Big Data

**Objectives:**

– Understand the concept and importance of Big Data

– Familiarize with the ecosystem and technologies

**Topics:**

– **What is Big Data?**

  – Definition and characteristics (Volume, Velocity, Variety, Veracity, Value)

  – The importance of Big Data in various industries

– **Big Data Ecosystem Overview:**

  – Hadoop ecosystem

  – Spark ecosystem

  – Data lakes vs. Data warehouses

**Practical Exercises:**

– Case studies of Big Data applications in different industries

– Discussion on real-world Big Data challenges and solutions

**Objectives:**

– Gain an overview of core Big Data technologies

– Understand the purpose and use cases for each technology

**Topics:**

– **Hadoop:**

  – HDFS (Hadoop Distributed File System)

  – MapReduce

– **Apache Spark:**

  – RDDs (Resilient Distributed Datasets)

  – DataFrames and Spark SQL

– **Other Technologies:**

  – Apache Flink

  – Apache Kafka

**Practical Exercises:**

– Set up a Hadoop and Spark environment using cloud-based platforms (e.g., AWS, Google Cloud)

– Run a sample MapReduce and Spark job

**Objectives:**

– Master the Hadoop ecosystem components

– Learn to set up and manage Hadoop clusters

**Topics:**

– **HDFS:**

  – Architecture and file operations

  – Data replication and fault tolerance

– **MapReduce:**

  – Understanding Map and Reduce functions

  – Writing and optimizing MapReduce jobs

– **YARN (Yet Another Resource Negotiator):**

  – Resource management and job scheduling

**Practical Exercises:**

**Objectives:**

– Understand the core concepts of Apache Spark

– Get hands-on experience with Spark’s API

**Topics:**

– **Spark Core Concepts:**

  – Spark architecture and components

  – Spark RDDs and transformations/actions

– **Spark SQL:**

  – DataFrames and SQL queries

  – Integrating Spark SQL with other data sources

**Practical Exercises:**

– Develop and execute Spark jobs using sample data

– Perform data manipulations and aggregations with Spark SQL

**Objectives:**

– Learn methods for data ingestion and processing

– Get familiar with ETL (Extract, Transform, Load) processes

**Topics:**

– **Data Ingestion Tools:**

  – Apache Kafka for real-time data streaming

  – Apache Flume for batch data ingestion

– **ETL Processes:**

  – Data extraction techniques

  – Data transformation and loading strategies

**Practical Exercises:**

– Configure and use Apache Kafka for data streaming

– Create ETL pipelines using Apache Flume or custom scripts

**Objectives:**

– Master advanced features and optimizations in Spark

– Learn about machine learning with Spark

**Topics:**

– **Spark Performance Tuning:**

  – Caching and persistence

  – Task scheduling and optimization

– **Spark MLlib:**

  – Introduction to machine learning with Spark

  – Common algorithms and their use cases

**Practical Exercises:**

– Optimize Spark jobs for performance

– Implement a machine learning model using Spark MLlib

**Objectives:**

– Apply analytical techniques to Big Data

– Understand data visualization and reporting

**Topics:**

– **Big Data Analytics:**

  – Exploratory Data Analysis (EDA)

  – Statistical and predictive analysis

– **Data Visualization:**

  – Tools for visualization (e.g., Tableau, D3.js)

  – Creating dashboards and reports

**Objectives:**

– Understand the importance of data security and privacy

– Learn about best practices and tools for securing Big Data

**Topics:**

– **Data Security:**

  – Encryption and access control

  – Data masking and anonymization

– **Privacy Regulations:**

  – GDPR, CCPA, and other data protection regulations

**Practical Exercises:**

– Implement encryption and access control in a Big Data environment

– Review and apply privacy regulations to sample datasets