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Antwak Experiential Program (AEP)

Live classes | 16 Weeks | Limited seats

Get Ready for a New Job in Data Engineering

A LIVE online Data Engineering course where you will learn Data Warehousing, Data Lakes, Data Processing, Big Data & Hadoop, Advanced SQL, Data Visualization & many more from top professionals and get endorsed by them

    Join at   ₹ 6,200/month

    100% Placement Assistance

    Half the Price and Double the Live classes compared to industry

    Apply Now

    Get Ready for a New Job in Data Engineering

    100+ hrs of live classes

    +

    10+ Industry leading projects

    +

    1:1 mentoring

    +

    Focus on soft skills

    +

    Endorsement by industry experts

    +

    CV/Linkedin review

    +

    Mock Interviews

    +

    Recruiter Introductions

    =
    New Data Engineering Career

    New Data Engineering Career

    Meet the Instructors

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

    Karthik Ramesh

    Senior Data Engineer
    Itoc | Westpac | Facebook | Barclays
    AWS Certified Solutions Architect
    MEET THE EXPERT

    Shashank Mishra

    Data Engineer
    Amazon | McKinsey | Paytm
    MEET THE EXPERT

    Anurag Singh

    Senior Artificial Intelligence Engineer
    Kimberly Clark | DIAGEO India | AB InBev
    IIM Lucknow
    MEET THE EXPERT

    Gururajan Govindan

    Data Scientist
    OneMagnify | Great Learning | IBM
    MEET THE EXPERT

    Hands-on Projects on Key Data Engineering Tools

    Program Curriculum

    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
    • 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 10
    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

    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
    Program Fee: 99,999  69,999/-  including GST
    Start with Flexible ZERO interest EMI plans starting @ ₹6,200/month

    Experience first hand the power of Antwak promise

    Get a glimpse of experiential learning from these expert videos

    VIEW ALL VIDEOS
    Significance of data profiling in any data warehousing project
    Abasesha Patra
    Siemens Healthineers | Genpact
    WATCH
    Steps involved in the maintenance of Data pipelines
    Matteo Fiore
    Inspera AS
    WATCH
    Different steps to execute a Big Data real-time data streaming project
    Karthik Ramesh
    Itoc | Facebook I Barclays
    WATCH
    Understand how serverless computing resembles cloud computing
    Abhisheak Gupta
    PayU
    WATCH

    Antwak provides holistic career services and 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 6 month and 9 month EMI options available at zero interest cost.
    Show More
    16 Weeks
    Taught by Industry leaders
    100+ hrs of Live classes
    Limited seats
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