HIGAET Big Data Engineering
Learn reliable Spark, Kafka, and lakehouse systems to process large-scale batch and streaming data reliably through HIGAET Practical Training projects.
Duration
10 weeks · 6-8 hours/week
Level
Advanced
Delivery
Online
Status
Open for enrollment
Why this technology matters.
Big data engineering is the discipline of processing very large batch and streaming datasets reliably with distributed systems like Spark, Kafka, and lakehouses. It matters now because event streams, logs, and large tables overwhelm single-machine workflows.
Engineers use it to build partitioned, fault-tolerant batch jobs, streaming topologies with windows, watermarks, and exactly-once handling, and lakehouse tables with schema evolution, compaction, and time travel. Scale does not fix design errors: clusters do not rescue skewed keys, late data, or undefined event semantics.
By the end you will be able to build distributed batch jobs with partitioning and fault tolerance, streaming topologies with windows and watermarks, and lakehouse tables with evolution, compaction, and performance profiling using skew and resource analysis.
Why this course exists
The gap is between a job that runs once on a sample and a system that keeps up with volume, late events, and schema change every day. This course teaches the arc from Sources to Pipelines to Models to Decisions at scale: partitioning and fault tolerance, streaming correctness, lakehouse maintenance, and honest performance evaluation.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with Python and SQL
- Familiarity with distributed concepts
- Basic data pipeline awareness
Technologies & tools
Skills you'll gain
A 10 weeks arc, module by module.
- Module 01
Module 01 — Foundations: Distributed Systems, Batch Versus Streaming, and Storage Formats
- Module 02
Module 02 — Core: Distributed Processing Models, Partitions, and Fault Tolerance
- Module 03
Module 03 — Core: Batch Engineering with Large-Scale Frames and SQL Engines
- Module 04
Module 04 — Engineering: Lakehouse Tables, Partitioning, and Incremental Processing
- Module 05
Module 05 — Engineering: Event Streaming, Messaging, and Stream Processing
- Module 06
Module 06 — Advanced: Stateful Streaming, Windows, Joins, and Late Data
- Module 07
Module 07 — Advanced: Performance Tuning, Skew Handling, and Cost Optimization
- Module 08
Module 08 — Production: Security, Monitoring, and Operations for Data Clusters
- Module 09
Module 09 — Capstone: Unified Batch and Streaming Platform with Lakehouse Output
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Build distributed batch jobs that process large datasets with partitioning and fault tolerance
- Design streaming topologies with windows, watermarks, and exactly-once handling
- Develop lakehouse tables with schema evolution, compaction, and time travel
- Evaluate job performance using metrics, skew analysis, and resource profiling
- Automate cluster workflows with scheduling, retries, and environment templates
- Optimize shuffle, storage formats, and query plans for cost and speed
- Integrate batch and streaming layers into unified analytics-ready outputs
- Secure big data workloads with encryption, access policies, and audit logging
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Partitioned batch processing job
- Project 02
Streaming topology with watermarks
- Project 03
Lakehouse table with time travel
- Project 04
Job performance tuning study
- Capstone
Batch and streaming lakehouse system
Speak the language first.
- Distributed batch processing
- Splitting large datasets across many workers so jobs finish faster and survive node failures.
- Partitioning
- Dividing data by keys or time ranges so workers read and process only what they need.
- Streaming windows
- Grouping continuous events into fixed or sliding time buckets for aggregation.
- Watermarks
- Markers for how late event data may arrive before a streaming window is considered complete.
- Exactly-once handling
- Techniques like idempotent writes and checkpoints that prevent duplicate results in streams.
- Lakehouse tables
- Large-scale tables with transactions, schema evolution, and time travel over object storage.
- Schema evolution
- Adding or changing columns safely so old and new data stay readable by existing jobs.
- Compaction
- Merging many small files into fewer large ones to speed up reads and lower costs.
- Skew analysis
- Detecting when a few keys hog workers while others sit idle, then rebalancing the load.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Spark job crawls because one partition dwarfs the rest
Inspect key distributions, salt the hot keys or repartition, and confirm worker runtimes even out.
Streaming results show duplicates after a restart
Enable checkpointing with idempotent sinks and verify exactly-once settings on the source and writer.
Late events silently drop from windowed counts
Tune watermarks and allowed lateness, then route overly late records to a side output for review.
Lakehouse reads slow down as small files pile up
Run compaction and vacuum on a schedule and check file counts per partition before and after.
Schema change breaks existing batch jobs
Apply evolution with added nullable columns, test readers against both versions, and pin table versions in jobs.
Executors run out of memory on wide shuffles
Reduce shuffle partitions, broadcast small joins, and profile memory per stage to find the heavy operator.
Before you move on, you should be able to
- Build distributed batch jobs with partitioning and fault tolerance
- Design streaming topologies with windows, watermarks, and exactly-once handling
- Develop lakehouse tables with schema evolution, compaction, and time travel
- Evaluate job performance using metrics, skew analysis, and resource profiling
- Deploy batch and streaming pipelines with checkpointing and recovery
- Explain resource tradeoffs behind partition and cluster sizing choices
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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A 10 weeks course — Data & Machine Learning.