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Data Engineer Job Oriented Program

#No.1 Data Engineer Course

Prepzee’s Data Engineering Course has been curated to help you master skills like PySpark, Azure Data Engineer Certification DP 203, Databricks, Snowflake, Airflow, Kafka and CosmosDB. This Data Engineering Bootcamp will help you get your dream Job in Data Engineering Domain.

  • Master Databricks, Snowflake, Airflow, Kafka
  • Hands-On PySpark Training for Data Engineering
  • Clear Azure Data Engineer Certification DP203

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Career Transition

This Data Engineering Training program is for you if

Data Engineer Classes Overview

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    90+ Hours of Live Training

    Including Top 3 Data Engineering Tools according to Linkedin Jobs

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    80+ Hours Hands-on & Exercises

    Learn by doing multiple labs in your learning journey.

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    10+ Projects & Case Studies

    Get a feel of Data Engineering professionals by doing real-time projects.

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    24*7 Technical Support

    Call us, E-Mail us whenever you stuck.

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    Learn from the Top 1% of Experts

    Instructors are Microsoft Certified Trainers.

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    Lifetime Live Training Access

    Attend multiple batches until you achieve your Dream Goal.

What You will Learn in the Program?

  • Module 1

    PySpark/ Kafka for Data Engineering    

    24 Hours

    Introduction to Python for Apache Spark

    Deep Dive into Apache Spark Framework

    Mastering Spark RDD’s

    Dataframes and SparkSQL

    Apache Spark Steaming Data Sources

    Core Concepts of Kafka

    Kafka Architecture

    Where is Kafka Used

    Understanding the Components of Kafka Cluster

    Configuring Kafka Cluster

  • Module 2

    Data Warehousing Fundamentals

    6 Hours
    • OLAP vs OLTP
    • What is a Data Warehouse?
    • Difference between Data Warehouse, Data Lake and Data Mart
    • Fact Tables
    • Dimension Tables
    • Slowly changing Dimensions
    • Types of SCDs
    • Star Schema Design
    • Snowflake Schema Design
    • Data Warehousing Case Studies
  • Module 3

    Data Engineering with Cloud ( Azure )

    36 Hours
    • Introduction to Microsoft Azure
    • Azure Databricks Introduction
    • Read and Write Data in Azure Databricks
    • Data Processing in Azure Databricks
    • Work with DataFrames in Azure Databricks
    • Platform Architecture, Security and Data Protection in Databricks
    • Introduction to Azure Synapse Analytics
    • Design a multidimensional schema to optimize analytical workloads
    • Azure Synapse serverless SQL pool
    • Ingest and Load Data into the Data Warehouse
    • Transform Data with Azure Data Factory or Azure Synapse Pipelines
    • Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics
    • Configure Azure Synapse Link with Azure Cosmos DB
  • Module 4

    Orchestration with Apache Airflow           

      12 Hours

    Airflow Introduction

    Different Components of Airflow

    Installing Airflow

    Understanding Airflow Web UI

    DAG Operators & Tasks in Airflow Job

    Create & Schedule Airflow Jobs For Data Processing

    Create plugins to add functionalities to Apache Airflow

  • Module 5

    Compute with Snowflake

    20 Hours

    Introduction of SnowFlake Data Warehousing Service

    SnowFlake Architecture

    Complete Setup of SnowFlake

    Create Data Warehouse on SnowFlake

    Analytical Queries on SnowFlake Data Warehouse

    Understand the entire Snowflake workflow from end-to end

    Undestanding SnowPark (Execute PySpark Application on SnowFlake)

  • Module 6

    Industry Scenario based Labs   

    40 Hours

    Lab 1 :  Explore Compute & Storage options for Data Engineering Workloads

    Lab 2 : Load and Save Data through RDD in PySpark

    Lab 3 : Configuring Single Node Single Cluster in Kafka

    Lab 4 : Run Interactive Queries using Azure Synapse Analytics Serverless SQL Pools

