1. About Big Data Technologies

Big Data Technologies are tools and frameworks designed to store, process, and analyze large datasets that traditional data processing systems cannot handle. These technologies are essential for industries like finance, healthcare, e-commerce, and social media, where data is generated at an unprecedented scale.

Key Characteristics of Big Data (3 Vs):

• Volume : Massive amounts of data.
• Velocity : High-speed data generation and processing.
• Variety : Structured, semi-structured, and unstructured data.

Key Applications:

• Data Warehousing : Storing and querying large datasets.
• Real-Time Analytics : Processing data streams in real-time.
• Machine Learning : Training models on large datasets.
• Business Intelligence : Generating insights from big data.

2. Why Learn Big Data Technologies?

• High Demand : Big Data engineers and analysts are in high demand across industries.
• Scalability : Handle massive datasets efficiently using distributed systems.
• Versatility : Used in diverse fields like finance, healthcare, marketing, and IoT.
• Career Growth : Lucrative salaries and opportunities for advancement.
• Tools & Frameworks : Learn popular technologies like Hadoop, Spark, Kafka, and NoSQL databases.

3. Full Syllabus

Phase 1: Basics (Weeks 1–4)

1. Introduction to Big Data
   • What is Big Data?
   • Key Concepts: Batch Processing vs Real-Time Processing.
   • Challenges: Storage, Processing, and Analysis of Big Data.
2. Linux Basics
   • Command Line Interface (CLI): File operations, permissions, and scripting.
   • Setting up a Linux environment for Big Data tools.
3. Hadoop Ecosystem
   • HDFS (Hadoop Distributed File System) : Storing large datasets across clusters.
   • MapReduce : Distributed data processing framework.
   • Tools: Apache Hadoop, Hive, Pig.
4. NoSQL Databases
   • Types of NoSQL Databases: Key-Value Stores, Document Stores, Columnar Stores, Graph Databases.
   • Tools: MongoDB, Cassandra, HBase.

Phase 2: Intermediate (Weeks 5–8)

5. Apache Spark
   • Core Concepts : RDDs (Resilient Distributed Datasets), DataFrames, and Datasets.
   • Spark SQL : Querying structured data.
   • Spark Streaming : Real-time data processing.
6. Data Ingestion
   • Tools: Apache Kafka, Apache Flume.
   • Use Cases: Streaming data from sources like social media, IoT devices.
7. Data Warehousing
   • Tools: Apache Hive, Amazon Redshift, Google BigQuery.
   • Querying large datasets using SQL-like syntax.
8. Data Visualization
   • Tools: Tableau, Power BI.
   • Visualizing insights from Big Data.

Phase 3: Advanced (Weeks 9–12)

9. Real-Time Analytics
   • Tools: Apache Flink, Apache Storm.
   • Use Cases: Fraud detection, real-time recommendations.
10. Cloud-Based Big Data Solutions
    • Platforms: AWS (EMR, S3, Athena), Google Cloud (BigQuery, Dataproc), Azure (HDInsight).
    • Managing Big Data workflows in the cloud.
11. Machine Learning with Big Data
    • Tools: Apache Mahout, MLlib (Spark’s Machine Learning Library).
    • Training models on large datasets.
12. Data Governance & Security
    • Ensuring data privacy and compliance.
    • Tools: Apache Ranger, Apache Atlas.

Phase 4: Real-World Applications (Weeks 13–16)

13. Building a Data Pipeline
    • Ingest data using Kafka or Flume.
    • Process data using Spark or Flink.
    • Store data in HDFS or a NoSQL database.
14. Big Data in Business Intelligence
    • Analyze customer behavior, sales trends, and market data.
    • Tools: Tableau, Power BI.
15. IoT & Big Data
    • Collect and analyze data from IoT devices.
    • Tools: Apache Kafka, Spark Streaming.
16. Ethics in Big Data
    • Addressing bias in data collection and analysis.
    • Ensuring data privacy and security.

4. Projects to Do

Beginner Projects

1. Word Count Using Hadoop MapReduce :
   • Count the frequency of words in a large text file.
   • Tools: Apache Hadoop.
2. NoSQL Database Setup :
   • Store and query data using MongoDB or Cassandra.
   • Dataset: Sample JSON or CSV data.
3. Data Ingestion with Kafka :
   • Stream tweets using Apache Kafka.
   • Tools: Twitter API, Apache Kafka.

Intermediate Projects

4. Log Analysis with Spark :
   • Analyze server logs to identify errors or patterns.
   • Tools: Apache Spark.
5. Real-Time Dashboard :
   • Build a dashboard to visualize streaming data.
   • Tools: Apache Kafka, Spark Streaming, Tableau.
6. Movie Recommendation System :
   • Build a recommendation engine using collaborative filtering.
   • Tools: Apache Spark (MLlib).

Advanced Projects

7. Fraud Detection System :
   • Detect fraudulent transactions in real-time.
   • Tools: Apache Flink, Spark Streaming.
8. Cloud-Based Data Pipeline :
   • Build a data pipeline using AWS EMR or Google Dataproc.
   • Tools: AWS S3, Google BigQuery.
9. Sentiment Analysis on Social Media :
   • Analyze sentiment in tweets or reviews using Spark.
   • Tools: Apache Spark, NLTK.

5. Valid Links for Learning Big Data Technologies

English Resources

1. freeCodeCamp :
   • Big Data Full Course .
2. Edureka :
   • Big Data and Hadoop Tutorials .
3. Simplilearn :
   • Big Data and Analytics Tutorials .
4. Apache Spark Official Channel :
   • Spark Tutorials .
5. Kafka Tutorials :
   • Kafka Crash Course .

Hindi Resources

1. CodeWithHarry :
   • Big Data Tutorial in Hindi .
2. Thapa Technical :
   • Big Data Beginner Tutorials .
3. Hitesh Choudhary :
   • Big Data Crash Course .

6. Final Tips

1. Start Small : Begin with simple projects like word count using Hadoop to understand the basics of Big Data.
2. Practice Daily : Spend at least 1 hour coding every day.
3. Focus on Tools : Master tools like Hadoop, Spark, Kafka, and NoSQL databases.
4. Stay Updated : Follow blogs like Towards Data Science , Medium , or Analytics Vidhya for the latest updates.
5. Join Communities : Engage with forums like Reddit’s r/bigdata or Discord groups for support.

100-Day Master Plan