Introduction to Big Data Technologies Training Course.
Introduction
Big Data has become a central focus in industries ranging from healthcare and finance to e-commerce and entertainment. The ability to collect, process, and analyze vast amounts of data is transforming business operations and decision-making. This course introduces the key technologies and tools used in Big Data processing and analysis, offering participants a solid foundation in understanding and working with large datasets. The course covers the concepts, frameworks, and technologies behind Big Data, such as Hadoop, Spark, and NoSQL databases, and provides hands-on experience in working with these tools to process and analyze large-scale data.
Objectives
By the end of this course, participants will:
- Understand the concept of Big Data and its challenges.
- Learn about the Hadoop ecosystem, including HDFS, MapReduce, and YARN.
- Get hands-on experience with Apache Spark for large-scale data processing.
- Understand the importance of distributed computing and parallel processing.
- Learn about NoSQL databases like MongoDB and Cassandra for handling unstructured data.
- Gain familiarity with Big Data tools for data ingestion, storage, processing, and visualization.
- Explore the applications of Big Data technologies in real-world scenarios.
Who Should Attend?
This course is ideal for:
- Data engineers, data scientists, and analysts who want to work with large datasets.
- IT professionals looking to transition into Big Data technologies.
- Business professionals interested in leveraging Big Data for decision-making.
- Students or beginners seeking to learn the fundamental technologies behind Big Data.
Day 1: Introduction to Big Data and Hadoop Ecosystem
Morning Session: What is Big Data?
- Defining Big Data: Volume, Variety, Velocity, and Veracity (4 V’s)
- Challenges of handling Big Data: Storage, scalability, and data quality
- Key differences between traditional databases and Big Data systems
- Applications of Big Data in various industries (e.g., healthcare, finance, marketing)
- Overview of the Big Data ecosystem: Tools, frameworks, and technologies
- Introduction to distributed computing and parallel processing
Afternoon Session: Introduction to Hadoop Ecosystem
- What is Hadoop? History, components, and use cases
- Understanding Hadoop Distributed File System (HDFS): Storage architecture, blocks, replication, and fault tolerance
- Hadoop MapReduce: Parallel processing, programming model, and use cases
- YARN (Yet Another Resource Negotiator): Resource management and job scheduling in Hadoop
- Hands-on: Setting up a basic Hadoop environment and exploring HDFS
Day 2: Working with Hadoop and MapReduce
Morning Session: Data Processing with MapReduce
- Introduction to MapReduce: Concept, input/output, and stages (Map, Shuffle, Reduce)
- Writing MapReduce programs: Mapper and Reducer functions
- Real-world use cases for MapReduce in processing large datasets
- Hands-on: Writing a simple MapReduce program to process text data
Afternoon Session: Advanced Hadoop Components
- Hadoop Common: Libraries and utilities used across the Hadoop ecosystem
- Hadoop Hive: SQL-like queries on Big Data for data warehousing and analysis
- Hadoop Pig: High-level scripting platform for analyzing large datasets
- Hadoop HBase: Columnar NoSQL database for real-time data access
- Hands-on: Querying data using Hive and processing data with Pig
Day 3: Introduction to Apache Spark for Big Data Processing
Morning Session: What is Apache Spark?
- Understanding Apache Spark: Overview, architecture, and components
- Benefits of Spark over Hadoop MapReduce: Speed, ease of use, and in-memory processing
- Spark Core: RDDs (Resilient Distributed Datasets) and transformations/actions
- Spark SQL: Working with structured data using SQL-like queries
- Introduction to Spark MLlib: Machine learning on Big Data
- Hands-on: Running a basic Spark job on a sample dataset
Afternoon Session: Advanced Spark Features
- Spark Streaming: Real-time data processing and stream analytics
- Spark GraphX: Graph processing and analytics
- Spark MLlib: Using Spark for machine learning tasks like classification and regression
- Spark on Cloud: Running Spark on cloud platforms like AWS EMR and Azure HDInsight
- Hands-on: Using Spark SQL and Spark MLlib for basic data analysis and modeling
Day 4: NoSQL Databases and Data Ingestion
Morning Session: Introduction to NoSQL Databases
- What is NoSQL? The rise of NoSQL databases in Big Data processing
- Types of NoSQL databases: Document, key-value, column-family, and graph databases
- Understanding MongoDB: Document-oriented database and use cases
- Understanding Cassandra: Distributed column-family store for handling large datasets
- Hands-on: Setting up a MongoDB database and inserting/retrieving data
Afternoon Session: Data Ingestion and ETL (Extract, Transform, Load)
- Introduction to data ingestion: Techniques for importing Big Data from various sources (e.g., sensors, logs, APIs)
- Batch vs. real-time data processing
- Apache Flume and Apache Kafka for data ingestion in real-time
- ETL pipelines: Tools for data transformation and storage (e.g., Apache NiFi, Talend)
- Hands-on: Setting up a data ingestion pipeline with Apache Kafka
Day 5: Big Data Analytics and Visualization
Morning Session: Big Data Analytics
- Introduction to Big Data analytics: Tools and techniques for analyzing large datasets
- Tools for Big Data analytics: Apache Zeppelin, Jupyter Notebooks, and Tableau for visualization
- Introduction to machine learning on Big Data: Using Spark MLlib and other tools for model building
- Big Data analytics in the cloud: Cloud solutions for storage and processing (e.g., AWS S3, Google BigQuery, Azure Data Lake)
- Hands-on: Analyzing a large dataset using Spark and Apache Zeppelin
Afternoon Session: Big Data in Action and Future Trends
- Real-world use cases: Predictive analytics, recommendation systems, fraud detection, and social media analysis
- Scalability and performance optimization in Big Data processing
- The future of Big Data technologies: AI, edge computing, and Internet of Things (IoT)
- Big Data privacy and security challenges: Ensuring data protection and compliance with regulations (e.g., GDPR)
- Final project: Participants work on a project where they will use Big Data tools to solve a real-world problem
Materials and Tools:
- Software and tools: Hadoop, Spark, MongoDB, Kafka, Hive, Cassandra
- Access to a cloud-based Big Data platform (e.g., AWS, Google Cloud, or Azure) for hands-on exercises
- Recommended readings and resources: Official documentation, tutorials, and case studies on Big Data tools
Conclusion and Final Assessment
- Recap of key concepts: Hadoop, Spark, NoSQL, data ingestion, and Big Data analytics
- Final project: Participants apply what they’ve learned to process and analyze a large dataset
- Certification of completion for those who successfully complete the course and final project