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Build the Data Systems That Power Tomorrow's AI Applications
Every AI model is only as powerful as the data behind it. Become the engineer who transforms raw data into intelligent, production-ready systems.
With Edureka's Advanced Certification in Data Engineering with GenAI, you'll:
✅ Master the complete modern data engineering stack.
✅ Build production-scale ETL, streaming, lakehouse, and analytics solutions.
✅ Prepare for the Databricks Certified Data Engineer Associate certification.
| Aug 29 th |
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Many traditional data engineering courses focus only on pipelines, storage, and processing. This program also covers the AI-ready data layer that modern enterprises require, including vector search, RAG pipelines, semantic layers, context engineering, and data governance.
Learners gain practical experience with Apache Spark, Delta Lake, Databricks Lakeflow, Unity Catalog, Azure Databricks, CI/CD, MCP, and agentic data workflows. With 25+ hands-on activities and dedicated Snowflake and dbt electives, the program helps professionals build secure, governed data platforms for analytics and Generative AI applications.
It also develops relevant technical skills that can support preparation for Databricks Data Engineer certification exams and the Microsoft DP-750 exam.
Note that, RAG quality begins with data quality, governance, and reliable retrieval—not only the language model.
Edureka’s Advanced Certification in Data Engineering with GenAI is a live, instructor-led program covering modern data engineering, lakehouse architecture, cloud platforms, data governance, and Generative AI.
The curriculum includes Python, SQL, Apache Spark, PySpark, Delta Lake, Databricks Lakeflow, Unity Catalog, Azure Databricks, CI/CD, MLflow, vector search, RAG, MCP, and agentic data engineering. Self-paced electives also introduce Snowflake, Snowpark, dbt, Databricks SQL, Genie, and data products.
Learners should have a working knowledge of Python and SQL. Prior experience with Apache Spark, Databricks, Microsoft Azure, Snowflake, dbt, or Generative AI is not required.
The foundational modules introduce the core data engineering and Databricks concepts required for the advanced topics.
The program is suitable for:
The program includes 66 hours of live, instructor-led training across 22 modules and five courses. It also provides six self-paced elective modules covering Databricks analytics, data products, Snowflake, and dbt.
Learners will be able to:
The program covers:
The program first builds skills in data ingestion, transformation, distributed processing, orchestration, governance, cloud integration, and deployment.
It then applies these skills to GenAI use cases involving unstructured data, embeddings, vector search, semantic layers, RAG, MCP, and AI agents. This approach helps learners build governed data platforms for analytics and AI applications.
Unity Catalog is Databricks’ centralized governance solution for data and AI assets.
Learners use Unity Catalog to manage permissions, row-level security, column masking, PII classification, lineage, audit logs, metric views, and secure data sharing.
The program covers Lakeflow Connect, Lakeflow Declarative Pipelines, and Lakeflow Jobs.
Learners build production workflows with data-quality rules, task dependencies, scheduling, retries, alerts, and pipeline monitoring.
Yes. The program is hands-on driven and enables learners towork on REST API ingestion, Spark optimization, Delta Lake, streaming pipelines, Lakeflow orchestration, Unity Catalog, Azure Databricks, CI/CD, RAG, MCP, and AI agents.
Learners need a Windows, macOS, or Linux computer with at least 8 GB RAM, although 16 GB is recommended.
The system should also have 50 GB of free storage and a stable internet connection of at least 5 Mbps. Cloud-based Databricks lab environments are provided.
Yes. GenAI can automate repetitive coding and documentation tasks, but organizations still require skilled Data Engineers to build, govern, secure, monitor, and optimize enterprise data systems.
Demand is increasingly shifting toward professionals with expertise in data governance, lakehouse architecture, cloud data platforms, real-time pipelines, and AI-ready data engineering.
The 22 live modules primarily focus on Databricks and Azure Databricks to provide structured, certification-aligned learning.
The program also includes self-paced electives covering Snowflake, Snowpark, dbt Core, dbt Semantic Layer, analytics engineering, and lakehouse interoperability.
The Model Context Protocol, or MCP, is an open standard that connects AI applications with enterprise tools, data sources, and services.
It enables controlled access to approved schemas, tables, metrics, APIs, and business systems without exposing unrestricted backend access.
MCP enables Data Engineers to securely connect governed enterprise data with GenAI applications and AI agents.
In this program, learners build an MCP server over Unity Catalog and apply runtime controls through Unity AI Gateway.
Yes. Learners build and evaluate RAG pipelines using governed enterprise data, vector search indexes, embeddings, chunking strategies, and foundation model endpoints.
The curriculum also explains when RAG is appropriate and when semantic layers or long-context models may provide a better solution.
Yes. RAG remains valuable when AI systems require current, traceable, secure, or domain-specific information.
Long-context models and RAG serve different use cases. The program teaches learners to evaluate retrieval quality, context relevance, latency, cost, and governance before selecting an architecture.
The live curriculum remains focused on Databricks and Azure Databricks to provide deeper platform and certification coverage.
Snowflake and dbt are included as self-paced electives because they are widely used alongside Databricks in modern enterprise data stacks.
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