Build practical Big Data skills with Codelyra's Big Data Training in Chennai. Learn Hadoop, HDFS, MapReduce, Hive, Spark, SQL, Python, AWS Big Data services and data-processing concepts through structured training and industry-relevant capstone projects.
Whether you are a beginner, graduate, software professional, data enthusiast or career switcher, the program takes you from Big Data fundamentals to distributed data processing and cloud-based analytics.
Choose a learning schedule that works with your academic or professional commitments and explore the available Big Data training formats at Codelyra.
| Batch | Suitable For | Learning Format |
|---|---|---|
| Weekday Batch | Students & Job Seekers | Regular instructor-led training |
| Weekend Batch | Working Professionals | Weekend-focused sessions |
| Flexible Batch | Busy Learners | Flexible scheduling options |
| Fast-Track Batch | Intensive Learners | Accelerated learning |
When comparing Big Data courses, consider the curriculum, practical training, Hadoop and Spark coverage, cloud exposure, project work, trainer expertise, certification options and career support.
Check Upcoming BatchBig Data is more than simply learning Hadoop commands. Modern data platforms involve distributed storage, large-scale processing, SQL analytics, streaming, cloud infrastructure and data engineering workflows.
Codelyra's learning approach connects these concepts through a progressive curriculum, helping learners build their understanding from foundational topics to practical Big Data technologies.
Build a foundation before moving into distributed data technologies. The learning path can begin with:
From there, learners progress toward Hadoop, Spark and cloud-based Big Data processing.
The program brings together three important areas of Big Data learning:
This provides exposure to distributed-data concepts alongside cloud-based data workflows.
Practice Big Data concepts through controlled environments and practical exercises rather than relying only on theoretical explanations.
Learners can apply concepts while working with data processing, distributed systems and cloud-oriented workflows.
Complete six capstone projects covering different Big Data application areas, including:
The curriculum focuses on skills that learners can demonstrate during technical interviews and project discussions.
Practical concepts, project experience and technical understanding can help learners explain how Big Data technologies are applied in application and data-processing workflows.
Learners can receive applicable Codelyra course certifications based on the program's completion and assessment requirements.
External vendor certifications are separate credentials and should not be represented as Codelyra certifications.
Career support can include:
Placement support is intended as career assistance and should not be presented as a guaranteed job offer.
Flexible timing options can make the program suitable for both students and working professionals.
Available schedules and delivery formats can be confirmed with the Codelyra course team based on the current training intake.
From data fundamentals and Python to Hadoop, Spark and AWS, the learning path is designed to connect concepts with practical exercises and project-based application.
Learn Big Data step by step, starting with data fundamentals, Linux and SQL, then progressing through Hadoop, HDFS, MapReduce, Hive, Spark, PySpark, AWS, streaming, data engineering and practical capstone projects.
Build the core knowledge required to work with large-scale data environments.
Understand distributed storage, processing and cluster resource management.
Work with data warehousing concepts, HiveQL, ETL and common Big Data file formats.
Learn distributed data processing with Spark, Spark SQL and PySpark.
Understand cloud fundamentals, AWS storage and services used in Big Data workflows.
Introduction to AWS services relevant to Big Data workflows.
Explore analytical approaches for understanding patterns and business-oriented metrics.
Understand the fundamentals of event-driven systems and real-time data processing.
Connect ingestion, transformation, storage, monitoring and deployment concepts into practical workflows.
Apply the concepts learned throughout the Big Data Hadoop course to practical projects that combine Hadoop, Spark, SQL, Python and cloud-based data workflows.
By completing the course, learners can develop practical skills across Big Data technologies, distributed processing, cloud platforms, analytics and data engineering workflows.
Learn how data can move through different stages, from its original source to processing, analysis and reporting.
Get practical exposure to commonly used Big Data technologies across distributed storage, data processing, analytics, streaming, cloud platforms, programming and development workflows.
