Big Data Technologies

"Unlocking the Power of Data: Master the Tools and Techniques of the Big Data Ecosystem"
Why This Training?
In the modern data-driven era, understanding and harnessing the power of Big Data has become vital for businesses across all sectors. This training is meticulously designed to provide participants with in-depth knowledge of the Big Data ecosystem, its tools, techniques, and best practices. Unlock the potential of Big Data and gain a competitive edge in your career and business endeavors.
Duration: 30 Hours (online / virtual live session)

Who Should Attend?

 Data Engineers, Data Scientists, and Data Analysts aiming to upgrade their skills.
 IT Professionals and Software Developers looking to enter the Big Data domain.
 Business Intelligence professionals keen on leveraging Big Data tools.
 Decision-makers and managers overseeing data-driven projects.
 Any individual passionate about the world of Big Data and its applications.

Course Highlights

 Foundational to Advanced: Commence with the basics of Big Data and journey through to advanced tools and techniques.
 Hands-on Sessions: Practical exercises with top Big Data tools, ensuring applied learning.
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 Expert Guidance: Learn from industry veterans with vast experience in Big Data implementations.
 Real-World Use Cases: Understand the application of Big Data through relevant industry case studies.
 Holistic Coverage: From data ingestion and storage to analytics and visualization, gain a comprehensive view of the Big Data landscape.
 Interactive Q&A: Engage in insightful discussions and clarify your doubts.


 Basic Programming Knowledge: Familiarity with any programming language.
 Understanding of Databases: Prior knowledge of SQL will be beneficial.
 Analytical Mindset: An inherent curiosity to decipher patterns and insights from data.
 Basic Hardware Familiarity: Understanding of servers, storage, and general IT infrastructure.

Training Materials Needed by Participants

A laptop or desktop with at least 8GB RAM and a stable internet connection.
Pre-installed Big Data tools (a list will be provided prior to the training).
Relevant datasets for hands-on sessions (will be shared during the course).
Note-taking materials, whether digital or traditional.
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Training Content

Big Data Technologies

1. Introduction to Big Data (3 hours)

Objective: Equip participants with foundational knowledge of Big Data, its significance, and the inherent challenges.
  • Overview of Big Data: What is Big Data? Why is it important?
  • 3Vs of Big Data: Volume, Velocity, and Variety.
  • Real-World Applications: Case studies showcasing the value of Big Data analytics.
  • Challenges with Traditional Systems: Why we needed a new set of tools and methodologies.

2. Big Data Ecosystem & Components (3 hours)

Objective: Provide a comprehensive overview of the Big Data ecosystem and familiarize participants with core components.
  • Introduction to the Big Data Ecosystem: A view of various tools and platforms.
  • Data Ingestion Tools: Introduction to Flume and Sqoop.
  • Storage Solutions: Basics of HDFS (Hadoop Distributed File System).

3. Deep Dive into Hadoop (3 hours)

Objective: Enable participants to understand Hadoop's architecture, components, and its data processing methodology.
  • Hadoop Components: MapReduce, YARN.
  • Architecture and Workflow: How Hadoop processes data.
  • Hands-on: Running a basic MapReduce job.

4. Apache Spark & Its Components (3 hours)

Objective: Introduce the capabilities of Apache Spark and its significance in fast, in-memory data processing.
  • Introduction to Apache Spark: Understanding its rise and significance.
  • Core Components: Spark RDDs, Spark DataFrames.
  • Spark Modules: SparkSQL, Spark Streaming, MLlib.
  • Hands-on: Setting up a basic Spark application.

5. Data Storage - NoSQL Databases (3 hours)

Objective: Elucidate the need for NoSQL databases, their types, and practical use cases.
  • Why NoSQL?: Differences between SQL and NoSQL.
  • Types of NoSQL Databases: Document, Columnar, Key-Value, Graph.
  • Introduction to MongoDB and Cassandra.
  • Hands-on: Setting up and inserting data into a NoSQL database.

6. Big Data on Cloud Platforms (3 hours)

Objective: Highlight the advantages of leveraging cloud platforms for Big Data solutions and introduce prominent cloud-based Big Data services.
  • Overview: Advantages of cloud platforms for Big Data.
  • Big Data on AWS: Introduction to services like EMR, Redshift, and Kinesis.
  • Big Data on Azure: Overview of HDInsight, Azure Stream Analytics.
  • Hands-on: Setting up a Big Data environment on a chosen cloud platform.

7. Advanced Analytics & Data Processing (3 hours)

Objective: Dive into advanced data analytics tools and techniques, emphasizing real-time data processing.
  • Introduction to Apache Kafka: Real-time data processing.
  • Data Analytics with Apache Hive: SQL-like querying with HiveQL.
  • Data Workflow Orchestration with Apache Airflow.
  • Hands-on: Setting up a data pipeline with Kafka, processing with Hive.

8. Data Visualization & BI Tools (3 hours)

Objective: Impress upon participants the importance of effective data visualization and familiarize them with top BI tools in the Big Data realm.
  • Importance of Data Visualization in Big Data.
  • Introduction to Tableau and Power BI: Connecting with Big Data sources, creating dashboards.
  • Open-Source Alternatives: Apache Superset.
  • Hands-on: Creating a dashboard using live Big Data streams.

9. Security & Best Practices in Big Data (3 hours)

Objective: Discuss the challenges and solutions associated with Big Data security and introduce best practices for robust Big Data implementations.
  • Challenges in Big Data Security.
  • Tools & Techniques: Apache Ranger, Apache Knox.
  • Data Governance and Cataloging with Apache Atlas.
  • Hands-on: Setting up basic security rules using Ranger.

10. Recap, Emerging Trends & Q&A (3 hours)

Objective: Consolidate the course's learnings, acquaint participants with emerging Big Data trends, and address any outstanding questions.
  • Review of the Big Data Landscape: What we've learned.
  • Emerging Trends: New tools, platforms, and methodologies on the horizon.
  • Interactive Case Study: End-to-end processing of a Big Data task.
  • Q&A Session: Addressing questions, uncertainties, and curiosities.
With clear objectives set for each session, participants will have a roadmap of what to expect and the skills they will acquire by the end of the training.
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