95 Hours
Intensive Hands-On Training
Spark • Kafka • Hadoop • SQL
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Become a job-ready Data Engineer with Hadoop HDFS/Hive, Apache Spark (Core/SQL/Streaming), Kafka, Airflow, NoSQL, data modeling, and cloud deployments (AWS/Azure). Build portfolio pipelines and earn a QR-verified certificate.
Curriculum includes batch & streaming ETL, partitioning & file formats (Parquet/ORC), optimization, lineage, monitoring, and CI/CD for data workflows.
Unlock the power of massive datasets with CDPL’s Hero Program. Build production-grade batch & streaming pipelines using Apache Spark, Kafka, Hadoop, Airflow, and cloud data platforms.
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Intensive Hands-On Training
Practical : Theory
Years of Expertise
Job Assistance
Doubt Solving
Global Certification
Practice on real datasets with orchestration, observability, and cost-aware design for roles like Data Engineer, Analytics Engineer, and Platform Engineer.
*Outcomes vary by prior experience, pace, and project depth.
Build scalable, real-time data pipelines and fault-tolerant architectures with Kafka, Spark, Hadoop and modern cloud platforms. Become the Data Engineer companies trust for petabyte-scale systems.
Design event-driven architectures, build producers/consumers, and process streams with Kafka + Kafka Connect + Schema Registry.
Batch & streaming with Spark: DataFrame APIs, Spark SQL, tuning, partitioning, checkpoints, and fault tolerance.
Build medallion layouts, work with Parquet/Delta/Iceberg, and serve BI/ML from curated layers.
Schedule pipelines with Airflow, transform with dbt, add data quality checks and lineage.
Implement access controls, PII masking, audit trails, and compliance-ready logging at scale.
Optimize storage/compute, right-size clusters, cache smartly, and monitor SLAs & costs.
A production-grade platform spanning event ingestion, stream/batch processing, and serving layers. You’ll implement CDC/ETL, medallion lakehouses, and low-latency endpoints to power BI and ML.
Keywords: big data engineering, kafka streaming, spark data processing, data lakehouse, airflow orchestration, dbt transformations, cloud data pipelines, scalable ETL, real-time analytics, data platform architecture.
An industry-aligned Big Data Engineering pathway from Hadoop & Spark to Kafka, cloud platforms, lakehouse architecture, and orchestration.
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Master HDFS architecture, MapReduce paradigms, YARN scheduling, and Hive for warehousing with partitions & bucketing.
Build streaming & batch pipelines using Spark Core/SQL, DataFrames, structured streaming, and MLlib with performance tuning.
Design fault-tolerant, high-throughput pipelines: topics, partitions, schema registry, exactly-once semantics, and connectors.
Deploy EMR, Databricks, Dataflow, and Synapse; storage layers (S3/GCS/ADLS), IAM, autoscaling, and cost-efficient jobs.
Implement Delta Lake & lakehouse patterns, dimensional modeling, Airflow DAGs, CI/CD, lineage & observability basics.
*Module order may vary slightly based on cohort needs and instructor discretion.
Build production-grade pipelines used by top enterprises. Practice streaming, lakehouse, ETL, and governance with battle-tested tools and patterns.
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Detect anomalous transactions at scale with streaming ingestion, fast features, and low-latency scoring.
Design a lakehouse with ACID tables, schema evolution, and query engines for BI & ML consumers.
Ingest 1M+ device events/day and power operational dashboards & alerts with cost-aware design.
Build resilient DAGs with retries, SLAs, data quality checks, and lineage for auditability.
Model a star/snowflake warehouse and deliver fast marts for BI, finance, and growth teams.
Implement PII detection, tokenization, and access controls to meet GDPR/DPDP compliance.
These industry-aligned projects emphasize scalability, governance, and real SLAs—ideal for Data Engineer, Analytics Engineer, and Platform Engineer roles.
*Scope may vary by dataset, domain, and pace.
Real reviews from graduates of our Big Data Engineering program—covering Kafka, Spark, Hadoop, Airflow, dbt, and cloud (AWS/GCP/Azure). Portfolio-ready projects and job-focused outcomes.
“Landed a Big Data Engineer role at an MNC in 8 weeks. Kafka streams + Spark tuning helped me ace the system design round.”
“The Spark and Kafka projects were industry-grade. I deployed a streaming pipeline with checkpoints and exactly-once semantics.”
