MASTER DATA ENGINEERING & AI DATA PLATFORM
Build Reliable Data Pipelines and AI-Ready Data Platforms
Learn how to collect, transform, store, validate and deliver data for analytics, business applications and AI systems.
Understand the complete journey from raw data and operational databases to automated pipelines, cloud data platforms, governed data products and AI-ready datasets.
Core Technology Stack: Python • SQL • Data Modelling • ETL/ELT • Apache Airflow • Apache Spark • dbt • Data Warehousing • Lakehouse • Cloud • Data Quality • Governance
- 6/8 Months
- 300+ Practical Hours
- Live Online Training
- Industry Practice Labs
What You Will Learn
Python, SQL & Data Engineering Foundations
Build the Foundations for Working with Real-World Data
Learn the programming, database and data-processing concepts required to develop reliable data pipelines.
- Python for data processing
- SQL queries, joins and aggregations
- Window functions and subqueries
- Relational databases and data storage
- CSV, JSON and Parquet data formats
- Data types and data modelling fundamentals
- Git and version-controlled development
- Testing, debugging and error handling
Understand how data is represented, accessed, processed and prepared for downstream systems.
Data Ingestion & ETL/ELT Pipelines
Move Data Reliably from Source Systems to Data Platforms
Learn how to collect data from multiple sources and prepare it for storage, transformation and analysis.
- File, database and API ingestion
- Batch data-processing workflows
- ETL versus ELT architecture
- Incremental data loading
- Change Data Capture concepts
- Schema handling and data type conversion
- Duplicate records and late-arriving data
- Retries, error handling and pipeline logging
Technology Stack: Python • SQL • APIs • PostgreSQL • Object Storage • ETL/ELT Patterns
Data Modelling & Data Warehousing
Organise Data for Analytics and Business Applications
Understand how raw data becomes structured, consistent and useful business information.
- Relational and dimensional modelling
- Fact tables and dimension tables
- Star and snowflake schemas
- Data warehouse architecture
- Data marts and analytical datasets
- Slowly changing dimensions
- Query performance and partitioning concepts
- Business metrics and consistent definitions
Understand the flow: Source Data → Staging → Transformation → Data Warehouse → Reports and Applications
Workflow Orchestration & Pipeline Automation
Automate Repeatable Data Engineering Workflows
Learn how to schedule, coordinate and monitor data pipelines rather than executing every step manually.
- Workflow orchestration fundamentals
- Directed Acyclic Graphs (DAGs)
- Task dependencies and scheduling
- Apache Airflow fundamentals
- Pipeline parameters and configuration
- Retries, backfills and failure handling
- Logging, alerts and task monitoring
- Automated pipeline testing
Technology Stack: Apache Airflow • Python • SQL • Git • Scheduling and Monitoring Tools
Distributed Processing & Modern Data Platforms
Process Larger Datasets with Modern Data Technologies
Learn the fundamentals of distributed data processing and understand how modern data platforms are designed.
- Distributed data-processing concepts
- Apache Spark and PySpark fundamentals
- DataFrames and transformations
- Partitioning and shuffle concepts
- Data lakes and data warehouses
- Lakehouse architecture
- Parquet and open data formats
- Batch and streaming data concepts
Technology Stack: Apache Spark • PySpark • Parquet • Object Storage • Lakehouse Concepts
Cloud Data Engineering
Build Data Workflows on Cloud Platforms
Understand how cloud infrastructure supports scalable data storage, processing and analytics.
- Cloud data engineering architecture
- Object storage and data lake foundations
- Managed ingestion and transformation services
- Cloud data warehouse concepts
- Identity and access management fundamentals
- Environment configuration and secrets
- Storage and compute cost considerations
- Deployment across development and production environments
Technology Stack: AWS • Microsoft Azure • Google Cloud • Cloud Storage • Managed Data Services
Data Quality, Governance & Observability
Make Data Trustworthy, Discoverable and Reliable
Learn how to identify data problems, monitor pipelines and maintain confidence in the information delivered to business applications.
