Apache Spark & PySpark
Process terabytes without melting the cluster — or the budget. Build production data pipelines on Spark 4 and Databricks, from first DataFrame to streaming lakehouse. The data engineering foundation of our catalogue — the pipelines that feed everything else — built for fresh IT graduates and working professionals alike.
The Apache Spark & PySpark program is a live, lab-heavy course in building the data pipelines that feed everything else — analytics, ML models, and every AI system in our catalogue. Models are only as good as the data pipeline behind them. And unlike the fast-churning AI stack, Spark's core has been stable for a decade: what you build here stays valuable. Every module ends with a pipeline you shipped, tuned, or rescued.
This is a current-release course, not a rehash of 2017 tutorials: Spark 4.x where RDDs are deprecated and DataFrames / Spark SQL are the primary interface (RDDs appear only as background), Adaptive Query Execution on by default, Spark Connect for developing locally against a remote cluster, open table formats — Delta Lake, Apache Iceberg, Apache Hudi — as the normal storage layer, and Databricks, the platform most job ads now name directly instead of saying "Spark".
It is designed for two audiences at once. If you are a fresh IT graduate, Python plus SQL is the shortest on-ramp into data engineering, and you graduate with a portfolio of real pipelines. If you are a working professional — developer, analyst, ETL or BI specialist, or a data scientist whose work needs to run at production scale — this course converts what you know into lakehouse-era engineering. The program runs 10 weeks (~75 hours), live online, closing with a batch + streaming lakehouse capstone you design, build and defend.
Where This Fits in Our Catalogue
- Apache Spark & PySpark (this course) — the data engineering foundation: the pipelines that feed everything else.
- MLOps Engineering — the natural next step: operate the models your pipelines feed, at production scale and cost.
- Generative AI & LLM Engineering and AI Agent Development — the AI systems downstream of your data: they are only as good as the pipeline behind them.
When NOT to Use Spark
For a few GB a day, a warehouse or a single-node engine is the right answer — reaching for a cluster reflexively is how budgets melt. Knowing that boundary is what separates an engineer from a tool operator, and reasoning about it out loud is a common senior-level interview question. This course teaches the boundary explicitly: you will learn to justify not using Spark as confidently as using it.
Course at a Glance
- Duration: 10 weeks | ~75 hours | live + hands-on
- Format: Live online · lab-heavy · portfolio of real pipelines
- Prerequisite: Working Python and comfortable SQL. No Hadoop, Scala or distributed-systems background required.
- Curriculum: 10 modules, from distributed foundations to MLlib and capstone
- Certification: Aligned to the Databricks Certified Associate Developer for Apache Spark (Python)
Course Highlights
- Every module leads with what you build and ship, not what you "learn about"
- Spark 4.x throughout: DataFrames first, RDDs as background only, AQE on by default
- Delta Lake, Iceberg and Hudi as the normal storage layer — the lakehouse, not HDFS-era Hadoop
- Structured Streaming from Kafka to Delta with watermarking and exactly-once guarantees
- A dedicated performance-rescue lab: cut a skewed job's runtime with before/after plans
- Databricks in depth — the name most job ads use — plus EMR, Dataproc and Azure
- Production engineering: testing, CI/CD, orchestration, idempotent restartable jobs
Hands-on Projects & Portfolio
Multi-hundred-GB raw ingest → cleaned → aggregated on Delta Lake, bronze/silver/gold.
MERGE upserts, late-arriving data handling and a data quality gate.
Real-time pipeline with watermarking and exactly-once guarantees.
Take a deliberately slow, skewed job and cut its runtime, with before/after execution plans and cost.
Airflow orchestration with tests, CI/CD and failure alerting.
EMR / Dataproc / Databricks deployment with autoscaling and cost analysis.
