Azure Data Engineer Training - Course In Ameerpet
                     AZURE DATA ENGINEERING • TECHNICAL PRESENTATION
How to Build AI-Ready
Data Platforms on 
A pzracuticarl eblu?eprint for trusted data, scalable 
pipelines, and AI workloads.
01 Connect data from business systems
02 Build a governed lakehouse foundation
03 Prepare reliable data for AI
04 Serve models, agents, and analytics securely
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1. What Makes a Data Platform AI-Ready?
Connected Reusable data 
Trusted data
sources products
Consistent, Bring together Publish curated 
accurate, well- databases, files, tables, APIs, and 
documented data SaaS applications, business definitions 
gives AI systems a events, and IoT teams can safely 
dependable data. reuse.
foundation.
AI-friendly 
Governed access delivery
Apply identity, Make data available 
permissions, for analytics, 
lineage, and privacy machine learning, 
controls from retrieval, and AI 
ingestion to use. applications.
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2. Reference Architecture on Azure
Connect the layers into one managed flow; apply security and governance throughout.
DATA SOURCES ERP • CRM • SQL • APIs • Files • IoT
INGESTION Azure Data Factory • Event Hubs • APIs
STORAGE Azure Data Lake Storage Gen2 • OneLake
PROCESS & Azure Databricks • Spark • Delta Lake • Fabric
CURATE
SERVE FOR AI & Azure AI Search • Azure Machine Learning • Power 
ANALYTICS BI
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3. Ingest Data Reliably at the Right Speed
Batch ingestion
Use Azure Data Factory to copy data from databases, files, and SaaS sources on a 
schedule.
Streaming ingestion
Use Azure Event Hubs for high-volume events such as app activity, devices, and 
transactions.
Incremental loads
Capture only new or changed records with watermarks, timestamps, or change data 
capture.
Resilient pipelines
Add retries, timeouts, checkpoints, and failure notifications to handle interruptions.
Standardized landing
Store raw data with source name, load time, and batch ID for traceability.
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4. Create a Scalable Lakehouse Foundation
Use open storage and transform raw data into trusted business datasets.
BRONZE • RAW SILVER • CLEAN GOLD • CURATED
Keep source-aligned Validate schemas, Create business-ready 
data with minimal remove duplicates, datasets, aggregates, 
changes. Preserve standardize formats, and feature tables for 
history so records can and apply business reporting and AI.
be replayed. rules.
Choose the right compute Optimize for scale
Use Databricks or Fabric Spark for Partition carefully, compact small 
distributed processing; use SQL files, use columnar formats such as 
engines when relational workloads Delta or Parquet, and tune 
fit. workloads.
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5. Prepare Data for Machine Learning and 
GenAI
Define meaning Improve data quality
Add business descriptions, owners, Check completeness, validity, 
data types, and clear names to uniqueness, freshness, and referential 
important datasets. integrity.
Create AI features Prepare retrieval content
Build consistent feature tables and Clean documents, split text into useful 
reusable transformations for model chunks, retain metadata, and create 
training and scoring. embeddings when needed.
Make data discoverable
Use catalogs, lineage, and searchable 
metadata so teams can find approved 
data.
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6. Govern, Secure, and Serve Data to AI
Identity and secrets
Use Microsoft Entra ID, managed identities, and Key Vault; avoid credentials in notebooks or code.
Fine-grained permissions
Restrict access by role, workspace, table, row, or column where the selected service supports it.
Privacy and governance
Classify sensitive data, apply retention rules, and track lineage with Purview and platform 
catalogs.
AI retrieval and model access
Expose approved data through curated tables, APIs, Azure AI Search indexes, or governed feature 
pipelines.
Monitor continuously
Track pipeline runs, data freshness, quality failures, model inputs, cost, and access activity.
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7. A Practical Roadmap: From Pilot to 
Production
1. Start with one use case
Choose a clear business outcome, data owner, users, and measurable success criteria.
2. Build a thin data path
Connect one or two sources, land raw data, and create a tested transformation 
pipeline.
3. Add quality and controls
Define validation rules, access policies, lineage, secrets management, and monitoring.
4. Enable an AI workload
Serve curated data to a model, search experience, or AI application; evaluate output 
quality.
5. Operate and improve
Track freshness, reliability, usage, cost, and feedback; scale after the pilot proves 
value.
KEY TAKEAWAY: AI quality depends on trusted, secure, well-described data—not just the model.
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For More Information About 
Azure Data Engineer
Address:- Flat no: 205, 2nd Floor,
   Nilagiri Block, Aditya Enclave,  Ameerpet, 
Hyderabad-16
Ph. No: +91-7032290546
www.visualpath.in
 [email protected]
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Thank you from Visualpath
Scan Me
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