# Big Data and Analytics

> 5-day big data and analytics training on Hadoop, HDFS, MapReduce, Hive, Pig and Spark, with hands-on labs and a capstone. In person or live virtual.

- Source: https://bodhih.com/big-data-and-analytics/
- Publisher: Bodhih Training (Bengaluru, India; since 2008)
- Last updated: 2026-10-15
- Contact: solutions@bodhih.com · +91 99000 11601

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Gain insights into Big Data and Analytics. Start leveraging these powerful tools to enhance your business intelligence today.

## At a glance

- **Duration:** 5-days · customisable
- **Who it’s for:** Anyone who wants to develop big data applications using Hadoop., Teams getting started or working on Hadoop based projects.
- **Format:** In person · Live virtual · Blended
- **Where:** India & 7 regions worldwide
- **Track record:** Since 2008 · 2,000+ companies

Big data and analytics training teaches teams to store, process and analyse large data sets using Hadoop and related tools such as HDFS, MapReduce, Hive, Pig and Apache Spark. The 5-day instructor-led program includes hands-on labs, cloud data platforms and a capstone project, and suits developers starting Hadoop projects. Bodhih recommends basic programming knowledge beforehand.

- Standard duration: 5-days, customisable to your needs
- Delivered in person at your location or as live virtual sessions
- Designed with the ADDIE model and evaluated for behaviour change
- Offered by Bodhih since 2008 to 2,000+ organisations across 7 regions

**Where and how:** big data and analytics training as an in-person workshop in [Bengaluru](https://bodhih.com/corporate-training-in-bangalore/), [Mumbai](https://bodhih.com/corporate-training-in-mumbai/), [Delhi NCR](https://bodhih.com/corporate-training-in-new-delhi/), [Gurugram](https://bodhih.com/corporate-training-in-gurugram/), [Hyderabad](https://bodhih.com/corporate-training-in-hyderabad/), [Chennai](https://bodhih.com/corporate-training-in-chennai/), [Pune](https://bodhih.com/corporate-training-in-pune/), [Kolkata](https://bodhih.com/corporate-training-in-kolkata/), [Ahmedabad](https://bodhih.com/corporate-training-in-ahmedabad/) and [Jaipur](https://bodhih.com/corporate-training-in-jaipur/); as a live online course; or delivered overseas in [Dubai](https://bodhih.com/corporate-training-in-dubai/), [Singapore](https://bodhih.com/corporate-training-in-singapore/) and across the Middle East, Asia and Africa.

Last reviewed 15 Oct 2026 by [Siddharthan VGJ](https://bodhih.com/siddharthan-vgj/), Founder · Bodhih Training, Bengaluru

Big Data Hadoop

## Program Overview:

This training will primarily Big data and Hadoop concepts, learning how to deploy Hadoop clusters.

Introduction to Hadoop and HDFS, concepts on Map reduce Programming model, Data Manipulation using pig and hive with hands on practical session.

## Training Duration:

5-days Instructor Led Training

## Target Audience:

- Anyone who wants to develop big data applications using Hadoop.
- Teams getting started or working on Hadoop based projects.

## Training Prerequisite:

Basic programming knowledge is recommended.

## Training Outcome:

- Practical Approach
- Hands on training session
- Real time project and case studies
- 24/7 support

#### Solutions & Services

## What participants will be able to do

### Understand distributed data processing

Participants explain how HDFS, YARN and MapReduce split storage and computation across a cluster.

### Query large data sets with SQL

Analysts and engineers use Hive and Spark SQL to explore and summarise big data.

### Build Spark pipelines

Engineers write batch transformations with Spark DataFrames in [Python](https://bodhih.com/python-for-data-analysis-training/) or Scala.

### Choose the right storage format

Teams compare row and columnar formats such as Parquet and understand partitioning.

### Relate Hadoop to modern platforms

Participants see how on-premises Hadoop concepts map to cloud data lakes and lakehouses.

### Deliver an end-to-end analytics flow

Teams ingest, clean, transform and analyse a realistic data set in a capstone lab.

