Data Engineering Bootcamp

Course Description

The high demand of data engineering, both locally and internationally, has prompted atomcamp x data pilot to launch its data engineering bootcamp. This Bootcamp is project and offers hands-on exercises in tools like Hadoop, Apache Kafka, and AWS.

In our 4-month Data Engineering bootcamp, you’ll go from mastering the basics like data pipelines and ETL, to specialized skills in Big Data and cloud computing. Each week tackles a critical aspect of data engineering. The course culminates in a capstone project, ensuring you’re fully equipped to launch a global career in creating and managing sophisticated data architectures.

Course Curriculum

Introduction to Data Engineering

Overview of Data Engineering and its Role in the Data Lifecycle
Understanding Data Pipelines and ETL (Extract, Transform, Load) Processes
Introduction to Data Modeling and Data Integration Tools
Setting Up the Development Environment and Tools for Data Engineering
Understanding CDC (Change Data Capture) and SCD (Slowly Changing Dimensions)
Hands-On Exercises on Implementing CDC and SCD

Data Modeling, Data Warehousing, and Dimensional Modeling

Concepts of Data Modeling and Schema Design
Relational Database Management Systems (RDBMS), SQL, and ACID Properties
Introduction to Scoped Source Systems and Identifying Key Business Functions
Understanding Data Warehousing Concepts and Architecture
Introduction to Dimensional Modeling, Snowflake, Star Schema, Data Vault Design, and Data Marts
Overview of Data Warehouse Technologies (e.g., Snowflake, Redshift, BigQuery)
Data Modeling for Various Data Architectures
Hands-On Exercises on Building Data Warehouses and Data Marts

Data Integration and Transformation

Techniques for Data Integration and Data Quality Assurance
Data Extraction from Various Sources (Databases, APIs, Web Scraping)
Data Transformation and Cleansing Using ETL Tools (Python, Fivetran, Dataddo, Talend, Informatica)
Hands-On Exercises on Data Integration and Transformation

Data Wrangling

Process of Cleaning, Restructuring, and Enriching Raw Data
Handling Missing Values, Duplicates, Correcting Data Types, and Standardizing Data
Reshaping, Pivoting, and Aggregating Data
Combining Multiple Datasets Efficiently
Parsing JSON, XML, and Other Unstructured Data Formats
Hands-On Exercises on Data Wrangling Using SQL and Python

Cloud Computing and Data Engineering

Overview of Cloud Computing Platforms (e.g., AWS, Azure, GCP)
Cloud-Based Data Storage and Processing Services (e.g., S3, Redshift, BigQuery)
Leveraging Cloud Platforms for Scalable and Efficient Data Engineering
Hands-On Exercises on Deploying Data Engineering Solutions on the Cloud Using AWS Lambda, GCP Cloud Functions, Pub/Sub, Azure Data Factory, and Databricks

Data Streaming and Real-Time Analytics

Introduction to Real-Time Data Streaming and Event-Driven Architectures
Processing Streaming Data Using Tools Like Apache Kafka
Real-Time Analytics Using Technologies Like Apache Spark Streaming or Apache Flink
Hands-On Exercises on Working with Streaming Data and Real-Time Analytics

Data Governance and Security

Introduction to Data Governance and Its Importance in Data Engineering
Implementing Data Access Controls and Encryption Techniques
Hands-On Exercises on Data Governance and Security Practices Using Open Metadata

Workflow Orchestration and Automation

Workflow Orchestration Tools (e.g., Apache Airflow, Mage AI)
Building and Managing Data Pipelines Using Workflow Automation
Comparison of Scheduling Techniques Across AWS, GCP, Azure, and Databricks
Monitoring and Error Handling in Data Engineering Workflows
Hands-On Exercises on Workflow Orchestration and Automation

Performance Optimization and Scalability

Techniques for Optimizing Data Processing Performance
Indexing Strategies for Relational and NoSQL Databases
Scaling Data Pipelines and Distributed Computing for Efficient Processing
Hands-On Exercises on Performance Optimization and Scalability

Project Work and Final Assessment

Data Engineering Project Incorporating All Concepts Learned
Guidance and Mentorship Provided by the Instructor
Project Presentation and Evaluation

STANDARD MONTHLY

PKR 20,000 /per month

For 4 Months

LUMPSUM

PKR 70,000 Advanced

For 4 Months

Pre-requisites for Bootcamp:

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Learning Objectives

Foundations of Data Engineering: Participants will gain knowledge of the role of data engineering and will be able to differentiate between data pipelines, ETL processes, and their significance.
Data Technologies and Tools: Participants will be able to navigate big data technologies, including Hadoop ecosystem components, and implement data integration, transformation, and extraction techniques.
Data Warehousing and Architecture: Participants will be able to comprehend data warehousing architecture and develop data models.They will gain practical experience with data warehouse technologies and dimensional modeling.
Real-time Data Processing and Cloud Computing: Participants will explore real-time data streaming and event-driven architectures and utilize cloud computing platforms for storage, processing, and analytics.They will implement data access controls and security measures.
Workflow Automation and Impactful Insights: Participants will employ workflow orchestration tools for data pipeline automation, create impactful data visualizations using industry-standard tools and optimize performance and scalability of data processing pipelines.