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Big Data Engineer AI-Powered

★★★★★ 4.8 · 143 learners

Engineer large-scale data pipelines that process petabytes. Master the modern Big Data stack: Apache Spark, Kafka, Hive, Airflow, Delta Lake, and cloud-native tools on AWS EMR & Databricks — the skills that command ₹12–30 LPA salaries.

Sessions40 Live
Duration4 Months
ModeOnline · Offline · Hybrid
LanguageEnglish & Tamil
SupportPlacement until placed
Enquire Now Call +91 90874 96799
40
Live Sessions
90 min
Per Session
4 Months
Duration
English
Language
Weekday
M T W T F S S
Weekend
M T W T F S S
Mode Online · Offline · Hybrid
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About the Course

Linkskill's Big Data Engineer program prepares you to design, build, and optimise large-scale data pipelines that power analytics, machine learning, and real-time applications. You'll go from foundational distributed computing concepts to building production-grade pipelines on cloud platforms.

The curriculum mirrors real-world data engineering workflows — ingestion, processing, storage, orchestration, and delivery. You'll work with Apache Spark on Databricks, stream data with Kafka, build data warehouses on Snowflake, and orchestrate workflows with Apache Airflow — all on AWS cloud infrastructure.

Course Curriculum

8 Modules · 40 Sessions

01

Big Data Foundations & Python for Engineering

4 sessions
  • Big Data concepts: Volume, Velocity, Variety, Veracity, Value
  • Distributed computing fundamentals: horizontal vs vertical scaling
  • Hadoop ecosystem overview: HDFS, MapReduce, YARN architecture
  • Python advanced: generators, decorators, context managers, async I/O
  • Linux for data engineers: shell scripting, cron jobs, file system management
  • Data engineering lifecycle: ingestion → processing → storage → serving
  • Modern data stack components and architecture patterns
02

Apache Spark & PySpark

8 sessions
  • Spark architecture: Driver, Executor, RDD, DAG execution model
  • PySpark DataFrames: transformations, actions, schema management
  • Spark SQL: complex queries, window functions, UDFs
  • Spark Structured Streaming: micro-batch and continuous processing
  • Databricks platform: notebooks, clusters, Delta Lake CRUD operations
  • Delta Lake: ACID transactions, time travel, schema evolution
  • Spark performance tuning: partitioning, caching, broadcast joins, AQE
  • Medallion Architecture: Bronze → Silver → Gold layer design
  • Spark on AWS EMR: cluster setup, S3 integration, spot instances
03

Data Ingestion & Apache Kafka

5 sessions
  • Event streaming fundamentals: producers, consumers, topics, partitions
  • Apache Kafka setup: brokers, ZooKeeper / KRaft, replication
  • Kafka Streams API for real-time data processing
  • Kafka Connect: source and sink connectors for databases and S3
  • Schema Registry with Avro for data contract enforcement
  • AWS Kinesis: data streams, Firehose for managed streaming
  • Real-time pipeline: Kafka → Spark Structured Streaming → Delta Lake
  • Apache Flink basics for stateful stream processing
04

Data Warehousing: Hive, Snowflake & dbt

5 sessions
  • Data warehouse concepts: OLAP vs OLTP, star schema, snowflake schema
  • Apache Hive: HQL, partitioning, bucketing, ORC/Parquet file formats
  • Snowflake: virtual warehouses, time travel, data sharing, Zero-Copy Cloning
  • dbt (data build tool): models, tests, documentation, macros, seeds
  • ELT patterns with dbt on Snowflake: raw → staging → mart layers
  • Apache Iceberg for open table format and multi-engine support
  • Data lakehouse architecture: combining data lake + warehouse strengths
05

Pipeline Orchestration with Airflow

5 sessions
  • Apache Airflow architecture: DAGs, Operators, Sensors, Hooks, XCom
  • Writing production DAGs: task dependencies, retry logic, SLAs
  • Airflow operators: Python, Bash, SparkSubmit, BigQueryOperator, S3
  • Dynamic DAG generation and DAG factories
  • Astronomer Astro CLI for local development and deployment
  • Monitoring and alerting: Airflow UI, Slack notifications, PagerDuty
  • Prefect and Dagster as modern Airflow alternatives
06

Cloud Data Engineering on AWS

5 sessions
  • AWS data services: S3, Glue, Athena, Redshift, Lake Formation
  • AWS Glue: ETL jobs, crawlers, data catalog, Spark integration
  • Amazon Redshift: columnar storage, distribution styles, sort keys, Spectrum
  • AWS Lambda for serverless data pipelines and event-driven ingestion
  • AWS Step Functions for orchestrating multi-service data workflows
  • Infrastructure as Code: Terraform for data engineering resources
  • Cost optimisation: S3 intelligent tiering, spot instances, right-sizing
07

Data Quality, Governance & MLOps

5 sessions
  • Data quality frameworks: Great Expectations, Soda Core, dbt tests
  • Data lineage and observability: OpenLineage, Marquez, Monte Carlo
  • Data governance: data cataloging with Apache Atlas / AWS Glue Catalog
  • GDPR and data privacy: PII masking, encryption, access controls
  • Feature stores for ML: Feast, Tecton — serving features at scale
  • Building data pipelines for LLM applications: vector embeddings and RAG
  • DataOps: applying DevOps principles to data pipeline development
08

Capstone Data Pipeline Projects

3 sessions
  • Project 1: Real-time e-commerce analytics pipeline — Kafka → Spark Streaming → Delta Lake → Snowflake
  • Project 2: Batch ELT pipeline — S3 → AWS Glue → Redshift → dbt → Power BI dashboard
  • Project 3: ML feature engineering pipeline — data ingestion → feature store → model training readiness
  • Architecture diagrams, performance benchmarks, and cost analysis
  • GitHub repository with full code, documentation, and README
  • Databricks Certified Data Engineer Associate exam preparation

Job Roles After This Course

Data Engineer Big Data Engineer Cloud Data Engineer ETL / Pipeline Developer Data Platform Engineer Streaming Data Engineer MLOps / Feature Engineer Data Architect

Tools & Technologies

Big Data Stack

Apache Spark / PySpark Apache Kafka Apache Airflow Delta Lake Databricks Apache Hive Snowflake dbt (data build tool) Apache Iceberg Apache Flink

Cloud & Infrastructure

AWS Glue AWS EMR Amazon Redshift AWS Kinesis Great Expectations Terraform Docker / Kubernetes Python

Key Course Features

📡
Live Cloud Lab Sessions
🏗️
3 Production-Grade Projects
🤖
AI / LLM Pipeline Track
🏆
Databricks Cert Prep
🏢
Industry Mentorship
📹
Session Recordings Provided
💳
Easy EMI Options
🔗
LinkedIn Profile Building
🎯
Interview Preparation
🎓

Industry Certificate

Issued upon completing 80% attendance and submitting all 3 pipeline projects. Certificate validates skills in Apache Spark, Kafka, Airflow, Delta Lake, dbt, Snowflake, and AWS data services. Also prepares you for Databricks Certified Data Engineer Associate.

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🤖
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