Python for Data Engineering
An advanced, job-oriented course in Python for data engineering. You learn the data engineer role, Git, Docker and a reproducible lab, then write clean, tested Python with logging and error handling, advanced SQL, Pandas and PySpark. You build ETL and ELT pipelines with API integration, incremental loading, schema validation and data quality checks, orchestrate them with Apache Airflow and design data warehouses and data lakes. You work with BigQuery and Snowflake in the cloud, learn streaming fundamentals, monitoring and debugging, and cover security, data governance and cost optimization. You use AI coding assistants for development, debugging, documentation and SQL optimization while validating every output yourself. You finish with four portfolio projects, an end-to-end capstone, interview preparation and practical assignments. All data is simulated.
- Duration: 16 weeks
- Fee: Rs 19,999 (about Rs 3,333 a month on EMI)
- Field: Data & Analytics
Roles this track prepares you for
- Data Engineer
- ETL Developer
- Analytics Engineer
- Junior Data Engineer
- Data Pipeline Developer
- Cloud Data Engineer
- BI Developer
- Data Platform Engineer
- Big Data Developer
- Data Warehouse Developer
Curriculum
- Data Engineering Foundations, Git, Docker and Lab Readiness - Understand the data engineer role and the data lifecycle, compare ETL and ELT architectures, set up a reproducible lab with Python, a local database, Git and Docker, and learn secrets, security and AI assistant rules from the first day.
- The data engineer role and the data lifecycle
- ETL, ELT and common pipeline architectures
- Lab setup, Python, VS Code and a local database
- Git and GitHub workflow for pipeline projects
- Docker basics for a reproducible lab
- Secrets, security basics and AI assistant rules for data engineering
- Python for Data Engineering - Write clean, modular Python for pipelines with data structures, packages, file formats, error handling, logging, retries, configuration, type hints, tests and debugging.
- Python data structures and functions for pipelines
- Modules, packages and clean project structure
- Reading and writing CSV, JSON and Parquet
- Error handling, logging and retries
- Configuration, type hints and environment variables
- Testing with pytest and debugging broken code
- SQL for Data Engineering - Use advanced SQL on pipeline data, including joins, CTEs, window functions for ranking and deduplication, keys and constraints, indexes, query plans, transactions and upserts, and tune slow queries.
- Advanced joins, CTEs and subqueries on pipeline data
- Window functions for ranking and deduplication
- Keys, constraints and relational modeling
- Indexes, query plans and performance
- Transactions, upserts and Python with SQL
- Reading query plans and tuning slow queries
- Data Processing with Pandas - Clean and transform data with Pandas, validate schemas, handle dates, text and missing data, process files too large for memory in chunks and build a tested ETL script.
- DataFrames, selection and cleaning
- Joins, groupby and reshaping
- Dates, text and missing data
- Data types and schema validation
- Performance, memory and chunked processing
- A tested Pandas ETL script
- PySpark and Distributed Processing - Learn how Spark runs a job, write PySpark DataFrame code with joins, aggregations and windows, use Parquet and partitioning, find and reduce shuffles and skew, and build a complete PySpark ETL job.
- Why Spark, how it runs, partitions and lazy evaluation
- DataFrames, transformations and actions
- Joins, aggregations and window functions in Spark
- Parquet, partitioning and schemas
- Shuffles, caching, skew and performance tuning
- Spark SQL and a complete PySpark ETL job
- ETL/ELT Pipelines, API Integration, Incremental Loading and Data Quality - Design pipelines that are safe to rerun, integrate APIs with pagination, rate limits and retries, load incrementally with watermarks, validate schemas and data contracts, run data quality checks with quarantine and reconciliation, and build a production-style pipeline. This module includes portfolio project 1.
- Pipeline design, idempotency and safe reruns
- API integration, pagination, rate limits and retries
- Incremental loading, watermarks and change data capture basics
- Schema validation and data contracts
- Data quality checks, quarantine and reconciliation
- Building a production-style incremental pipeline
- Apache Airflow and Data Orchestration - Learn orchestration concepts and the Airflow architecture, build DAGs with tasks and dependencies, schedule and backfill runs, design idempotent tasks, handle retries, alerts and failures, and orchestrate a full pipeline with Airflow in Docker. This module includes portfolio project 2.