## Who should attend

- Developers building or maintaining big data applications
- Data engineers working on Hadoop, Spark or cloud data platforms
- Data analysts who need to query very large data sets
- Teams migrating from on-premises Hadoop to cloud data platforms
- Architects evaluating data lake and lakehouse designs

## Recommended program outline

Recommended design, customised to your context after a short needs analysis.

### **Big data foundations and Hadoop architecture**Module 1 · Day 1

- What makes data 'big': volume, velocity and variety
- HDFS storage, replication and YARN resource management
- Setting up and exploring a Hadoop cluster
- Hadoop ecosystem tools and where each fits

### **MapReduce and data processing concepts**Module 2 · Day 2 (morning)

- The MapReduce programming model explained
- Writing and running a simple MapReduce job
- Why newer engines such as Spark are now more common
- Combiners, partitioners and job tuning basics

### **Data manipulation with Hive and Pig**Module 3 · Day 2 (afternoon) and Day 3

- Hive tables, partitions and HiveQL queries
- Pig Latin for data flows on legacy clusters
- File formats: CSV, Avro, ORC and Parquet

### **Apache Spark**Module 4 · Day 3 and Day 4

- Spark architecture, DataFrames and lazy evaluation
- Spark SQL and PySpark transformations
- Performance basics: partitioning, caching and joins
- Introduction to structured streaming

### **Cloud data platforms and capstone**Module 5 · Day 5

- Data lakes, lakehouses and open table formats in overview
- Managed big data services on major cloud providers
- Capstone: ingest, transform and analyse a data set end to end
- Data quality, governance and cost considerations

## Sample training plan: 5-day hands-on design

This five-day lab takes developers, data engineers and analysts from Hadoop foundations to Spark pipelines and a view of modern cloud data platforms. All work runs on a lab cluster using one realistic data set: a year of clickstream, orders and returns from a fictional online grocery chain. Participants load data into HDFS, write a MapReduce job, query with Hive, compare file formats, build PySpark pipelines, tune joins and finish with an end-to-end capstone that answers business questions for a mock product head.

80% hands-on practice · 33.8 contact hours · Cohort: 10–18 participants, each with a laptop and access to the lab cluster · In person · Live virtual

### Day 1: Big data foundations and Hadoop architecture

Participants can explain Hadoop architecture and load, inspect and manage data in HDFS on a live cluster.

1. 09:30–10:00 · The human cluster (Energiser)
2. 10:00–10:30 · Volume, velocity and variety (Concept burst)
3. 10:30–11:00 · Tour the cluster (Demo & practice)
4. 11:15–12:30 · HDFS hands-on (Hands-on lab)
5. 12:30–13:00 · Ecosystem map (Group challenge)
6. 13:45–15:00 · Explore the data (Hands-on lab)
7. 15:15–16:45 · Frame the capstone questions (Case clinic)
8. 16:45–17:30 · Day 1 lab log (Reflection)

### Day 2: MapReduce, then Hive

Participants can write and tune a MapReduce job and create partitioned Hive tables to query the data.

1. 09:30–10:00 · Map and reduce with paper (Energiser)
2. 10:00–11:00 · First MapReduce job (Demo & practice)
3. 11:15–12:00 · Combiners and partitioners (Hands-on lab)
4. 12:00–13:00 · Hive tables and HiveQL (Hands-on lab)
5. 13:45–15:00 · Partitions make the difference (Hands-on lab)
6. 15:15–16:45 · Query relay (Group challenge)
7. 16:45–17:30 · When would I still use MapReduce? (Reflection)

### Day 3: File formats and Spark foundations

Participants can choose suitable file formats and build Spark DataFrame transformations.

1. 09:30–10:00 · Format face-off (Energiser)
2. 10:00–11:00 · Convert and compare (Hands-on lab)
3. 11:15–11:45 · How Spark thinks (Concept burst)
4. 11:45–13:00 · First PySpark pipeline (Hands-on lab)
5. 13:45–15:00 · Spark SQL and joins (Hands-on lab)
6. 15:15–16:45 · Pipeline build sprint (Build sprint)
7. 16:45–17:30 · Pipeline review (Peer coaching)

### Day 4: Spark performance and streaming

Participants can diagnose and improve Spark job performance and build a simple streaming query.

