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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
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
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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.

12 Questions to Ask Before You Enrol — Answered

Before joining any Big Data Engineer course in Salem or online, ask these 12 questions. Here are Linkskill Academy's straight answers.

1Does the Big Data Engineer course cover Excel, SQL, Power BI or Tableau, Python and statistics? Can I get the full module list in writing?

This is a data engineering course: Python, Apache Spark/PySpark, Kafka, Hive, Snowflake and dbt, Airflow, and cloud data engineering on AWS, plus data quality and governance. SQL is used throughout. For Excel and Power BI, see the Data Analyst course. Full module list: 1. Big Data Foundations & Python for Engineering (4 sessions); 2. Apache Spark & PySpark (8 sessions); 3. Data Ingestion & Apache Kafka (5 sessions); 4. Data Warehousing: Hive, Snowflake & dbt (5 sessions); 5. Pipeline Orchestration with Airflow (5 sessions); 6. Cloud Data Engineering on AWS (5 sessions); 7. Data Quality, Governance & MLOps (5 sessions); 8. Capstone Data Pipeline Projects (3 sessions). We will send this module list on WhatsApp or email on request.

2How many real projects will I do, and with what datasets? Can I see past student projects?

Every module includes hands-on labs, and the course ends with capstone data-pipeline projects: ingestion → processing → warehouse → orchestration on real datasets. Ask us on WhatsApp and we will share sample capstone projects from past learners.

3What is the trainer's industry experience? Do they work in the industry now?

Programmes are led by Sreemathy Sampath, Founder & Managing Director of Linkskill Academy, who has 10+ years of training experience in data and analytics, together with practising industry mentors for specialist modules. Ask us on WhatsApp for the name, LinkedIn profile and current role of the trainer assigned to your batch, and we will send it in writing before you pay.

4Can I attend a free demo class before paying?

Yes. You can attend a free demo / orientation session before you pay anything. Book a slot on WhatsApp (+91 90874 96799). We also run regular free and low-cost workshops (for example our free SQL one-day workshop), which are a good way to experience our teaching style first.

5How many students are in a batch? Are there weekday, weekend or online options?

Batches are kept small so the trainer can review every learner's work. We run weekday and weekend batches, in person at our Arisipalayam, Salem centre, live online, or hybrid. Ask us for the exact seat count and timings of the next batch and we will confirm them on WhatsApp or email.

6What is the duration and total fee, including taxes and exam charges? Are instalments available?

Duration: 40 live sessions of 90 min each, over about 4 Months. Fee: Full programmes at Linkskill are ₹18,000–₹35,000 depending on the course, mode and batch; we confirm the exact Big Data Engineer fee in writing. EMI/instalment options are available. Before you pay we send the full amount in writing, with GST and any third-party exam fees shown separately, so there are no hidden charges.

7Who issues the certificate? Is it linked to Microsoft or another recognised body, or is it the academy's own?

The certificate is issued by Linkskill Academy Pvt. Ltd. on completing the sessions and capstone projects. It is the academy's own certificate, not an AWS or Databricks certificate.

8Do you offer placement support or only interview prep? Which companies hired your recent students, and how many were placed last year?

We offer placement support, not a job guarantee, and we do not advertise "100% placement". Support continues after the course until you are placed: resume and LinkedIn rebuilding, mock interviews, portfolio review and referrals to hiring companies we work with. Ask us for the list of companies that hired our recent learners and last year's placement numbers, and we will share them in writing.

9Can I speak to two or three past students?

Yes. On request we connect you with two or three past learners from a recent batch (with their consent) so you can ask them directly about the training, projects and placement support.

10What is the refund or batch-change policy if I'm not satisfied?

Our written policy: 100% refund if you cancel 7+ days before the batch starts; 75% refund if you cancel within the first 7 days of class and have attended no more than 2 sessions; after week 1 there is no refund, but you can transfer to a later batch free of charge. Batch transfers are unlimited and free. Refunds are processed within 7 business days.

11Is an internship included, and is it paid or unpaid?

An internship is not bundled into the course fee by default. Linkskill runs separate internship programmes (4–8 weeks, ₹2,000–₹5,000 programme fee depending on the track) where you work on live projects with mentorship and a certificate. These are learning internships, not stipend-paying jobs; a few paid opportunities may be offered to exceptional candidates. Ask us whether a current offer includes an internship.

12If I miss a class, do I get recordings or a repeat session?

Every live session is recorded and shared with you, so you never lose a class. If you miss several sessions, you can join the same topic in a parallel or later batch at no extra cost (free batch transfer).

Get it in writing. Ask for any of these answers on WhatsApp (+91 90874 96799) or email (info@linkskillacademy.live) and we will reply in writing, so you have a record before you pay. Be cautious of any institute that promises "100% placement" without naming companies or numbers. Get these answers on WhatsApp →
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