- Orchestration concepts and Airflow architecture
- DAGs, tasks, operators and dependencies
- Scheduling, backfills and catchup
- Passing data between tasks and idempotent task design
- Retries, alerts, SLAs and failure handling
- Orchestrating a full pipeline with Airflow in Docker
- Data Warehousing, Data Modeling and Data Lake Architecture - Learn warehouse concepts, dimensional modeling with star schemas, slowly changing dimensions, data lake and lakehouse layers, file formats and partitioning, and analytics engineering with modeling layers and tested SQL transformations.
- Data warehouse concepts, OLTP versus OLAP
- Dimensional modeling, facts, dimensions and star schemas
- Slowly changing dimensions and history
- Data lake, lakehouse and medallion layers
- File formats, partitioning and storage design
- Analytics engineering, modeling layers and tested SQL transformations
- Cloud Data Engineering with BigQuery and Snowflake - Learn cloud data engineering basics, load and query data in BigQuery with partitioning and clustering, work with Snowflake stages, warehouses and cost control, and deploy a pipeline to the cloud and clean it up. This module includes portfolio project 3.
- Cloud data engineering basics, storage, compute and cost
- Cloud storage and loading data into BigQuery
- BigQuery SQL, partitioning and clustering
- Snowflake architecture, stages and loading
- Snowflake queries, warehouses and cost control
- Deploying a pipeline to the cloud and cleaning up
- Streaming Fundamentals, Monitoring and Pipeline Reliability - Learn the difference between batch and streaming, process a simulated event stream with windows, monitor pipelines with metrics and logging, track data freshness, debug production failures and write runbooks and root cause notes.
- Batch versus streaming, events and windows
- Structured streaming with a simulated source
- Pipeline monitoring, metrics and logging
- Data freshness, alerting and observability
- Debugging production failures
- Runbooks, incident response and root cause notes
- AI-Assisted Engineering, Security, Governance and Cost Optimization - Use AI assistants to develop, debug, document and optimize SQL while validating every output with tests and query plans, and review pipelines for security, access control, privacy, lineage, governance and cloud cost.
- AI-assisted pipeline development with independent validation
- AI for debugging and documentation
- AI for SQL optimization, checked with query plans
- Security, secrets, access control and least privilege
- Data governance, privacy, lineage and catalogs
- Cost optimization and cloud resource cleanup
- Capstone, Portfolio, Interview Preparation and Role Assignments - Deliver one end-to-end, tested and monitored data pipeline from ingestion to a cloud warehouse, then prepare your portfolio, interview answers and practical assignments for Data Engineer, ETL Developer and Analytics Engineer roles. This module includes portfolio project 4.
- Capstone brief, architecture and plan
- Capstone build, ingestion, transformation and orchestration
- Capstone testing, quality, monitoring and deployment
- Portfolio case study, README and GitHub profile
- Interview preparation for Data Engineer, ETL Developer and Analytics Engineer roles
- Practical assignments, system design and take-home tasks
Frequently asked questions
How long is the Python for Data Engineering course?
It runs for 16 weeks. It is self-paced online learning from EasySkill Career Academy, Surat.
How much does the Python for Data Engineering course cost?
The fee is Rs 19,999, or about Rs 3,333 a month on EMI.
What will I build in Python for Data Engineering?
Every module ends in a graded practical task that AI checks in about 30 seconds, so you finish with a portfolio you can show to employers or clients.
Which roles does Python for Data Engineering prepare me for?
It prepares you to apply for roles such as Data Engineer, ETL Developer, Analytics Engineer, Junior Data Engineer, Data Pipeline Developer. It does not guarantee a job or income.
Can I learn online, or do I have to visit a centre?
You can learn fully online. EasySkill also has centres in Adajan, Vesu and Bhestan, Surat, if you prefer to visit.
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