- Data validation and quality rules
- Completeness, freshness and consistency checks
- Schema evolution and data contracts concepts
- Data lineage and metadata
- Data cataloguing and discoverability
- Access control and sensitive-data handling
- Pipeline monitoring and alerting
- Root-cause analysis for data incidents
Understand why reliable data requires quality checks, governance, lineage and monitoring—not pipelines alone.
AI-Ready Data & Data Platform Engineering
Prepare Data Foundations for Analytics and AI Applications
Learn how to prepare and deliver reliable data that can be used by analytics systems, machine learning workflows and AI applications.
- Structured and unstructured data preparation
- Reusable and documented data products
- Data access patterns for AI applications
- Dataset preparation for model training and evaluation
- Business definitions and semantic context
- Data platform integration patterns
- Continuous improvement of data products
Technology Stack: Python • SQL • Cloud Data Platforms • Data Quality Tools • AI Data Preparation
Hands-On Industry Practice
Ingest. Transform. Validate. Orchestrate. Deliver.
Practise the end-to-end data engineering workflow required to turn raw source data into a reliable, documented and AI-ready data product.
Define the Data Use Case & Inspect Sources
- Choose a practical business reporting or application requirement.
- Identify source files, database tables or APIs.
- Inspect sample records, fields and data types.
- Define expected outputs and data freshness requirements.
- Document assumptions and acceptance criteria.
Build the Data Ingestion Pipeline
- Connect to files, databases or an API.
- Load source data into a landing or staging area.
- Handle malformed records and missing fields.
- Implement retries and error-handling logic.
- Record ingestion status, errors and record counts.
Clean & Transform Data
- Use Python and SQL to clean source data.
- Standardise data types, formats and business values.
- Apply joins, filters and aggregations.
- Create reusable transformation logic.
- Validate transformed outputs against expected results.
Model Data for Analytics
- Design fact and dimension tables where appropriate.
- Create analytical tables or data marts.
- Apply consistent business metric definitions.
- Test keys, relationships and duplicate handling.
- Compare query results against business expectations.
Orchestrate the Data Pipeline
- Create a workflow with dependent tasks.
- Schedule ingestion and transformation steps.
- Configure retries, backfills and failure handling.
- Add pipeline logs, monitoring and alerts.
- Run the workflow and investigate a simulated failure.
Process Data at Scale & Use Cloud Storage
- Store data using an appropriate file and folder structure.
- Work with columnar formats such as Parquet.
- Explore partitioning and distributed-processing concepts.
- Compare data lake, warehouse and lakehouse use cases.
- Document storage, compute and access decisions.
Implement Data Quality & Governance
- Define completeness, validity and freshness checks.
- Detect schema changes and unexpected values.
- Record data lineage and pipeline metadata.
- Apply access controls to sensitive data.
- Generate a quality report and investigate failed checks.
Publish an AI-Ready Data Product
- Prepare a clean and documented dataset for downstream use.
- Create a repeatable data-refresh workflow.
- Make curated data available for analytics or an AI application.
- Monitor data freshness, pipeline failures and quality.
- Deliver documentation, validation results and a technical handover.
Data Engineering & AI Data Platform
What the Job Market Is Telling Us
Companies need engineers who can build reliable data pipelines, manage cloud data platforms, maintain data quality and deliver trusted data for analytics and production AI.
Tools & Technologies
Programming
Python • SQL • Git
Databases & Storage
PostgreSQL • Relational Databases • CSV • JSON • Parquet • Object Storage
Data Integration
APIs • ETL/ELT • Incremental Loads • Change Data Capture Concepts
Transformation & Modelling
SQL • Python • Dimensional Modelling • dbt Concepts
Workflow Orchestration
Apache Airflow • DAGs • Scheduling • Retries • Backfills
Distributed Processing
Apache Spark • PySpark • Partitioning Concepts
Data Platforms
Data Lakes • Data Warehouses • Lakehouse Architecture
Cloud
AWS • Microsoft Azure • Google Cloud
Data Quality & Governance
Data Validation • Data Lineage • Metadata • Access Control
AI Data Foundations
Structured and Unstructured Data • Dataset Preparation • AI-Ready Data Products
Who is this Program For?