    Lab 5 : Data Exploration and Transformation in Azure Databricks

    Lab 6 : Explore Transform and Load Data into the Data Warehouse using Spark

    Lab 7 : Ingest and Load Data into the Data Warehouse

    Lab 8 : Transform Data with Azure Data Factory or Azure Synapse Pipeline

    Lab 9 : Real Time Stream Processing with Stream Analytics

    Lab 10 : Create a Stream Processing Solution with Event Hub and Databricks

Program Creators

Neeraj

Amazon Authorised Instructor

14+ Years of experience

Sidharth

Amazon Authorised Instructor

15+ Years of experience

Nagarjuna

Microsoft Certified Trainer

12+ Years of experience

KK Rathi

Microsoft Certified Trainer

17+ Years of experience

Where Will Your Career Take Off?

  • Data Engineer

    The primary role involves designing, building, and maintaining data pipelines and infrastructure to support data-driven decision-making.

  • Data Integration Specialist

    Responsible for integrating data from various sources, ensuring data quality, and creating a unified view of data for analysis.

  • Cloud Data Warehouse Engineer

    Designing and managing data warehouses for efficient data storage and retrieval, often using technologies like Databricks, Snowflake and Azure.

  • Cloud Data Engineer:

    Specializing in data engineering within cloud platforms like AWS, Azure leveraging cloud-native data services.

  • Data Consultant:

    Providing expertise to organizations on data-related issues, helping them make informed decisions and optimize data processes.

  • Data Architect

    Designing the overall data architecture for an organization, including data models, storage, and access patterns.

Skills Covered

Tools Covered

Unlock Bonuses worth 20000₹

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Bonus 1

AWS Cloud Practitioner Course

Worth 5000₹
Bonus 2

Linux Fundamentals Course

Worth 3000₹
Bonus 3

Azure DP 203 Master Cheat Sheet

Worth 3000₹
Bonus 4

Playbook of 97 Things Every Data Engineer should Know

Worth 4000₹
Bonus 5

Designing Data Intensive Applications PlayBook

Worth 4000₹

Time is Running Out. Grab Your Spot Fast!

Placement Overview

  • 500+
    Career Transitions
  • 9 Days
    Placement time
  • Upto 350%
    Salary hike
  • Download report

Data Engineer Job Oriented ProgramLearning Path

Course 1

online classroom pass

Data Engineer Job Oriented Program

Embark on your journey towards a thriving career in data engineering with best Data Engineering courses. This comprehensive program is meticulously crafted to empower you with the skills and expertise needed to excel in the dynamic world of data engineering.Learn Data Engineering with Prepzee, throughout the program, you’ll explore a wide array of essential tools and technologies, including industry favorites like Databricks, Snowflake, PySpark, Azure, Azure Synapse Analytics, and more.Dive into industry projects, elevate your CV and LinkedIn presence, and attain mastery in Data Engineer technologies under the mentorship of seasoned experts.

 

1.1: Overview of Python

1.2: Different Applications where Python is Used

1.3: Values, Types, Variables

1.4: Operands and Expressions

1.5: Conditional Statements

1.6: Loops

1.7: Command Line Arguments

1.8: Writing to the Screen

1.9: Python files I/O Functions

1.10: Numbers

1.11: Strings and related operations

1.12: Tuples and related operations

1.13: Lists and related operations 

1.14: Dictionaries and related operations

1.15: Sets and related operations

 

Hands On:

  • Creating “Hello World” code
  • Demonstrating Conditional Statements
  • Demonstrating Loops
  • Tuple – properties, related operations, compared with list
  • List – properties, related operations
  • Dictionary – properties, related operations
  • Set – properties, related operations

2.1 Functions

2.2 Function Parameters

2.3 Global Variables

2.4 Variable Scope and Returning Values

2.5 Lambda Functions

2.6 Object-Oriented Concepts

2.7 Standard Libraries

2.8 Modules Used in Python

2.9 The Import Statements

2.10 Module Search Path

2.11 Package Installation Ways

 

