Big Data Technologies

Mastering Big Data Tools and Techniques for Scalable Data Processing and Analytics

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MODE
Virtual (Google Meet)

TYPE
Mentor Based

LEVEL
Moderate

DURATION
3 Weeks

About

The Big Data Technologies program provides a comprehensive understanding of Big Data processing frameworks, data storage solutions, distributed computing, and real-time analytics. Participants will gain hands-on experience with Hadoop, Spark, Kafka, NoSQL databases, and cloud-based Big Data solutions, preparing them for careers in data engineering, analytics, and artificial intelligence.

Aim

To equip participants with the skills and knowledge required to process, store, analyze, and manage massive datasets using modern Big Data technologies and frameworks, enabling data-driven decision-making and scalable computing solutions.

Program Objectives

To introduce participants to Big Data concepts, tools, and architectures.
To train participants in distributed computing frameworks (Hadoop, Spark, Kafka, etc.).
To provide hands-on experience with NoSQL databases and real-time analytics.
To explore AI and machine learning applications on Big Data platforms.
To prepare professionals for Big Data careers in analytics, engineering, and AI.

Program Structure

Week 1: Introduction to Big Data and Data Processing Frameworks

Module 1: Understanding Big Data and Its Ecosystem

What is Big Data?
- Characteristics: Volume, Velocity, Variety, Veracity, and Value (5Vs).
- Challenges in big data processing and storage.
Big Data Use Cases
- Applications in healthcare, finance, e-commerce, IoT, and AI.
Big Data Processing Architectures
- Batch Processing vs. Stream Processing.
- Lambda vs. Kappa Architecture.

Hands-On Lab:

Setting up a big data environment using Apache Hadoop and Spark.

Module 2: Distributed Storage with Hadoop and HDFS

Introduction to Hadoop Ecosystem
- Hadoop Distributed File System (HDFS) architecture.
- YARN (Yet Another Resource Negotiator) and MapReduce.
Data Storage and Management in HDFS
- File formats: Parquet, Avro, ORC.
- Data ingestion tools: Apache Sqoop and Flume.

Hands-On Lab:

Uploading and retrieving data from HDFS using CLI and APIs.

Week 2: Big Data Processing and NoSQL Databases

Module 3: Data Processing with Apache Spark

Introduction to Apache Spark
- Difference between MapReduce and Spark.
- RDDs (Resilient Distributed Datasets), DataFrames, and Datasets.
Spark Components
- Spark SQL, Spark Streaming, MLlib (Machine Learning), GraphX.

Hands-On Lab:

Writing Spark applications for batch data processing using PySpark.

Module 4: NoSQL Databases for Big Data

Types of NoSQL Databases
- Document-based (MongoDB), Columnar (Apache Cassandra), Key-Value (Redis), Graph (Neo4j).
Database Scalability and High Availability
- Data partitioning, sharding, replication strategies.

Hands-On Lab:

Setting up a NoSQL database (MongoDB/Cassandra) and performing queries.

Week 3: Real-Time Big Data Processing, Cloud, and Advanced Analytics

Module 5: Real-Time Big Data Processing and Streaming

Introduction to Real-Time Streaming Frameworks
- Apache Kafka, Apache Flink, Apache Storm, Spark Streaming.
Event-Driven Architecture and Messaging Systems
- Kafka topics, producers, consumers, and stream processing.

Hands-On Lab:

Setting up Kafka and streaming real-time data.

Module 6: Cloud-Based Big Data Solutions and Future Trends

Big Data in the Cloud
- AWS EMR, Google BigQuery, Azure HDInsight.
Machine Learning and AI in Big Data
- Using big data for predictive analytics and AI/ML.

Hands-On Lab:

Deploying a big data pipeline on a cloud platform.

Participant’s Eligibility

Data engineers and software developers
Data analysts and business intelligence professionals
Cloud and DevOps engineers
Students and researchers in Big Data and AI applications

Program Outcomes

Mastery of Hadoop, Spark, Kafka, and NoSQL databases
Hands-on experience in data processing and real-time analytics
Ability to build Big Data pipelines for AI and ML applications
Understanding of cloud-based Big Data solutions and architectures
Readiness for Big Data certifications (Cloudera, AWS Big Data, Google Cloud Certified Data Engineer)

Fee Structure

Standard Fee: INR ~~16,998~~ USD ~~224~~

Discounted Fee: INR 8,499 USD 112

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!

List of Currencies