Data Engineer Job Oriented Program
Jenny
Cloud Engineer at Walmart Labs
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Prepzee's Cloud Masters program changed my career from SysAdmin to Cloud Expert in just 6 months. Thanks to dedicated mentors, I now excel in AWS, Terraform, Ansible, and Python.
Vishal Purohit
Product Manger at Icertis Hear My StoryGreat learning experience through the platform. The curriculum is updated and covers all the topics. The trainers are experts in their respective fields and follow more of a practical approach.
Aman Yadav
Senior Associate Product at Amazon Hear My StoryNice experience, I will recommend it to all the learners who are willing to join and learn IT skills. I was able to switch my domain from non-IT to IT in a reputed MNC
You’re an IT Professional who is looking for a career in Data Engineering especially dealing with Cloud-based solutions.
You’re looking to switch domains into the Future Proof Data Industry without going into Statistics and coding may start in Data Engineering.
You’re a DBA, with experience in database management and SQL, and can transition into data engineering roles with ease.
You’re a Data Analyst/ Scientist who wants to work with data at a larger scale and manage data pipelines that may transition into data engineering.
Including Top 3 Data Engineering Tools according to Linkedin Jobs
Learn by doing multiple labs in your learning journey.
Get a feel of Data Engineering professionals by doing real-time projects.
Call us, E-Mail us whenever you stuck.
Instructors are Microsoft Certified Trainers.
Attend multiple batches until you achieve your Dream Goal.
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
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
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)
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
14+ Years of experience
15+ Years of experience
12+ Years of experience
17+ Years of experience
The primary role involves designing, building, and maintaining data pipelines and infrastructure to support data-driven decision-making.
Responsible for integrating data from various sources, ensuring data quality, and creating a unified view of data for analysis.
Designing and managing data warehouses for efficient data storage and retrieval, often using technologies like Databricks, Snowflake and Azure.
Specializing in data engineering within cloud platforms like AWS, Azure leveraging cloud-native data services.
Providing expertise to organizations on data-related issues, helping them make informed decisions and optimize data processes.
Designing the overall data architecture for an organization, including data models, storage, and access patterns.
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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:
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:
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:
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:
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:
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:
8.1 Apache Spark Streaming Data Sources
8.2 Streaming Data Source Overview
8.3 Example Using a Kafka Direct Data Source
Hands-On:
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
Our tutors are real business practitioners who hand-picked and created assignments and projects for you that you will encounter in real work.
Ingest data into a data lake and apply PySpark for data integration, transformation, and optimization. Create a system that maintains a structured data repository within a data lake to support analytics
Create a robust data warehousing solution in Snowflake for a retail company. Ingest and transform sales data from various sources, enabling advanced analytics for inventory management and sales forecasting.
Build a comprehensive ETL (Extract, Transform, Load) pipeline that automates the extraction, transformation, and loading of data into a data warehouse. Implement scheduling, error handling, and monitoring for a robust ETL process.
Perform standard DataFrame methods to explore and transform data.Key Points:Create a lab environment. Azure Databricks cluster.
The project includes loading data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. Use workload management and Copy activity in an Azure Synapse pipeline for petabyte-scale data ingestion.
The project revolves around building data integration pipelines to ingest from multiple data sources, transforming data using mapping data flows and notebooks, and performing data movement into one or more data sinks.
Enrolling in the AWS Data Engineer Job Oriented Program by Prepzee for the AWS Data Engineer certification (DEA C01) was transformative. The curriculum covered critical tools like PySpark, Python, Airflow, Kafka, and Snowflake, offering a complete understanding of cloud data engineering. The hands-on labs solidified my skills, making complex concepts easy to grasp. With a perfect balance between theory and practice, I now feel confident in applying these technologies in real-world projects. Prepzee's focus on industry-relevant education was invaluable, and I’m grateful for the expertise gained from industry professionals.
I enrolled in the DevOps Program at Prepzee with a focus on tools like Kubernetes, Terraform, Git, and Jenkins. This comprehensive course provided valuable resources and hands-on labs, enabling me to efficiently manage my DevOps projects. The insights gained were instrumental in leading my team and streamlining workflows. The program's balance between theory and practice enhanced my understanding of these critical tools. Additionally, the support team’s responsiveness made the learning experience smooth and enjoyable. I highly recommend the DevOps Program for anyone aiming to master these essential technologies.
Enrolling in the Data Engineer Job Oriented Program at Prepzee,, exceeded my expectations. The course materials were insightful and provided a clear roadmap for mastering these tools. The instructors' expertise and interactive learning elements made complex concepts easy to grasp. This program has been invaluable for my professional growth, giving me the confidence to apply these technologies effectively in real-world projects.
Enrolling in the Data Analyst Job Oriented Program at Prepzee, covering Python, SQL, Advanced Excel, and Power BI, was exactly what I needed for my career. The course content was well-structured and comprehensive, catering to both beginners and experienced learners. The hands-on labs helped reinforce key concepts, while the Prepzee team’s support was outstanding, always responsive and ready to help resolve any issues.
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.
08/12/2024 - 20/04/2025
8:00 pm TO 11:00 pm IST (GMT +5:30)
Online(Sat-Sun)
12 Hours left at this price!
Get Certified after completing this course with Prepzee
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Data Transformation
