Data Engineering

Python for Data Engineering

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On-site

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Python for Data Engineering

Build a strong foundation in building production-style data pipelines using Python, from ingestion to incremental processing and data quality checks. Learn how to design maintainable pipelines with logging, retries, validations, and reporting patterns used in real-world batch processing systems.

Duration:

3 days

Level:

Intermediate

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1500+ users onboarded

Who will Benefit from this Training?

Beginner to intermediate Data Engineers
Data Analysts moving into Data Engineering
Backend engineers working with data pipelines
Analytics engineers using Python + SQL
Anyone building batch pipelines and data automation scripts

Training Objectives

Use Python confidently for day-to-day data engineering tasks.
Build reliable ingestion and transformation scripts for batch pipelines.
Work with common pipeline formats including CSV, JSON, and Parquet (intro).
Connect Python pipelines to databases and data warehouses.
Implement robust transformations using pandas and hybrid SQL + Python approaches.
Build incremental ingestion logic using watermark loads and deduplication.
Handle schema changes, bad records, and late-arriving data safely.
Implement data quality checks and validations in Python.
Build modular, config-driven, production-ready pipeline code with logging, error handling, and retry patterns.
Deliver an end-to-end mini data pipeline as a capstone project.

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Key training modules

Comprehensive, hands-on modules designed to take you from basics to advanced concepts

Module 1: Python for Data Engineering (Core Skills and Workflow)
1. Python essentials for pipelines (types, functions, modules)
2. Virtual environments and dependency management basics
3. Project structure for data engineering scripts (src, configs, tests)
4. Working with time and timestamps for pipeline runs
5. Hands-on: Lab: Build a simple pipeline skeleton that reads input, processes, and writes output with proper exit codes
Module 2: Building Batch Ingestion Scripts (Reliable Data Loads)
1. Ingestion patterns (file drop, API pull, database extract concepts)
2. File discovery and run-by-date processing (partition folders)
3. Idempotency basics (safe re-runs, overwrite vs append)
4. Writing staging outputs and run metadata
5. Hands-on: Lab: Build a batch ingestion script that loads daily files into a staging area
Module 3: Working with CSV and JSON (Parsing, Schemas, Pitfalls)
1. Reading CSV safely (dtypes, delimiters, encoding, quoting)
2. Schema definition vs inference and why it matters
3. Working with JSON (nested fields, flattening, normalization)
4. Handling corrupt records and bad rows (quarantine pattern concept)
5. Hands-on: Lab: Ingest messy CSV + nested JSON, normalize fields, and write clean outputs
Module 4: Parquet Introduction (Columnar Storage for Pipelines)
1. Why Parquet (compression, column pruning, analytics performance)
2. Writing Parquet with stable schemas (types and nullability)
3. Partitioned Parquet outputs (date, region, source)
4. Small files awareness and file sizing basics
5. Hands-on: Lab: Convert cleansed CSV/JSON data into partitioned Parquet outputs
Module 5: Connecting to Databases and Warehouses (Extraction and Loading)
1. DB connectivity basics (connection strings, pooling concepts)
2. Reading with SQL and loading into pandas for transformations
3. Writing results back to PostgreSQL (upsert concepts)
4. Warehouse connectivity concepts (drivers and auth patterns overview)
5. Hands-on: Lab: Extract data from PostgreSQL, transform in Python, and load curated tables back
Module 6: Transformations with pandas and Hybrid SQL + Python
1. Core pandas transformations (filter, select, merge, groupby)
2. Date/time transformation patterns (standardize, timezone awareness concept)
3. Hybrid approach: do heavy joins/filters in SQL, refine in pandas
4. Performance basics (avoid row-wise loops, vectorization patterns)
5. Hands-on: Lab: Build a transformation step that uses SQL extraction + pandas enrichment + curated output
Module 7: Incremental Loads (Watermarks and Deduplication)
1. Full refresh vs incremental and when to choose each
2. Watermark loads (updated_at, ingestion timestamp) and storing state
3. Deduplication patterns (business keys, last-write-wins concepts)
4. Backfill and reprocessing windows for missed days
5. Hands-on: Lab: Implement incremental ingestion with a watermark and validate multiple runs
Module 8: Handling Schema Changes, Bad Records, Late-Arriving Data
1. Schema drift detection (new columns, type changes) and safe handling
2. Bad records strategy (drop, quarantine, fix-forward workflows)
3. Late-arriving data patterns (reprocess window, merge concepts)
4. Idempotent processing for retries and partial failures
5. Hands-on: Lab: Simulate schema changes and corrupt records and implement safe pipeline behavior
Module 9: Data Quality Checks and Validation in Python
1. Validation patterns (nulls, duplicates, ranges, referential checks concepts)
2. Fail-fast vs warn-only design and when to use each
3. Generating validation reports (counts, failed rows, summary)
4. Quality gates before publishing curated outputs
5. Hands-on: Lab: Add a validation stage that blocks publishing and outputs a quality report
Module 10: Production-Ready Pipeline Code (Config-Driven, Logging, Retries)
1. Config-driven pipelines (YAML/JSON + env vars + CLI args)
2. Structured logging (run_id, dataset, row counts, timing)
3. Error handling strategy (categorize errors, retries with backoff concepts)
4. Modular design (extract, transform, load layers) and testing basics
5. Hands-on: Lab: Refactor scripts into modular pipeline code with configs, logging, and retry behavior
Module 11: Capstone Project (End-to-End Mini Data Pipeline)
1. Capstone goal: Deliver a working batch pipeline with quality checks
2. Ingest CSV/JSON sources and produce partitioned Parquet staging
3. Transform using pandas + SQL and load curated tables into PostgreSQL
4. Implement incremental loads with watermark and deduplication
5. Add schema drift handling, bad record quarantine, and validation report
6. Hands-on: Capstone Lab: Deliver runnable pipeline code, configs, logs, and sample outputs with a short runbook