Hands-On:

  • Functions – Syntax, Arguments, Keyword Arguments, Return Values
  • Lambda – Features, Syntax, Options, Compared with the Functions
  • Sorting – Sequences, Dictionaries, Limitations of Sorting
  • Errors and Exceptions – Types of Issues, Remediation
  • Packages and Module – Modules, Import Options, sys Path

 

3.1 Spark Components & its Architecture

3.2 Spark Deployment Modes

3.3 Introduction to PySpark Shell

3.4 Submitting PySpark Job

3.5 Spark Web UI

3.6 Writing your first PySpark Job Using Jupyter Notebook

3.7 Data Ingestion using Sqoop

 

Hands-On:

  • Building and Running Spark Application
  • Spark Application Web UI
  • Understanding different Spark Properties

4.1 Challenges in Existing Computing Methods

4.2 Probable Solution & How RDD Solves the Problem

4.3 What is RDD, It’s Operations, Transformations & Actions

4.4 Data Loading and Saving Through RDDs

4.5 Key-Value Pair RDDs

4.6 Other Pair RDDs, Two Pair RDDs

4.7 RDD Lineage

4.8 RDD Persistence

4.9 WordCount Program Using RDD Concepts

4.10 RDD Partitioning & How it Helps Achieve Parallelization

4.11 Passing Functions to Spark

 

Hands-On: 

  • Loading data in RDDs
  • Saving data through RDDs
  • RDD Transformations
  • RDD Actions and Functions
  • RDD Partitions
  • WordCount through RDDs

5.1 Need for Spark SQL

5.2 What is Spark SQL

5.3 Spark SQL Architecture

5.4 SQL Context in Spark SQL

5.5 Schema RDDs

5.6 User-Defined Functions

5.7 Data Frames & Datasets

5.8 Interoperating with RDDs

6.1 Need for Kafka

6.2 What is Kafka

6.3 Core Concepts of Kafka

6.4 Kafka Architecture

6.5 Where is Kafka Used

6.6 Understanding the Components of Kafka Cluster

6.7 Configuring Kafka Cluster

 

Hands-On: 

  • Configuring Single Node Single Broker Cluster
  • Configuring Single Node Multi-Broker Cluster

7.1 Drawbacks in Existing Computing Methods

7.2 Why Streaming is Necessary

7.3 What is Spark Streaming

7.4 Spark Streaming Features

7.5 Spark Streaming Workflow

7.6 How Uber Uses Streaming Data

7.7 Streaming Context & DStreams

7.8 Transformations on DStreams

7.9 Describe Windowed Operators and Why it is Useful

7.10 Important Windowed Operators

7.11 Slice, Window and ReduceByWindow Operators

7.12 Stateful Operators

 

Hands-On: 

  • WordCount Program using Spark Streaming

8.1 Apache Spark Streaming  Data Sources

8.2 Streaming Data Source Overview

8.3 Example  Using a Kafka Direct Data Source

 

Hands-On: 

  • Various Spark Streaming Data Sources

9.1 OLAP vs OLTP

9.2 What is a Data Warehouse?

9.3 Difference between Data Warehouse, Data Lake and Data Mart

9.4 Fact Tables

9.5 Dimension Tables

9.6 Slowly changing Dimensions

9.7 Types of SCDs

9.8 Star Schema Design

9.9 Snowflake Schema Design

9.10 Data Warehousing Case Studies

10.1 Introduction to cloud computing

10.2 Types of Cloud Models

10.3 Types of Cloud Service Models

10.4 IAAS

10.5 SAAS

10.6 PAAS

10.7 Creation of Microsoft Azure Account

10.8 Microsoft Azure Portal Overview

11.1  Introduction to Azure Synapse Analytics

11.2  Work with data streams by using Azure Stream Analytics

11.3  Design a multidimensional schema to optimize analytical workloads
11.4  Code-free transformation at scale with Azure Data Factory
11.5  Populate slowly changing dimensions in Azure Synapse Analytics pipelines

