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Course syllabus

Azure Data Engineering

ADLS, Data Factory, Synapse, Databricks, and Fabric — a live Azure data platform path

Duration 4 months (16 weeks) Format 100% online · live · interactive · flexible Fee ₹19,999 (was ₹79,980 · 75% off)

Relaunch offer — first 2 batches only

16 weeks of 100% online live classes. Build the lake, pipelines, warehouse, and streaming work on Azure. Learn from home or anywhere, with recordings after every session.

Who it’s for: Analysts, backend engineers, and career switchers targeting Azure Data Engineer roles.

Pay & enroll · ₹19,999 Download brochure Enquire WhatsApp

Highlights

What this program covers

  • 4 months · live online
  • ADLS Gen2 · Data Factory · Synapse · Databricks · Fabric
  • Security, Purview, and pipeline cost control
  • Capstone lake-to-gold pipeline a hiring manager can inspect

Opening slide

AZURE DATA ENGINEERING

Azure Data Engineering map: Source, Ingest, Lake, Transform, Warehouse, Orchestrate, BI.
  1. SourceApps, APIs, files — the messy start.
  2. IngestBatch or CDC, without silent duplicates.
  3. LakeRaw land first. Schema later if you must.
  4. TransformSQL / Spark that is idempotent and tested.
  5. WarehouseStars, gold tables, something BI can trust.
  6. OrchestrateA DAG, retries, an SLA — not a cron on a laptop.
  7. BIIf the table is late, the dashboard is a lie.

Whiteboard

Diagrams we draw in week one

Every later module (Data Factory, Synapse, Databricks, quality) is a sharper version of these two pictures.

01 — pick the right pattern

ETL vs ELT vs Streaming

Three pipeline patterns: ETL transforms then loads; ELT loads then transforms in the warehouse; streaming never fully stops.
  1. ETLExtract → transform → load warehouse. Classic batch. Still useful.
  2. ELTExtract → load lake/warehouse → transform in SQL. Cloud warehouses love this.
  3. StreamingEvents → Kafka topic → consumer/sink. When batch is too late.

Most failed “real-time” projects were a batch job with a shorter cron. We start here so you do not overbuild.

02 — draw this in interviews

Production pipeline

Production pipeline from sources through ingest, lake, transform, warehouse, quality, and BI, wrapped by an orchestrator.

Quality is a gate, not a slide. If checks fail, the job fails — the dashboard does not quietly lie.

03 — what “good” looks like

Ops kit

Idempotent run it twice, same tables

Observable you can say which task died

Owned a name on the DAG, not “the pipeline”

Interview walk bronze → silver → gold out loud, without notes

  1. 01ADLS
  2. 02Data Factory
  3. 03Synapse
  4. 04Databricks
  5. 05Streaming
  6. 06Fabric
  7. 07Purview
  8. 08Capstone

After you pay

What enrollment unlocks

Payment is processed on Feednet Solutions. Approved students get the learning home, recordings, and assessments there.

  • Access to everyday class recording sessions
  • Daily assessments + module-wise and weekly assessments
  • Saturday assignments that raise your score
  • Monthly grand test / monthly offline or online interview
  • Learning-home (LMS) dashboard after enrollment
  • Resume, portfolio & mock-interview support on the paid track

Curriculum

Modules

  1. Module 1 — Azure data platform and identity
    • Subscriptions, resource groups, regions, and tags for a data estate
    • Microsoft Entra ID, RBAC, and service principals vs managed identities
    • When to pick Azure SQL, Synapse, Databricks, Fabric, or Cosmos DB
  2. Module 2 — Azure Data Lake Storage Gen2
    • Hierarchical namespace, containers, directories, and ACLs vs RBAC
    • Bronze / silver / gold zones on the lake, lifecycle, and immutability
    • Hands-on: land CSV/Parquet, list with az/storage SDK, set a retention policy
  3. Module 3 — Azure Data Factory pipelines
    • Linked services, datasets, copy activity, and Integration Runtimes
    • Mapping data flows vs Databricks notebooks — pick on purpose
    • Triggers, parameters, tumbling windows, and idempotent reruns
  4. Module 4 — Azure Synapse Analytics
    • Dedicated SQL pool vs serverless SQL on the lake
    • Synapse pipelines vs ADF — same IR family, different workspace
    • External tables, CETAS, and serving a star schema to Power BI
  5. Module 5 — Azure Databricks and Delta Lake
    • Workspaces, clusters, jobs, and Unity Catalog vs hive metastore
    • Delta Lake: MERGE, time travel, OPTIMIZE, Z-ORDER
    • Notebooks as ADF activities; secrets via Key Vault-backed scopes
  6. Module 6 — Streaming on Azure
    • Event Hubs: partitions, consumer groups, capture to ADLS
    • Stream Analytics windows, late data, and output to Synapse/Power BI
    • Databricks Structured Streaming vs ASA — latency vs ops cost
  7. Module 7 — Azure SQL, Cosmos DB, and serving layers
    • Azure SQL as source/sink: CDC, elastic pools, Hyperscale
    • Cosmos DB partition keys, change feed, and analytical store
    • When the serving layer is Power BI DirectQuery vs Import vs gold tables
  8. Module 8 — Microsoft Fabric and OneLake
    • OneLake shortcuts, lakehouse vs warehouse items
    • Data Factory / Dataflow Gen2 vs classic ADF
    • When Fabric replaces Synapse for a team — and when it does not
  9. Module 9 — Security, networking, and Key Vault
    • Managed identities on ADF, Synapse, Databricks, and Function Apps
    • Private endpoints, firewalls, and VNet injection for data services
    • Key Vault, customer-managed keys, and column/row-level security
  10. Module 10 — Purview, quality, and cost
    • Microsoft Purview: scan, classify, lineage from ADF/Synapse/Databricks
    • Data quality rules, Great Expectations-style checks in notebooks
    • Cost: storage vs compute, reserved capacity, and shutting idle Spark
  11. Module 11 — CI/CD, monitoring, interviews, and capstone
    • ARM/Bicep or Terraform for data resources; ADF Git integration
    • Log Analytics, pipeline alerts, and RCA for a failed tumbling window
    • Capstone: ADLS + ADF + Databricks/Synapse gold tables a hiring manager can inspect

On completion

Certificate of completion

After you finish Azure Data Engineering, Feednet Teach issues a certificate like this in your name. The learner name is hidden on this sample.

Sample
Feednet Solutions Pvt Ltd Hyderabad, Telangana, India CIN: U72900TG2019PTC134896

Certificate of Completion

Azure Data Engineering

This certificate is proudly presented to

for successfully completing the Azure Data Engineering training program, demonstrating dedication and proficiency in Azure data lakes, pipelines, warehousing, Databricks, and Microsoft Fabric.

Duration: 4 Months

Program Director PROGRAM DIRECTOR
Authorized Signatory AUTHORIZED SIGNATORY

Certificate ID: FSPL-ADE-SAMPLE · Issued on completion

Sample only. Your name, dates, and certificate ID appear after you complete Azure Data Engineering.
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