Aspiring Data Engineers
Build the foundational skills required to develop, test and operate data pipelines.
Python & SQL Developers
Extend your programming and database knowledge into production-oriented data engineering workflows.
Data Analysts & BI Professionals
Understand the engineering processes behind reliable analytical datasets, warehouses and reporting systems.
Cloud, Software & AI Learners
Learn how data infrastructure supports modern applications, business analytics and AI systems.
Your Outcome
Build Skills for Data Engineering & AI Data Platforms
By the end of the program, learners should gain practical exposure to:
Career Direction: Data Engineer • Cloud Data Engineer • ETL/ELT Developer • Analytics Engineer • Data Platform Engineer • Big Data Engineer, depending on prior experience and individual role requirements.
Why Primebit Solution?
17 Years of Training Experience
Learn with an established training company with 17 years of experience in technical education.
In-Depth Technical Training
Build a strong understanding of concepts, tools and implementation through detailed, step-by-step teaching.
100% Practical Lab-Based Learning
Apply your knowledge through hands-on labs, real-world workflows and practical implementation exercises.
Career & Placement Support
Get career guidance, interview preparation and placement assistance for relevant national and international opportunities.
Global Alumni & Career Opportunities
Connect with a learning community spanning the USA, UK, India, Dubai, Canada, Australia and other regions.
Global Companies with Relevant Data Engineering & Data Platform Roles
Google, Microsoft, Amazon, AWS, Databricks, Snowflake, IBM, Oracle, NVIDIA, Salesforce, Adobe, Walmart Global Tech, Uber, Netflix, JPMorgan Chase, Accenture, Deloitte, Capgemini, Infosys, TCS.
These companies operate in areas related to data engineering, analytics, cloud data platforms, enterprise software or AI infrastructure. Actual roles and requirements vary by team and location.
PROGRAM AT A GLANCE
Program Name
Data Engineering & AI Data Platform
Learning Journey
Python & SQL → Data Ingestion → ETL/ELT → Data Modelling → Workflow Orchestration → Distributed Processing → Cloud Data Platforms → Data Quality & Governance → AI-Ready Data
Core Skills
Data Engineering • SQL • Python • Pipeline Automation • Cloud Data Platforms • Data Quality
Technology Stack
Python • SQL • Apache Airflow • Apache Spark • dbt Concepts • AWS • Microsoft Azure • Google Cloud • Data Warehousing • Lakehouse
Practical Focus
Data ingestion • Transformation • Pipeline orchestration • Data validation • Cloud storage • AI-ready datasets
Delivery Mode
Live online training with guided practical sessions.
Duration
6/8 Months
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Frequently Asked Questions
Learners should build their Python and SQL foundations before progressing to more advanced data engineering workflows. The learning journey should introduce these fundamentals before moving into pipeline automation and cloud data platforms.
A data engineer builds and maintains systems that collect, transform, validate, store and deliver reliable data for analytics, business applications and AI.
Yes. Python and SQL are core foundations for data processing, transformation, validation and pipeline development.
The learning path covers cloud data architecture and data-platform concepts across AWS, Microsoft Azure and Google Cloud.
The hands-on practice roadmap covers data ingestion, transformation, modelling, orchestration, quality checks and delivery of a curated data product.
Yes. The program introduces AI-ready data preparation, structured and unstructured data, and the role of reliable data products in supporting AI applications.
Build Pipelines. Deliver Trusted Data. Enable AI.
BUILD YOUR CAREER IN DATA ENGINEERING & AI DATA PLATFORMS
Develop practical skills across data ingestion, transformation, modelling, automation, cloud data platforms and data quality.
Learn how reliable data systems support business analytics, modern applications and AI workloads.
PRIMEBIT SOLUTION
- Data Engineering
- AI Data Platforms
- Pipeline Automation
- Cloud Data Lakehouse
- Enterprise Data Governance
Build the data pipelines. Master the modern data stack. Shape the future of AI-driven enterprises.