11.6  Design a Modern Data Warehouse using Azure Synapse Analytics
11.7  Secure a data warehouse in Azure Synapse Analytics

12.1  Explore Azure Synapse serverless SQL pool capabilities


12.2  Query data in the lake using Azure Synapse serverless SQL pools


12.3  Create metadata objects in Azure Synapse serverless SQL pools


12.4  Secure data and manage users in Azure Synapse serverless SQL pools

13.1  Understand big data engineering with Apache Spark in Azure Synapse Analytics


13.2  Ingest data with Apache Spark notebooks in Azure Synapse Analytics


13.3  Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics


13.4  Integrate SQL and Apache Spark pools in Azure Synapse Analytics


13.5   Integrate SQL and Apache Spark pools in Azure Synapse Analytics

14.1  Describe Azure Databricks
14.2  Read and write data in Azure Databricks
14.3  Work with DataFrames in Azure Databricks
14.4  Work with DataFrames advanced methods in Azure Databricks

15.1  Use data loading best practices in Azure Synapse Analytics
15.2  Petabyte-scale ingestion with Azure Data Factory or Azure Synapse Pipelines

16.1  Data integration with Azure Data Factory or Azure Synapse Pipelines


16.2  Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines

16.3  Orchestrate data movement and transformation in Azure Data Factory or Azure Synapse Pipelines

17.1  Optimize data warehouse query performance in Azure Synapse Analytics
17.2  Understand data warehouse developer features of Azure Synapse Analytics

17.3  Analyze and optimize data warehouse storage in Azure Synapse Analytics

18.2  Configure Azure Synapse Link with Azure Cosmos DB
18.3  Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics
18.4  Query Azure Cosmos DB with SQL serverless for Azure Synapse Analytics

19.1  Secure a data warehouse in Azure Synapse Analytics
19.2  Configure and manage secrets in Azure Key Vault
19.3  Implement compliance controls for sensitive data

20.1  Enable reliable messaging for Big Data applications using Azure Event Hubs
20.2  Work with data streams by using Azure Stream Analytics
20.3  Ingest data streams with Azure Stream Analytics

21.1  Process streaming data with Azure Databricks structured streaming

22.1  Create reports with Power BI using its integration with Azure Synapse Analytics

23.1  Use the integrated machine learning process in Azure Synapse Analytics

24.1  Introduction of Airflow

24.2 Different Components of Airflow

24.3 Installing Airflow

24.4 Understanding Airflow Web UI

24.5 DAG Operators & Tasks in Airflow Job

24.6 Create & Schedule Airflow Jobs For Data Processing

25.1 Snowflake Overview and Architecture
25.2 Connecting to Snowflake
25.3 Data Protection Features
25.4 SQL Support in Snowflake
25.5 Caching in Snowflake
Query Performance
25.6 Data Loading and Unloading
25.7 Functions and Procedures
Using Tasks
25.8 Managing Security
Access Control and User Management
25.9 Semi-Structured Data
25.10 Introduction to Data Sharing
25.11 Virtual Warehouse Scaling
25.12 Account and Resource Management

Learn Projects & Assignments Handpicked byIndustry Leaders

Our tutors are real business practitioners who hand-picked and created assignments and projects for you that you will encounter in real work.

That’s what They Said

  • Stam Senior Cloud Engineer at AWS
    Amit Sharma Manager at Visa

    Enrolling in the Cloud Master Course by Prepzee was a game-changer for me. The comprehensive curriculum covering AWS, DevOps, Azure, and Python gave me a holistic understanding of cloud technologies. The hands-on labs were invaluable, and I now feel confident navigating these platforms in my career. Prepzee truly delivers excellence in cloud education. The balance between theory and practical application was perfect, allowing me to grasp complex concepts with ease. I'm grateful for the opportunity to have learned from industry experts through this course.

