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Big Data Analytics: Level-01

Big Data Analytics Track

Big Data Analytics:
Level-01

4.8 (1,230 reviews)

Introduction to Big Data Analytics: Level-01. Learn to work with Hadoop, Spark, and NoSQL. Perform large-scale data processing and uncover insights using analytics tools.

Created by Avanteia
12,580 Total Enrolled
15 September 2024 Last Updated
Enroll Now
Big Data Analytics Level-01 Course
2 Months Duration
Certificate On Completion
Level-01 Level
8 Modules Syllabus
2 Months Duration
English Language
Certificate Included
Introduction

Overview

Learn to work with Hadoop, Spark, and NoSQL. Perform large-scale data processing and uncover insights using analytics tools.

Big Data Spark Hadoop Python SQL Analytics
Goal

Learning Outcome

Enhance skills in processing and managing big data, implement real-time data processing, and build data integration pipelines while performing intermediate data analysis.

Curriculum

Syllabus

Click any module to expand and view topics and hands-on labs included.

  • What is Big Data? 5 Vs (Volume, Velocity, Variety, Veracity, Value)
  • Big Data ecosystem & career scope
  • Traditional databases vs Big Data systems
Hands-on Lab
Explore Kaggle datasets Exploratory data analysis using Python (Pandas + Matplotlib)
  • Data sources (logs, IoT, social media, sensors)
  • Batch vs Streaming data ingestion
  • ETL concepts (Extract, Transform, Load)
Hands-on Lab
Use Python requests + APIs to collect data Ingest streaming data using Kafka local setup
  • HDFS (Hadoop Distributed File System)
  • MapReduce programming model
  • Hadoop ecosystem (Hive, Pig, HBase, Sqoop)
Hands-on Lab
Install Hadoop single-node cluster (local VM or Docker) Run HDFS file operations (upload, list, read, delete) Execute a simple MapReduce word count job
  • Introduction to Hive & HQL (Hive Query Language)
  • Data warehousing concepts
  • Partitioning & Bucketing
Hands-on Lab
Install Apache Hive Run SQL queries on Hive tables (select, group by, join)
  • Pig Latin basics
  • NoSQL databases: HBase overview
  • HBase data model & CRUD operations
Hands-on Lab
Run Pig Latin scripts for log processing Create an HBase table & insert/query data
  • Spark architecture & RDDs
  • DataFrames & Datasets
  • Spark SQL basics
Hands-on Lab
Install PySpark (local or Colab) Perform ETL on large dataset using PySpark
  • Spark MLlib for Machine Learning
  • Clustering, Classification, Regression in Spark
  • Graph processing with GraphX
Hands-on Lab
Use Spark MLlib for sentiment analysis (Twitter/Kaggle dataset) Run KMeans clustering on customer dataset
  • Real-time data processing concepts
  • Apache Kafka & Spark Streaming
  • Use cases: fraud detection, IoT analytics
Hands-on Lab
Install Kafka and simulate real-time event streaming Process stream using PySpark Streaming
Results

What You Will Learn

Big Data Fundamentals

Understand the 5 Vs of Big Data, ecosystem components, and career opportunities in the field.

Data Collection & Ingestion

Collect data from APIs, handle batch and streaming ingestion, and understand ETL workflows.

Hadoop & Spark Ecosystem

Work with HDFS, MapReduce, Hive, Pig, HBase, and perform large-scale processing with Spark.

Real-Time Analytics

Implement streaming pipelines with Kafka and Spark Streaming for real-time insights.

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