Big Data Analytics Laboratory Notes
Subject Overview
The practical companion to Big Data Analytics, giving hands-on experience with Hadoop and big data analytical tools to implement the clustering, classification and association-rule techniques covered in theory on real datasets. A 2-credit elective lab paper.
Subject at a glance
1
Units
4
Topics
2
Credits
1
Infographics
Unit-wise Syllabus
1 units — open any unit for detailed topic-wise notes, diagrams and important exam questions
- Hands-on implementation of clustering
- classification and association-rule mining techniques using Hadoop/big data analytical tools on real-world datasets
- applying the concepts of HDFS
- MapReduce and the Hadoop ecosystem covered in the theory paper
HDFS
Distributed storage
YARN
Resource management
MapReduce
Batch processing
Hive
SQL-like queries
Pig
Data-flow scripts
HBase
NoSQL database
Spark
Fast in-memory processing
Study guide
In this lab you implement clustering, classification and association-rule mining on real-world datasets using Hadoop and big data analytical tools, applying HDFS, MapReduce and the wider Hadoop ecosystem from the theory paper. Document the data size, runtime and results of each experiment.
Why Big Data Analytics Laboratory Matters
Real experience with Hadoop-style tools and large data sets distinguishes you from candidates with theory alone.
How to Prepare for Big Data Analytics Laboratory
- Run experiments on data sets of increasing size.
- Record runtimes and memory use.
- Write a short analysis of each result.
Big Data Analytics Laboratory: Frequently Asked Questions
What is done in the big data lab?
Clustering, classification and association-rule mining with Hadoop and big data tools on real data.