Video summary

Azure Data Engineer Full Course For Beginners (2026 Step-By-Step Guide)

Main summary

Key takeaways

Educational

Course Roadmap: Building a Beginner-to-Job-Ready Azure Data Engineer Skillset

The video presents an “Azure Data Engineer Full Course for Beginners (2026)” roadmap, estimated at around 25 hours, aimed at taking learners from fundamentals to a final end-to-end project. It outlines an Azure-first learning order:

  • Start with Azure Fundamentals (core services and data concepts like Data Links, Azure databases, Cosmos DB)
  • Move to Azure Data Factory (ADF) as the backbone for orchestration
  • Use Azure Synapse Analytics for data warehousing and building a dimensional/star schema model
  • Continue with Azure DataBricks for hands-on work

The plan includes:

  • Interview preparation with industry-style questions
  • A final combined project spanning these technologies

The course navigation is chapter-based with timestamps and supports skipping topics depending on prior knowledge, making it suitable for both complete beginners and learners who already understand Azure basics.

Throughout the course, the instructor repeatedly emphasizes community engagement—encouraging viewers to subscribe, leave commitment comments, and provide feedback—while keeping the focus on the technical journey.

Azure Setup and Data Storage Foundation

The video begins by explaining how to get access to Azure via a free Azure account:

  • Use “Try Azure for free”
  • Sign up with a Microsoft account
  • Note that card details are used for verification while not charging immediately
  • Mentioned benefit: $200 credit for 30 days
  • Access the Azure portal at portal.azure.com

Practical hierarchy for Azure data engineering

  1. Create a Resource Group
  2. Create a Storage Account
    • Select LRS redundancy (cheapest)
    • Enable Hierarchical namespace to turn blob storage into a data-lake-like structure
  3. Create containers representing common lake layers:
    • Bronze
    • Silver
    • Gold
  4. Organize containers/folders based on data type:
    • Structured sources (e.g., SQL database concepts)
    • Semi-structured sources (e.g., Cosmos DB / NoSQL)
    • Unstructured / semi-structured storage (blob/data lake concepts)

Storage Account capabilities (high-level mapping)

  • Containers
  • Blob/file storage behavior
  • File shares
  • Queues (for ingestion/streaming concepts)
  • Tables (NoSQL key/value style)

Working with Structured, Semi-Structured, and Unstructured Data

Structured data with Azure SQL Database

The course demonstrates how to set up Azure SQL Database:

  • Create an Azure SQL server
  • Configure connectivity
  • Use an online Query Editor (or mention Azure Data Studio)
  • Create a table using CREATE TABLE
  • Insert rows using INSERT INTO ... VALUES ...
  • Query using SELECT ... FROM ...
  • Delete resources afterward to avoid ongoing costs

Semi-structured data with Cosmos DB

Cosmos DB is introduced as a fit for semi-structured / NoSQL data, including discussion of Cosmos DB APIs:

  • NoSQL
  • MongoDB API
  • Table
  • Gremlin
  • Cassandra
  • PostgreSQL

A common data engineering pattern is highlighted: treat Cosmos DB often as a source, then load/process later into a warehouse/lakehouse.

Unstructured / semi-structured data with Azure Data Lake Storage (Gen2 behavior)

With hierarchical namespace enabled, the video shows:

  • Creating containers and directories
  • Uploading files such as CSV
  • Accessing/processing those files through a pipeline-based workflow

Azure Data Factory (ADF): Orchestration, Ingestion Patterns, and Robust Pipelines

The course centers on Azure Data Factory as a cloud ETL/ELT orchestration tool.

ADF building blocks

  • Linked services (connections)
  • Datasets (references to data locations)
  • Pipelines (orchestration containers)
  • Activities, including:
    • Copy Activity (move data)
    • Data Flows (transformations; Spark-backed)
    • Get Metadata, If Condition, For Each, Execute Pipeline
    • Set Variable, Validation
    • Triggers (schedule/tumbling windows/storage events)

Copying data between lake layers (Bronze → Silver example)

A practical example covers:

  • Copying from one container/folder (e.g., Bronze) to another (e.g., Silver)
  • Using linked services, source/destination datasets
  • Running the pipeline with Debug
  • Verifying results in the destination

Parameterized ingestion with loops (monthly files pattern)

To avoid duplicating pipelines, the course teaches a design pattern:

  1. A parameterized dataset (e.g., month)
  2. A For Each activity iterating months (e.g., 1..12)
  3. Dynamic path substitution using the loop variable
  4. Handling leading-zero formatting using If Condition:
    • Months 1–9: 01..09
    • Months 10–12: 10..12

This is implemented using two copy activities with different path rules.