    Kashmira Palkar Manager - Deloitte
  • Abhishek Pareek Technical Manager Capgemini.
    I enrolled in the Cloud Master Course at Prepzee with a focus on DevOps and Azure certification. This comprehensive course provided me with a wealth of pointers and resources, equipping me to adeptly manage my ongoing projects in DevOps, Kubernetes, Terraform, and Azure. The course's insights were instrumental in leading my DevOps team effectively, while also enhancing my grasp of Azure's intricacies. The support team's responsiveness and efficient issue resolution further elevated my learning experience. I highly recommend the Cloud Master Course for anyone seeking to master the dynamic interplay of DevOps, Azure, Kubernetes, and Terraform.
    Nishant Jain Senior DevOps engineer at Encora
    Vishal Purohit Product Manager at Icertis
  • Prepzee's Cloud Master Course exceeded my expectations. The course materials were detailed and insightful, providing a clear roadmap to mastering cloud technologies. The instructors' expertise shone through their teachings, and the interactive elements made learning enjoyable. This course has undoubtedly been a valuable asset to my professional growth.

    Abhishaily Srivastva Product Manager - Amazon

    I wanted a comprehensive cloud education, and the Cloud Master Course at Prepzee delivered exactly that. The course content was rich and well-presented, catering to both beginners and those with prior knowledge. The instructors' passion for the subject was evident, and the hands-on labs helped solidify my understanding. Thank you, Prepzee!

    Komal Agarwal Manager EY

    Prepzee has been a great partner for us and is committed towards upskilling our employee.Their catalog of training content covers a wide range of digital domains and functions, which we appreciate.The best part was there LMS on which videos were posted online for you to review if you missed anything during the class.I would recommend Prepzee to all to boost his/her learning.The trainer was also very knowledgeable and ready to answer individual questions.

    Shruti Tawde HR at JM Financial Services Ltd

Data Engineer Job Oriented Program Fees

Live Online Classroom
  • 05/05/2024 - 26/08/2024
  • 8:00 pm TO 11:00 pm IST (GMT +5:30)
  • Online(Sat-Sun)
49,800.00
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12 Hours left at this price!

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Frequently Asked Questions

Enroll in our Data Engineer Job-Oriented Program and embark on a dynamic journey towards a thriving career in data engineering. This comprehensive program is designed to equip you with the skills and knowledge necessary to excel in the ever-evolving field of data engineering. Throughout this program, you'll delve into a diverse array of tools and technologies that are crucial for data engineers, including popular platforms like Databricks, Snowflake, PySpark, Azure, and Azure Synapse Analytics, among many more.

Prepzee offers 24/7 support to resolve queries. You raise the issue with the support team at any time. You can also opt for email assistance for all your requests. If not, a one-on-one session can also be arranged with the team. This session is, however, only provided for six months starting from your course date.

All instructors at Prepzee are Microsoft certified experts with over twelve years of experience relevant to the industry. They are rightfully the experts on the subject matter, given that they have been actively working in the domain as consultants. You can check out the sample videos to ease your doubts.

Prepzee provides active assistance for job placement to all candidates who have completed the training successfully. Additionally, we help candidates prepare for résumé and job interviews. We have exclusive tie-ups with reputed companies.

Projects included in the training program are updated and hold high relevance and value in the real world. Projects help you apply the acquired learning in real-world industry structures. Training involves several projects that test practical knowledge, understanding, and skills. High-tech domains like e-commerce, networking, marketing, insurance, banking, sales, etc., make for the subjects of the projects you will work on. After completing the Projects, your skills will be synonymous with months of meticulous industry experience.

Prepzee's Course Completion Certificate is awarded once the training program is completed, along with working on assignments, real-world projects, and quizzes, with a least 60 percent score in the qualifying exam.

Actually, no. Our job assistance program intends to help you land the job of your dreams. The program offers opportunities to explore competitive vacancies in the corporates and look for a job that pays well and matches your profile and skill set. The final hiring decision will always be based on how you perform in the interview and the recruiter's requirements.

You can enroll for Azure dp 203 certification.

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-Malcolm X

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"Learn as if you'll live forever."

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