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Big Data Analytics: Level-02

Big Data Analytics Track

Big Data Analytics:
Level-02

4.8 (1,230 reviews)

Introduction to Big Data Analytics: Level-02. Master real-time analytics, machine learning integration, and big data pipelines. Optimize systems for speed, scale, and actionable business intelligence.

Created by Avanteia
12,580 Total Enrolled
15 September 2024 Last Updated
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Big Data Analytics Level-02 Course
3 Months Duration
Certificate On Completion
Level-02 Level
12 Modules Syllabus
3 Months Duration
English Language
Certificate Included
Introduction

Overview

Master real-time analytics, machine learning integration, and big data pipelines. Optimize systems for speed, scale, and actionable business intelligence.

Big Data Spark Hadoop Python Cloud Analytics
Goal

Learning Outcome

Master scalable data architecture, apply advanced analytics and machine learning, manage real-time processing and data governance, and optimize big data systems for performance.

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
  • Key-Value stores (Redis)
  • Document stores (MongoDB)
  • Columnar stores (Cassandra)
Hands-on Lab
Install MongoDB Community Edition Insert & query large JSON dataset
  • Visualization frameworks (Matplotlib, Seaborn, Plotly)
  • Dashboards: Tableau Public (free), Power BI Desktop
  • Connecting visualization tools with Big Data
Hands-on Lab
Create interactive dashboards with Plotly/Dash Use Tableau Public to visualize Big Data CSV
  • Big Data services on AWS, GCP, Azure
  • Google BigQuery basics
  • Cloud storage integration
Hands-on Lab
Run queries on Google BigQuery free tier Store & process data using AWS S3 + Athena (free tier)
  • End-to-End Big Data pipeline design
  • Combining Hadoop, Spark, NoSQL, Visualization
  • Performance tuning & optimization
Hands-on Lab
Choose a real-world dataset (social media, IoT, e-commerce) Perform: Data ingestion → Processing (Spark) → Storage (MongoDB/HDFS) → Visualization (Dash/Tableau) Deploy project on GitHub/Google Cloud free tier
Results

What You Will Learn

Scalable Data Architecture

Master advanced Hadoop ecosystem components and design scalable big data architectures for enterprise needs.

Spark MLlib & Analytics

Apply machine learning with Spark MLlib, perform clustering, classification, and regression on large datasets.

Data Governance & Security

Implement data governance, security, and compliance frameworks for enterprise big data systems.

Performance Optimization

Optimize and tune big data systems for speed, scale, and actionable business intelligence.

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What Our Students Say

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