworks play a significant role and with the help of Apache Airflow, the task of creating a data pipeline is not only easy but it’s actually fun.
Real Time-Stream Data Processing
Module 10
Concepts you will learn
  • Setting up Kafka
  • Topics, Partitions, Offsets, Topic replication
  • Producers, brokers, consumers, zookeeper
  • Kafka message guarantees
  • Kafka Architecture
  • Interacting with Kafka cluster
  • Python producer and consumer simulating application
  • Kafka offsets and managements
  • Setting up Spark
  • Spark Architecture
  • Spark Context, Spark Session
  • Interact with spark-shell
  • Actions & Transformations
  • Spark program explanation
  • Working of Spark
  • Spark query execution
  • RDDs, Data Frames, and Data sets
  • Spark streaming listening for messages from a socket
  • Spark Streaming + Kafka Integration
Learning Outcome
Class 1

Quiz

Case Study

Class Participation

After this class, you will be able to
  • Understand the importance, use case, and attributes of streaming data
  • Design, develop and test real-time stream processing applications
  • Master various Kafka components- consumer, producer, and brokers
  • Handle real-time data feeds through Apache Kafka
  • Use Kafka streams library and Kafka Producer APIs
Class 2

Quiz

Case Study

Class Participation

After this class, you will be able to
  • Integrate spark streaming with Kafka
  • Work on Spark streaming to build a scalable fault-tolerant streaming application
  • Use data sources to process massive streams of real-time data
  • Implement Spark 2's structured and streaming APIs
Tools you will master
  • Apache Kafka - An unified, high-throughput, low-latency platform for handling real-time data feeds
  • Apache Spark - An extension of core Spark API, enables scalable, high-throughput, fault-tolerant stream processing of live data streams
Project
Hands-on Hadoop Based Solution
In this project you will:
  • Installing and managing Kafka cluster
  • Designing pipelines to process real-time streams
  • Maintaining stateful data across a continuous stream for performing
  • Perform analytics operations using Apache Kafka
WoW Factor
Class conducted by a professional with 10+ years of first-hand experience in cutting edge big data technologies like Spark (Batch / Stream processing), Kafka, Hortonworks / Cloudera stacks, Reactive streams to name a few.
You will master Apache Kafka, used in production by over 33% of the Fortune 500 companies such as Netflix, Airbnb, Uber, Walmart, and LinkedIn.
Data Warehousing & ETL
Module 11
Concepts you will learn
  • Concepts of a data warehouse
  • OLTP and OLAP
  • Datamart
  • Operational Data Store
  • Dimensions and facts
  • Types of Hierarchies
  • Normalization
  • Schema types - Star, Snowflake, Galaxy
  • Principles of dimensional modeling
  • Modeling - ER diagrams
  • ETL Concepts
  • ETL Architectural components
  • Data Loading techniques
Learning Outcome
Class 1

Quiz

Case Study

Class Participation

After this class, you will be able to:
  • The fundamental architecture and components of data warehouse and data mart
  • The process of data extraction, transformation, and loading
  • Microsoft Azure SQL Data Warehouse basics
  • Data Warehouse MPP architecture table types
  • Partitioning, distribution key, and many other important concepts
Class 2

Quiz

Case Study

Class Participation

After this class, you will be able to:
  • The difference between Traditional vs Modern vs Synapse Data warehouse architecture
  • To provision, configure, and scale Azure Synapse Analytics service
  • Integrated data from disparate sources
  • Perform reporting and analysis of the data
Tools you will master
  • Azure SQL Data Warehouse - Provide functionality & enables to analyze on-premises data warehouse and migrate data to Azure Data Warehouse
  • Azure Synapse Analytics - A limitless analytics service that brings together data integration, enterprise data warehousing, and big data analytics
Project
Building Warehousing solution with Azure
In this project you will:
  • Perform hands-on development with Azure SQL Data warehouse
  • Ingest, prepare, manage, and serve data with Azure Synapse analytics service
  • Set firewall rules and connect with SQL Server Management studio
WoW Factor
8+ years experience in setting up scalable cloud big data platforms for real-time stream and analytics. Expertise in designing SQL scripts, Dimensional Modelling, Data Warehousing, Business Intelligence Tools.
Synapse Analytics acts as a bridge between data warehouse, data lake, machine learning, and data pipelines, provides you a comparative edge
Data Governance & Management
Module 12
Concepts you will learn
  • Data Lineage
  • Data Collection
  • Data Governance
  • Data Transformation
  • Data Quality and Sharing
  • Data Security
  • Data Privacy and Compliance
Learning Outcome
Class 1