“Best investment for my cloud data engineering career. Lakehouse with Delta + Airflow orchestration stood out in interviews.”
“Hands-on governance and cost optimization. Learned to right-size clusters and add data quality with dbt tests.”
“From on-prem Hadoop to cloud-native pipelines on AWS. Clear rubrics, PR reviews, and strong portfolio storytelling.”
“Interview prep mirrored real scenarios—CDC ingestion, schema evolution, and SLA monitoring using metrics & alerts.”
“Landed a Big Data Engineer role at an MNC in 8 weeks. Kafka streams + Spark tuning helped me ace the system design round.”
“The Spark and Kafka projects were industry-grade. I deployed a streaming pipeline with checkpoints and exactly-once semantics.”
“Best investment for my cloud data engineering career. Lakehouse with Delta + Airflow orchestration stood out in interviews.”
“Hands-on governance and cost optimization. Learned to right-size clusters and add data quality with dbt tests.”
“From on-prem Hadoop to cloud-native pipelines on AWS. Clear rubrics, PR reviews, and strong portfolio storytelling.”
“Interview prep mirrored real scenarios—CDC ingestion, schema evolution, and SLA monitoring using metrics & alerts.”
Read independent reviews of our Big Data Engineering course. Alumni highlight Kafka and Spark projects, cloud deployments, orchestration with Airflow, and job placements.
High-demand roles across data infrastructure, cloud data platforms, streaming systems, and modern lakehouse stacks.
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*Logos are illustrative of hiring potential. Openings vary by location, skills, and experience.
Whether you’re upskilling or switching careers, this Big Data Engineering program turns Spark, Kafka, Hadoop, Airflow, and cloud data platforms into production-ready skills with a recruiter-friendly portfolio.
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Transition from app/backend engineering to high-impact Big Data roles building scalable pipelines.
Scale from GB to PB—streaming ingestion, lakehouse modeling, and analytics-ready marts.
Master distributed systems, observability, and security for data platforms on cloud.
Launch a Big Data Engineering career with mentor-guided projects and placement support.
Perfect for Software Engineers, Data Analysts/ETL Developers, IT & SysAdmins, and Fresh Graduates targeting Data Engineer, Analytics Engineer, and Platform Engineer roles.
*Learning paths adapt by background and pace.
Build production-grade data pipelines and lakehouse platforms using a curated stack recruiters trust: Hadoop, Spark, Kafka, Hive, AWS EMR, Databricks, Airflow, and Docker.
HDFS & YARN for batch processing and durable, scalable storage across clusters.
Unified batch & streaming with Spark SQL, DataFrames, tuning, and checkpoints.
Durable pub/sub, Connect, and Schema Registry for real-time data pipelines.
Warehouse-style queries with metastore-driven schemas and partitions.
Elastic clusters, spot savings, autoscaling, and integrations with S3/Lake Formation.
Delta Lake, notebooks, jobs, Unity Catalog, and collaborative ML/ETL workflows.
Author DAGs, schedule & monitor pipelines with retries, SLAs, and alerts.
Portable runtime for services & jobs; build reproducible images for data apps.
Master Hadoop, Spark, Kafka, Hive, AWS EMR, Databricks, Airflow, and Docker to build scalable, fault-tolerant, real-time data engineering solutions.
Follow these four proven steps to progress from learner to job-ready Big Data Engineer with production-style projects recruiters trust.
Hadoop HDFS/Hive, Spark Core/SQL/Streaming, Kafka fundamentals, and cloud basics to build a strong engineering foundation.
Implement batch & real-time ETL with Spark + Kafka, orchestrate with Airflow, and publish documentation with diagrams & runbooks.
Ship to AWS/GCP/Azure (EMR/Dataproc/Databricks), optimize storage (Parquet/ORC), set up IAM, logging, lineage & monitoring.
Resume/LinkedIn revamp, whiteboard system design for data, SQL & Spark drills, scenario-based interviews. Target ₹12–20 LPA.
Learn from anywhere. Your journey to a Big Data career starts here.
Everything about our Big Data Engineering program—curriculum, tools, projects, timelines, and career support.
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Still have questions? Talk to an advisor for a personalized walkthrough of the curriculum and outcomes.
Enroll now for a project-first program with global certification, 95+ guided hours, and 100% job assistance—covering Kafka, Spark, Hadoop, Airflow, and cloud deployments.
Flexible schedules • Mentor support • Seats are limited—secure yours today.
