Big Data with Hadoop: HDFS, MapReduce & Ecosystem Tools

Skill Goal:
Build a strong understanding of Big Data concepts, challenges, and the Hadoop ecosystem.

Skills Developed:

• Define Big Data and its key characteristics (Volume, Velocity, Variety)

• Explain limitations of traditional data systems

• Identify core Hadoop ecosystem components

• Understand distributed computing concepts

• Recognize real-world Big Data use cases across industries

Sprint Outcome:
Ability to explain Big Data fundamentals and map business problems to Hadoop-based solutions.

Skill Goal:
Understand and design scalable distributed storage using Hadoop Distributed File System (HDFS).

Skills Developed:

• Explain HDFS architecture and working principles

• Understand NameNode and DataNode roles

• Apply replication and fault tolerance concepts

• Understand high availability in HDFS

• Analyze distributed data storage behavior

Sprint Outcome:
Ability to design and explain reliable distributed storage systems using HDFS.

Skill Goal:
Develop and execute data processing workflows using the MapReduce programming model.

Skills Developed:

• Understand MapReduce architecture and workflow

• Explain map, shuffle, and reduce phases

• Manage input and output formats

• Execute basic MapReduce jobs

• Analyze job execution flow

Sprint Outcome:
Ability to build and execute MapReduce jobs for large-scale data processing.

Skill Goal:
Improve performance and efficiency of MapReduce jobs.

Skills Developed:

• Use combiners and partitioners effectively

• Optimize MapReduce job configurations

• Identify performance bottlenecks

• Apply best practices for job design

• Analyze execution logs for optimization

Sprint Outcome:
Ability to optimize MapReduce workflows for performance and scalability.

Skill Goal:
Perform data transformation and processing using Apache Pig.

Skills Developed:

• Understand Pig architecture and execution model

• Write Pig Latin scripts

• Perform data transformations on Hadoop

• Process structured and semi-structured data

• Validate transformed datasets

Sprint Outcome:
Ability to transform large datasets efficiently using Pig scripts.

Skill Goal:
Query and analyze Big Data using Hive and SQL-like language.

Skills Developed:

• Understand Hive architecture and components

• Write HiveQL queries

• Create and manage tables

• Apply partitioning and bucketing

• Perform data analysis using Hive

Sprint Outcome:
Ability to query and analyze large datasets using HiveQL.

Skill Goal:
Optimize Hive queries and understand NoSQL storage with HBase.

Skills Developed:

• Optimize complex Hive queries

• Understand HBase architecture and use cases

• Compare Hive and HBase usage scenarios

• Integrate Hive with HBase

• Explore NoSQL data modeling concepts

Sprint Outcome:
Ability to choose and apply Hive or HBase based on data requirements.

Skill Goal:
Process Big Data efficiently using Apache Spark.

Skills Developed:

• Understand Spark architecture and components

• Compare Spark with MapReduce

• Work with Resilient Distributed Datasets (RDDs)

• Perform data transformations and actions

• Execute Spark jobs

Sprint Outcome:
Ability to process large-scale data faster using Spark.

Skill Goal:
Integrate Hadoop with data ingestion and streaming tools.

Skills Developed:

• Understand roles of Kafka, Flink, and Storm

• Perform data ingestion using Sqoop and Apache NiFi

• Differentiate batch and real-time processing

• Design data pipelines

• Map tools to real-world use cases

Sprint Outcome:
Ability to design end-to-end data pipelines using Hadoop ecosystem tools.

Skill Goal:
Enhance Spark performance and apply advanced capabilities.

Skills Developed:

• Work with Spark SQL and DataFrames

• Understand Spark MLlib fundamentals

• Perform data analysis using Spark

• Apply performance tuning techniques

• Optimize distributed processing workloads

Sprint Outcome:
Ability to build optimized Spark applications for real-world data processing.

Project Goal:

Design and implement a scalable Big Data processing solution using Hadoop ecosystem tools to solve a real-world business problem.

Skills Demonstrated:

• Analyze a real-world Big Data business scenario

• Define data processing objectives and success criteria

• Store and manage data using HDFS

• Process large datasets using MapReduce and Spark

• Transform data using Pig and query using Hive

• Apply optimization techniques for performance

• Integrate data pipelines using ecosystem tools (Kafka, Sqoop, NiFi)

• Compare batch and real-time processing approaches

• Present scalable architecture and data insights

• Demonstrate practical Big Data solution design