1. 09:30–10:00 · Guess the slow job (Energiser)
2. 10:00–11:00 · Partitioning and caching lab (Hands-on lab)
3. 11:15–12:30 · Join clinic (Case clinic)
4. 12:30–13:00 · Data quality gates (Skill drill)
5. 13:45–15:00 · Structured streaming (Demo & practice)
6. 15:15–16:45 · Capstone build, part one (Build sprint)
7. 16:45–17:30 · Stand-up and blockers (Reflection)

### Day 5: Cloud data platforms and capstone

Participants can relate Hadoop to cloud data platforms and present an end-to-end analytics flow with findings.

1. 09:30–10:00 · Old world, new world (Energiser)
2. 10:00–11:00 · Lakes, lakehouses and table formats (Case clinic)
3. 11:15–13:00 · Capstone build, part two (Build sprint)
4. 13:45–15:00 · Capstone showcase (Presentation)
5. 15:15–16:30 · Governance and cost check (Group challenge)
6. 16:30–17:30 · My 90-day data plan (Action planning)

Recommended: 5 days in person (09:30–17:30), or 10 × 3.5-hour live virtual labs over three weeks, on a lab cluster provided for the course, with a 30-day pipeline review. A sample design: every Bodhih program is customised after a short needs analysis.

### How long is big data and Hadoop training?

The recommended design is five days in person, from 09:30 to 17:30, or ten live virtual labs of three and a half hours each over three weeks. A three-day Spark-focused version suits engineers who know Hadoop basics. A 30-day pipeline review follows for real jobs.

### What activities are included in big data and Hadoop training?

Participants work on a lab cluster with a year of online grocery data. They load it into HDFS, write and tune a MapReduce job, race to answer questions in Hive, build PySpark pipelines, fix slow joins and present an end-to-end capstone to a mock product head.

## When to choose this

✓**Your team maintains Hadoop clusters or is starting a big data project**✓**Engineers need to move from MapReduce and Hive to Spark**✓**You are planning a move from on-premises Hadoop to a cloud data platform**✓**Analysts need to query data that no longer fits in traditional tools**

## Frequently asked questions

### Is Hadoop still worth learning?

Many organisations still run Hadoop clusters, and its core ideas of distributed storage and parallel processing underpin modern data platforms. New projects more often use Spark and cloud data lakes. Bodhih's program covers Hadoop foundations and then moves to Spark and cloud concepts, so teams can support existing systems and plan for what comes next.

### What is the difference between Hadoop and Spark?

Hadoop is an ecosystem that includes HDFS for storage, YARN for resource management and MapReduce for processing. Spark is a processing engine that can run on Hadoop or elsewhere. It keeps data in memory where possible, which usually makes it much faster than MapReduce for analytics and iterative work. Many teams use Spark with HDFS or cloud storage.

### What skills do I need before big data training?

Basic programming knowledge is recommended, ideally in Python, Java or Scala, along with working SQL and familiarity with the Linux command line. Participants do not need prior Hadoop experience. Bodhih shares a short readiness checklist and can add a Python or SQL refresher for groups that need one.

### What is a data lakehouse?

A data lakehouse combines low-cost data lake storage with features traditionally found in data warehouses, such as reliable transactions, schema management and fast SQL queries. It is typically built on open file and table formats in cloud storage. The program explains the concept so teams understand how it relates to their Hadoop experience.

### How long is the big data and analytics program?

Bodhih's recommended program is five days of instructor-led training with daily labs and a capstone. Teams focused only on Spark or only on analytics querying can take a shorter, targeted version. The outline, tools and cloud platform used in labs are customised to your environment after a needs analysis.

### What does big data and Hadoop training cover?

Over five days it covers big data foundations and Hadoop architecture, HDFS, the MapReduce programming model, data manipulation with Hive and Pig, file formats, Apache Spark performance and streaming, and cloud data platforms, ending with an end-to-end analytics capstone. Sessions are hands-on, and basic programming knowledge is recommended.