Quiz

Case Study

Class Participation

After this class, you will be able to
  • Understand Meaning and Importance of Data Governance
  • Get Align with Data governance goals and benefits
  • Be Familiar with the Components of a data governance framework
  • Understand Data governance challenges
  • Be able to Implement Data governance in Respective Organisation
  • Get Introduced to Best practices for managing data governance initiatives
Class 2

Quiz

Case Study

Class Participation

After this class, you will be able to
  • Understand what data lineage is
  • Learn about the value of telling the data story
  • Learn about the data lineage use cases
  • Implement Data Lineage in practical
  • Learn Data Security Aspect as a Data Engineer
  • Building Data Projects while taking Privacy & Compliance into account
WoW Factor
Experienced professional with 2 decades of experience in the Software Industry, currently working on Big Data Analytics project to provides high-level management view as well as grass-root level operational actions leading to a quantum leap in efficiency improvement.
Effective data governance ensures that data is consistent and trustworthy and doesn't get misused. It's increasingly critical for a data engineer to face new data privacy regulations and rely more and more on data analytics to help optimize operations and drive business decision-making.

Earn your Certificate of Achievement

This certificate will be proof that you have taken a big step in mastering the domain by completing our comprehensive Masters program.

Strengthen your resume, share your achievement & set yourself for an accelerated growth.

Design Illustration

Learner Profile

AntWak Data Engineering course brings professionals and graduates from a variety of backgrounds, enhancing the diversity of the cohort

Design Illustration

Why is AntWak the right choice for you?

Offline Training Institutes
Online Edtech Companies
Powered by Real Professionals from Top Brands
Primarily driven by Academicians
Driven by Academicians or tie-ups with institutes with few industry lecturers
All classes designed & delivered by real professionals suited for each competency
Extensive Live Classes
In-class program with no proper learning tech
Learning mainly self-paced with low interactions
✓✓
100+ hours of Live, Immersive classes
Deep-focus on holistic development to crack your dream interview
Mostly not available
1-2 sessions of CV prep & mock interviews
✓✓
20+ hours of support on Soft skills, CV prep & mock interviews
Recognition beyond certificate
Program level certificate
Program level certificate
✓✓
Program level certificate + Skill based Rating by Senior Professionals - embed into your CV
Best Price for Value
₹75,000 + GST = ₹88,000
₹1,20,000 + GST = ₹1,40,000
✓✓
99,999 69,999 including GST

Accessible & Affordable

Financing options available to fit the program in your monthly budget

Program Fee: 99,999  69,999/-  including GST
Start with Flexible ZERO interest EMI plans starting @ ₹9,333/month

Holistic career services & support

Career Coach
In-depth help on CV review, Linkedin readiness and interview prep
Experienced Mentors
1-1 mentorship & Live sessions with mentors who have traversed similar journeys
Industry Introductions
Personalized intros & referrals to our community of mentors & hiring partners

FAQ

Who is this Data Engineering course for?
This course is suitable for
1. IT professionals looking for a career in Data Engineering
2. Database administrators looking for career transition into Data Engineering
3. Data analyst and BI Developers with programming exprience
4. Beginners in the data engineering domain
5. UG/PG students with programming knowledge
Will I be able to complete this program alongside my full time job?
Yes. The program is designed keeping in mind rigorous schedule of both student and mentors. The core curriculum will be covered over the weekends (1 class of 2 hour on Saturday and 2 classes of 2 hour each on Sunday). However, students will be given assignment and pre-reads which they suppose to cover over the weekdays so that they can make most of industry experts time on weekend.
Overall time commitment of 14-16 hours/week will suffice. Also, community events such as AMA, fireside chat and guest speakers will happen in the evenings on a regular basis. You get to pick what you want to attend.
How much will the course cost overall?
The course will cost Rs. 69,999 inclusive of GST.
Is there EMI option available?
Yes, there are 4 month and 6 month EMI options available at zero interest cost.
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