Big Data and Analytics

Description:
Learn how to store, process, and analyze large datasets with Hadoop, Spark, and visualization tools for data-driven decision making.

Learning Objectives:

  • Understand big data characteristics and architecture

  • Use Hadoop ecosystem tools like HDFS and MapReduce

  • Process data with Apache Spark

  • Visualize results with Power BI or Tableau

Detailed Content:

14.1 Introduction to Big Data

  • Volume, Velocity, Variety — the 3 Vs of big data.

  • Traditional RDBMSs can’t handle massive unstructured data.

14.2 Hadoop Ecosystem

  • HDFS: Distributed storage

  • MapReduce: Batch processing

  • Other tools: Hive (SQL interface), Pig (scripting), HBase (NoSQL)

14.3 Apache Spark

  • Faster, in-memory alternative to MapReduce.

  • Components: Spark SQL, Spark Streaming, MLlib (machine learning), GraphX.

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("Example").getOrCreate()

14.4 Data Ingestion

  • Tools: Sqoop (import from SQL), Flume/Kafka (streaming data)

14.5 Data Visualization

  • Use Tableau or Power BI to create dashboards.

  • Visual elements: bar charts, heatmaps, scatter plots.