Hands-on Experience with Tools

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Training Delivery Format

Flexible, comprehensive training designed to fit your schedule and learning preferences

Opt-in Certifications

AWS, Scrum.org, DASA & more

100% Live

on-site/online training

Hands-on

Labs and capstone projects

Lifetime Access

to training material and sessions

How Does Personalised Training Work?

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Skill-Gap Assessment

Analysing skill gap and assessing business requirements to craft a unique program

1

Personalisation

Customising curriculum and projects to prepare your team for challenges within your industry

2

Implementation

Supplementing training with consulting support to ensure implementation in real projects

3

Why Python for Data Engineering for your business?

Faster pipeline development: Build ingestion, transformation, and automation quickly with Python ecosystems.
Better integration support: Connect APIs, databases, and cloud services with mature libraries.
Improved data quality automation: Validate, profile, and test data programmatically at scale.
Enables advanced use cases: Python unlocks ML, NLP, and automation workflows beyond SQL.
Higher team productivity: Engineers deliver more with reusable scripts and modular pipeline code.

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Frequently Asked Questions

1. What are the pre-requisites for this training?

The training does not require you to have prior skills or experience. The curriculum covers basics and progresses towards advanced topics.

2. Will my team get any practical experience with this training?

With our focus on experiential learning, we have made the training as hands-on as possible with assignments, quizzes and capstone projects, and a lab where trainees will learn by doing tasks live.

3. What is your mode of delivery - online or on-site?

We conduct both online and on-site training sessions. You can choose any according to the convenience of your team.

4. Will trainees get certified?

Yes, all trainees will get certificates issued by Uptut under the guidance of industry experts.

5. What do we do if we need further support after the training?

We have an incredible team of mentors that are available for consultations in case your team needs further assistance. Our experienced team of mentors is ready to guide your team and resolve their queries to utilize the training in the best possible way. Just book a consultation to get support.

Python for Data Engineering

Who will Benefit from this Training?

Training Objectives

Build a high-performing, job-ready tech team.

Key training modules

Hands-on Experience with Tools