Tools & Stack
Python PySpark Spark SQL Apache Spark 4.x Spark Connect Delta Lake Apache Iceberg Apache Kafka Databricks Unity Catalog Apache Airflow Parquet AWS EMR S3 GCP Dataproc Azure Databricks Docker Git
Certification Alignment
- Syllabus mapped to the Databricks Certified Associate Developer for Apache Spark (Python)
- Covers substantial ground for the Databricks Certified Data Engineer Associate
- Presented as preparation and alignment — no official partnership or accreditation implied, and no exam outcome guaranteed
Career Outcomes
Spark remains the default engine wherever there's real data volume, messy pipelines, lakehouse tooling or cloud platforms — and Python plus SQL is the shortest on-ramp into it. Many job ads now say "Databricks" rather than "Spark," which is why this course covers both. It targets:
- Data Engineer
- Big Data Engineer
- Analytics Engineer
- Databricks Engineer
- ETL Developer
- Data Platform Engineer
- Machine Learning Engineer (data-heavy)
- Senior Data Analyst working beyond warehouse scale
Who This Course Is For
- Final-year students and fresh IT graduates targeting data engineering roles
- Python developers and software engineers moving into data engineering
- SQL-strong data analysts and BI professionals hitting the limits of warehouse-only tooling
- ETL and ODI/Informatica-style developers modernising onto the lakehouse
- Data scientists who need their work to run on production-scale data
- Engineers preparing for Databricks certification
Material Includes
- Class recordings
- Lab guides & pipeline starter repos
- Practice datasets at realistic scale
- Certificate
Prerequisites
- Working Python
- Comfortable SQL
- No Hadoop, Scala or distributed-systems background required
Estimated Time: 7 Hours
You ship:- Your first distributed job, traced end-to-end through the Spark UI — jobs, stages, tasks and shuffles annotated by hand
- Why single-machine processing breaks; the Spark execution model; driver and executors
- Jobs/stages/tasks; lazy evaluation; partitions and shuffles
- Cluster managers (YARN, Kubernetes, Databricks); reading the Spark UI from day one
Estimated Time: 9 Hours
You ship:- A multi-source transformation pipeline — joins, window functions, nested types — written in the API you'll use for 80% of real work, with explicit schemas throughout
- Reading and writing every common format; explicit schemas instead of inference
- Transformations and actions; joins; window functions; aggregations; complex and nested types
- Mixing SQL with DataFrame code cleanly
Estimated Time: 7 Hours
You ship:- An incremental load pipeline with upserts, deduplication and late-arriving data handling — guarded by a data quality layer that fails loudly instead of silently
- Semi-structured JSON and XML; nested flattening; null and type handling
- Deduplication; slowly changing dimensions; incremental loads and upserts; late-arriving data
- A data quality layer that fails loudly
Estimated Time: 9 Hours
You ship:- A performance rescue: a deliberately slow, skewed job cut down to size, documented with before/after execution plans and cost
- Catalyst optimizer and Tungsten; reading execution plans; Adaptive Query Execution
- Partition sizing; repartition vs coalesce; broadcast joins; caching and persistence levels
- Diagnosing and fixing data skew; avoiding Python UDFs in favour of built-ins; Pandas UDFs with Arrow when you truly need them
Estimated Time: 8 Hours
You ship:- A transactional Delta Lake layer with MERGE upserts, schema evolution, time travel rollback and an OPTIMIZE/compaction routine
- ACID transactions on object storage; schema enforcement and evolution; MERGE for upserts
- Time travel and rollback; OPTIMIZE, Z-ordering and compaction; vacuum
- How Apache Iceberg and Hudi compare and when each is chosen
Estimated Time: 8 Hours
You ship:- A real-time pipeline from Kafka into Delta with watermarking, stateful aggregations, exactly-once semantics and checkpointed recovery
- The same DataFrame API applied to real-time data; Kafka sources and sinks; micro-batch vs continuous
- Event time vs processing time; watermarking; stateful aggregations
- Exactly-once semantics; checkpointing and recovery; streaming into Delta
Estimated Time: 7 Hours
You ship:- A governed medallion (bronze/silver/gold) pipeline on Databricks — declarative pipelines, Unity Catalog lineage, scheduled workflows
- Workspace, clusters and notebooks; Databricks SQL
- Delta Live Tables / declarative pipelines; Unity Catalog for governance and lineage
- Jobs and workflows; medallion architecture in practice
Estimated Time: 6 Hours
You ship:- The same pipeline deployed to a managed cloud cluster — packaged, submitted, autoscaled on spot instances, with a cost analysis attached
- AWS EMR, GCP Dataproc and Azure; object storage access patterns; spark-submit and packaging
- Spark Connect for local development against remote clusters
- Cluster sizing; autoscaling; spot instances and cost control
Estimated Time: 7 Hours
You ship:- A production-grade Spark job — tested, logged, Airflow- orchestrated, idempotent and restartable — with CI/CD and failure alerting
- Structuring a Spark repo; testing PySpark code; logging and the Spark 4 structured logging framework
- Orchestration with Airflow; CI/CD for pipelines; idempotent and restartable jobs
- Monitoring, alerting and on-call for data pipelines
Estimated Time: 7 Hours
You ship:- One end-to-end batch + streaming lakehouse pipeline — designed, built and defended — plus a distributed ML pipeline with cross-validation at scale
- Distributed feature engineering; ML pipelines and cross-validation at scale
- When to use MLlib versus handing off to a Python ML stack
- Capstone design, build and defense
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