Course (Part 375)

• Understand core Azure building blocks: Virtual Machines (IaaS), Azure Functions (serverless), and Azure Storage (Blob, File, Table, Queue).
• Recognize Databricks, DBFS, Delta Lake and their roles in large‑scale data engineering.
• Explain Delta Table features: ACID, time travel, schema enforcement/evolution, and optimization techniques.
• Describe Unity Catalog governance, Autoloader ingestion, Delta Live Tables (DLT) pipelines and basic DLT use cases.
• Design a simple Bronze → Silver → Gold layered data architecture and plan CI/CD for data workloads.

Azure Virtual Machines (VMs) are full OS instances you configure and manage; use them when you need control over OS, drivers, or custom runtimes. Azure Functions are managed, event‑driven compute that scale automatically and reduce operational overhead. Azure Storage provides tiers and services for unstructured and structured data; choose Blob for objects, Files for SMB shares, Table for simple NoSQL patterns and Queue for messaging. Databricks is a managed Spark platform that integrates with Azure storage and Databricks File System (DBFS). Delta Lake is a transactional storage layer over Parquet files that adds ACID guarantees, schema enforcement/evolution, time travel, and performance optimizations (compaction, Z‑ordering, data skipping). Autoloader automates file discovery and ingestion with schema evolution support; Delta Live Tables (DLT) give a declarative way to build production data pipelines with built‑in monitoring and reliability. Unity Catalog centralizes metadata and access control across Databricks assets. Architecturally, decouple ingestion (event capture or file landing) from processing, persist raw data in Bronze, clean/enrich in Silver, and produce curated, analytics‑ready Gold datasets. Apply CI/CD, testing (batch/stream integration tests), and monitoring (Azure Monitor, Application Insights) to productionize data systems.

EN: Azure Virtual Machines provide customizable compute; Azure Functions run event‑driven code; Azure Blob Storage stores large unstructured data. Databricks and Delta Lake enable robust analytics and reliable data pipelines.

বাংলা: Azure ভার্চুয়াল মেশিন (VM) কাস্টমাইজযোগ্য কম্পিউট সরবরাহ করে; Azure Functions ঘটনা‑চালিত কোড চালায়; Azure Blob Storage বড় আনস্ট্রাকচার্ড ডেটা সংরক্ষণ করে। Databricks এবং Delta Lake শক্তিশালী বিশ্লেষণ এবং নির্ভরযোগ্য ডেটা পাইপলাইন সম্ভব করে।

• Choose storage formats (Parquet + Delta) for analytics workloads for performance and transactionality.
• Use managed identities and Azure AD for secure service-to-service authentication; avoid hardcoding secrets.
• Enable encryption at rest & in transit, and enforce network controls (NSG, private endpoints) for production systems.
• Design for schema evolution: prefer Delta merge/mergeSchema patterns or explicit evolution strategies.
• Monitor costs: use appropriate VM sizes, function plans, and storage tiers (Hot/Cool/Archive) according to access patterns.

1. Azure Functions require you to manage the underlying OS. — False
2. Delta Lake provides ACID transactions on top of Parquet files. — True
3. DBFS is a replacement for Azure Blob Storage. — False (DBFS is a layer that uses cloud storage underneath)
4. Autoloader can support automatic schema evolution. — True

1. Azure ________ (VMs) provide Infrastructure as a Service. — Virtual Machines
2. Delta Lake supports ________ (ACID) transactions. — ACID
3. Databricks file system abbreviation is ________. — DBFS
4. For streaming ingestion with auto schema changes use ________. — Autoloader

1. Which Azure service is best for running short event‑triggered code without managing servers?
   • A. Azure Virtual Machines
   • B. Azure Functions — Correct
   • C. Azure SQL Database
   • D. Azure Blob Storage
2. Time travel in Delta Lake enables:
   • A. Faster writes
   • B. Querying previous versions of data — Correct
   • C. Automatic scaling of clusters
   • D. Removing old files permanently
3. Unity Catalog primarily addresses:
   • A. Compute provisioning
   • B. Data governance and access control — Correct
   • C. Network security
   • D. Cost optimization

Match item on left with correct item on right:

Question: Describe a simple Bronze→Silver→Gold pipeline for web analytics logs.

Model Answer: Ingest raw web logs into Azure Blob Storage (Bronze) using Event Hubs or direct upload. Use Autoloader in Databricks to incrementally load new files into Delta Bronze tables. In Databricks, run transformation jobs (or DLT) to parse fields, filter noise, and enrich with reference data, storing results in Silver Delta tables. Aggregate sessions, compute KPIs, and produce denormalized analytics datasets into Gold Delta tables or Synapse for BI dashboards. Automate via jobs and CI/CD, secure with Unity Catalog, and monitor with Azure Monitor/Application Insights.

Task: Propose a mini capstone: design a serverless ETL that ingests IoT sensor CSVs, processes anomalies, and stores results for analytics.

Suggested Solution: Use Azure Blob Storage for file landing; when a new CSV arrives, an Event Grid trigger invokes an Azure Function to validate schema and push metadata to a queue. Databricks Autoloader / DLT picks up new files for transformation; apply anomaly detection model in Databricks and write cleaned events to Gold Delta tables. Use Power BI connected to Synapse or Databricks SQL endpoint for dashboards. Manage access with Unity Catalog and automate deployment with Azure DevOps pipelines.

1. Lab: Create a free Azure account and deploy a small Linux VM; SSH into it and install Python.
2. Lab: Create an Azure Function (HTTP trigger) that writes a message to Blob Storage.
3. Lab: Provision a Databricks workspace, create a cluster, and run a notebook that reads/writes a small Delta table.

1. Read: Delta Lake documentation section on time travel and schema evolution; summarize in one page.
2. Practice: Convert a small CSV to Delta using a Databricks notebook and query a previous version using versionAsOf.
3. Design: Create a one‑page architecture diagram for a Bronze→Silver→Gold pipeline for e‑commerce clickstream data, listing Azure services used.

Implement a minimal pipeline:

1. Upload a sample CSV to Azure Blob Storage.
2. Use Databricks Autoloader or spark.read to ingest into a Delta Bronze table.
3. Run a transformation notebook to create a Silver table (clean and type-cast fields).
4. Query the Silver table and visualize one metric (e.g., counts by category) in Databricks notebook chart.

Submit screenshots and a short log of commands run.