Course Overview:

Big Data is transforming industries by enabling organizations to harness the power of massive, complex datasets to drive innovation, optimize operations, and improve decision-making. This 5-day advanced training course provides a comprehensive overview of Big Data technologies, architecture, and real-world applications. Participants will learn how to work with the key tools and frameworks that are revolutionizing data management, storage, and analysis, including Hadoop, Spark, NoSQL databases, and cloud-based technologies. The course includes hands-on labs and practical use cases to equip participants with the skills needed to manage and analyze Big Data effectively.

Introduction:

As businesses and organizations increasingly generate and store vast amounts of data, traditional data processing systems are no longer sufficient. Big Data technologies have emerged to address the challenges of processing, storing, and analyzing data at scale. From managing petabytes of unstructured data to executing complex analytics in real-time, Big Data has opened new frontiers in data-driven decision-making.

This course dives deep into the technologies and tools that power the Big Data ecosystem. Participants will explore how these technologies work together to enable efficient data storage, processing, and analysis. In addition to learning about the technical tools, participants will examine real-world applications of Big Data in industries such as healthcare, finance, retail, and social media.

Objectives:

By the end of this course, participants will be able to:

1. Understand Big Data Fundamentals:
   • Define what constitutes Big Data and its key characteristics (Volume, Velocity, Variety, Veracity, and Value).
   • Explore the importance and challenges of Big Data in modern business environments.
2. Master Big Data Storage and Management:
   • Understand different data storage models (structured, semi-structured, and unstructured).
   • Learn about Hadoop Distributed File System (HDFS), cloud storage, and NoSQL databases (e.g., HBase, Cassandra).
3. Work with Big Data Processing Frameworks:
   • Understand the Hadoop ecosystem (MapReduce, YARN, Hive, Pig).
   • Gain hands-on experience with Apache Spark for fast, in-memory processing.
4. Explore Real-Time Data Processing:
   • Learn how to process streaming data using Apache Kafka, Apache Flink, and Spark Streaming.
5. Integrate and Analyze Big Data:
   • Perform data wrangling and analysis using tools like Apache Hive and Apache Impala.
   • Implement machine learning models and analytics on Big Data using Spark MLlib.
6. Learn Big Data Use Cases and Applications:
   • Examine real-world Big Data applications in industries such as healthcare, e-commerce, finance, and telecommunications.
   • Understand the integration of Big Data with IoT, cloud computing, and artificial intelligence.
7. Implement Big Data Solutions on the Cloud:
   • Understand cloud platforms like AWS, Azure, and Google Cloud for scalable Big Data solutions.
   • Learn to use cloud-based services such as Amazon EMR, Google Dataproc, and Azure HDInsight for processing Big Data workloads.
8. Explore the Future of Big Data Technologies:
   • Discuss trends in the Big Data ecosystem, including edge computing, automation, and AI-driven analytics.

Who Should Attend?:

This training course is ideal for professionals who are involved in managing, analyzing, or deriving insights from large datasets. Participants should have a solid foundation in data analytics or software engineering and seek to expand their knowledge of Big Data tools and applications. Specific audiences include:

1. Data Engineers: Professionals responsible for building and maintaining data pipelines and architecture.
2. Data Scientists and Analysts: Individuals who want to scale their data analysis to handle larger and more complex datasets.
3. Software Developers: Developers interested in learning how to build Big Data applications and integrate with Big Data technologies.
4. IT Professionals: System administrators, database administrators, and cloud architects looking to expand their knowledge of Big Data storage and processing.
5. Business Analysts and Managers: Those who need to understand the potential of Big Data in driving business decisions and how to leverage the technology in their organizations.
6. Students: Graduate-level students in computer science, data science, or related fields who want to specialize in Big Data technologies.

Course Schedule and Topics:

Day 1: Introduction to Big Data & Storage Technologies

Objectives: Understand the basics of Big Data, the key challenges, and the different technologies used for storage and management.

• Morning Session:
  • Introduction to Big Data: What is Big Data? Key characteristics (5 Vs).
  • The challenges of Big Data: scalability, complexity, data governance, and security.
  • Overview of Big Data applications across various industries (finance, healthcare, e-commerce).
• Afternoon Session:
  • Big Data Storage Options: Structured vs. unstructured data.
  • Hadoop Distributed File System (HDFS): Architecture and how it stores data at scale.
  • Introduction to NoSQL databases: HBase, Cassandra, MongoDB.
  • Hands-on exercise: Working with HDFS and basic commands.

Day 2: Hadoop Ecosystem and Batch Processing

Objectives: Learn the components of the Hadoop ecosystem and how to process data in batch using MapReduce and other Hadoop tools.

• Morning Session:
  • Overview of the Hadoop Ecosystem: Core components (HDFS, YARN, MapReduce).
  • MapReduce Programming Model: Understanding Map and Reduce functions.
  • Data Storage with HDFS: Storing and retrieving large datasets.
• Afternoon Session:
  • Hive: A data warehousing tool built on top of Hadoop, used for querying large datasets using SQL-like syntax.
  • Pig: A platform for analyzing large datasets using a simple scripting language.
  • Hands-on exercise: Writing basic MapReduce jobs and using Hive for querying datasets.

Day 3: Apache Spark and In-Memory Processing

Objectives: Gain hands-on experience with Apache Spark for fast, in-memory Big Data processing.

• Morning Session:
  • Introduction to Apache Spark: The need for fast data processing and Spark’s architecture.
  • Key Spark components: Spark Core, Spark SQL, Spark Streaming, Spark MLlib.
  • Spark vs. Hadoop MapReduce: Performance comparisons.
• Afternoon Session:
  • Spark Programming: Using RDDs and DataFrames for data manipulation.
  • Spark Streaming: Real-time processing with Spark.
  • Hands-on exercise: Running Spark jobs and analyzing data using Spark SQL.

Day 4: Real-Time Data Processing with Kafka and Flink

Objectives: Learn how to process and analyze streaming data with Apache Kafka, Flink, and Spark Streaming.

• Morning Session:
  • Introduction to Stream Processing: Real-time data processing vs. batch processing.
  • Apache Kafka: A distributed event streaming platform for building real-time data pipelines.
  • Integrating Kafka with Spark Streaming: Processing streaming data.
• Afternoon Session:
  • Apache Flink: A framework for stream processing and real-time analytics.
  • Hands-on exercise: Building a real-time data pipeline with Kafka and Spark/Flink.

Day 5: Advanced Topics, Cloud Integration & Use Cases

Objectives: Explore advanced Big Data applications, cloud-based Big Data solutions, and real-world use cases.

• Morning Session:
  • Cloud-based Big Data Solutions: Big Data processing on AWS, Google Cloud, and Azure.
  • Big Data Machine Learning: Introduction to Spark MLlib and integrating machine learning models on Big Data.
  • IoT and Big Data: Integrating Big Data with Internet of Things (IoT) devices for real-time analytics.
• Afternoon Session:
  • Big Data Use Cases: Examining real-world examples from healthcare, retail, financial services, and telecommunications.
  • Ethics in Big Data: Privacy concerns, data governance, and bias in machine learning models.
  • The Future of Big Data: Trends in edge computing, automation, and AI-driven analytics.
  • Course Wrap-up and Q&A.