Distributed Big Data ecosystem
Distributed storage
Distributed batch processing
Cluster resource management
SQL-based Big Data analytics
Distributed data processing
Python-based Spark processing
Large-scale SQL analytics
Data processing & automation
Data querying
Event streaming fundamentals
Cloud object storage
Managed Big Data processing
Data integration & cataloging
Serverless data querying
Cloud data warehousing
Version control
Big Data environment
These tools are introduced in context, helping learners understand how distributed storage, processing, querying, cloud services, streaming and version control can work together in Big Data projects.
Apply Hadoop, Spark, PySpark, SQL, Python, Kafka and AWS concepts through practical Big Data projects covering batch processing, streaming analytics, data lakes and end-to-end data pipelines.
Analyze large-scale customer and transaction data to identify purchasing patterns and generate useful business insights.
Build a simulated streaming pipeline for analyzing customer events as they are generated.
Process a large financial transaction dataset and identify patterns that may require further investigation.
Process large volumes of application and server logs to identify usage patterns and operational trends.
Build a cloud-based data workflow using AWS services for storage, cataloging, processing and analytics.
Build an end-to-end Big Data pipeline combining data ingestion, distributed processing, analytics and cloud services.
Build practical Big Data skills through structured learning, hands-on projects, cloud concepts, and career-oriented support designed for different learner needs.
Learning schedules are designed to accommodate students and working professionals where available.
Receive applicable course certifications according to Codelyra's certification requirements.
Available payment or EMI options can help eligible learners manage course fees, subject to applicable terms.
Career-oriented assistance can help learners prepare for job opportunities and technical discussions.
Work on practical Big Data scenarios involving Hadoop, Spark, SQL, Python, and AWS to connect concepts with project-based learning.
Learn how Big Data concepts connect with modern cloud services and practical data-processing workflows.
From Hadoop and Spark to SQL, Python, AWS, and real-world project workflows, the course brings key Big Data concepts together in a structured learning path.
Find clear answers to common questions about Big Data, Hadoop, Spark, Python, AWS, course suitability, practical training, and learning options.
Big Data refers to datasets that are large, fast-changing, or complex enough to require specialized technologies and distributed approaches for storage, processing, and analysis.
Hadoop provides an ecosystem for distributed storage and processing of large datasets. HDFS is used for distributed storage, while components such as MapReduce and YARN support distributed processing and resource management.
Hadoop is an ecosystem that includes distributed storage and processing technologies. Spark is a distributed processing engine designed for large-scale data workloads and supports batch, SQL, streaming, and other processing use cases.
They are not simply direct alternatives. Hadoop includes technologies such as HDFS and YARN, while Spark focuses primarily on distributed data processing. Modern data platforms can use Spark alongside cloud storage and other data technologies.
Python is not mandatory for every Big Data technology, but it is highly useful for PySpark, data processing, automation, and analytics.
The curriculum is designed to connect Big Data concepts with AWS services such as S3, EMR, Glue, Athena, and other relevant cloud technologies.
Depending on the workload, AWS provides services for cloud storage, processing, data integration, querying, streaming, and data warehousing. Examples include S3, EMR, Glue, Athena, Kinesis, and Redshift.
Yes. A structured learning path can start with Linux, SQL, Python, and Big Data fundamentals before introducing Hadoop, Spark, and cloud technologies.
The right course depends on factors such as syllabus depth, practical labs, Hadoop and Spark coverage, AWS exposure, projects, trainer expertise, certification, and career support. Students should compare these factors rather than relying only on rankings.
Look for a curriculum covering:
Yes. Big Data training can be suitable for working professionals, particularly when flexible or weekend schedules are available.
No. Big Data focuses heavily on handling and processing large-scale datasets, while data analytics focuses on extracting insights from data. There is significant overlap between the two fields.
Review the course curriculum, project structure, learning options, and practical training details before choosing a Big Data learning path.
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