Complex file filtering via metadata + conditional copy

Another pipeline pattern filters only files that match naming rules:

  • Get Metadata Activity returns child items (files)
  • For Each iterates through those files
  • If Condition checks whether the filename starts with a prefix (e.g., fact)
  • If true, the pipeline copies using parameterized datasets into a reporting folder/container

Transformations with ADF Data Flows (Spark-based)

ADF Data Flows are demonstrated using transformations such as:

  • Select (projection/removing columns)
  • Filter
  • Conditional Split
  • Derived Column (null handling and computed fields)
  • Aggregate / Group By, with mention of window-function-like usage for gold-level analytics
  • Alter Row and Sync for insert/upsert-style logic

Data Flows are triggered using a Data Flow activity inside a pipeline.

Triggering and orchestration strategies

The course covers:

  • Schedule triggers (start time, recurrence, end time)
  • Storage event triggers (start pipelines when blobs/files appear)

A key practical step is included:

  • Delete the uploaded file after processing to prevent re-triggering

It also mentions troubleshooting for triggers not firing, such as registering event grid/resource provider.

For multi-step orchestration:

  • Use a parent pipeline with multiple Execute Pipeline activities
  • Trigger once (e.g., by a storage event)
  • Best practice: pass parameters from parent to child pipelines

Security and secrets with Azure Key Vault

ADF security is covered via Azure Key Vault:

  • Create a Key Vault
  • Grant ADF access via its managed identity (Key Vault permissions to read secrets)
  • Use a pipeline Web Activity to fetch secrets from Key Vault using managed identity
  • Emphasize using the correct API version (incorrect versions can fail)
  • Enable secure output to avoid secrets leaking in run logs (framed as an “interview trap”)

PySpark / Spark Processing: From Bronze to Silver to Gold

After ingestion/orchestration, the video transitions to Spark-based processing and lakehouse-style transformations, emphasizing safe reading and correct schema handling.

Discovering files and reading safely

  • Enable recursive file lookup so Spark can find nested files
  • Emphasize that schema handling matters before running load jobs

Explicit schema definition (avoid inferSchema)

Two schema approaches are taught:

  • Struct Type schema using PySpark types and StructField definitions
  • DDL (SQL-style) schema as a multi-line schema string

Learners pass the schema into readers via .schema(customSchema) to avoid inferSchema pitfalls and overhead.

Transformations while writing into Silver (Parquet)

In Bronze → Silver:

  • Column renaming via withColumnRenamed
  • Write modes such as append, overwrite, error, and ignore
  • Silver is written in Parquet

The video notes that Parquet output creates multiple files and includes files starting with _, which are ignored on subsequent reads—so filename management is unnecessary.

Feature engineering examples

The course demonstrates practical transformations, including:

  • Splitting multi-valued fields like zone1/zone2 into separate columns:
    • zone1 from index [0]
    • zone2 from index [1]
    • If only one zone exists, the second column becomes null
  • Date/time engineering for trip data:
    • Create trip_date using to_date(timestamp)
    • Extract trip_year via year()
    • Extract trip_month via month()
  • Projection using .select(...) to keep only needed columns

Layer progression: Bronze → Silver → Gold

  • Bronze → Silver: transformations saved as Parquet
  • Silver → Gold: further refinement saved as Delta tables

Delta Lake Fundamentals and Advanced Table Operations

The instructor explains Delta Lake concepts, focusing on how Delta tables manage changing data over time.

External vs managed tables (DROP behavior)

A key lesson:

  • Dropping an external/unmanaged table removes metadata, but underlying data remains
  • Dropping a managed table deletes metadata and underlying stored data

Creating Gold Delta tables and querying

The flow for producing Gold tables:

  1. Read Silver Parquet inputs
  2. Write to a Gold container in Delta format
  3. Create a Delta table on top of that location

It demonstrates:

  • write.format("delta")...saveAsTable(...)
  • Spark SQL queries like SELECT ... FROM gold.<table>

Delta transaction log, CRUD, and versioning

Delta mechanics explained:

  • Delta = Parquet + delta log (_delta_log)
  • The log records updates/inserts/deletes and commit history
  • Queries reconstruct latest state using log versions

DML operations shown:

  • UPDATE
  • DELETE
  • Viewing history with DESCRIBE HISTORY

Time travel is introduced by restoring a table to an earlier version and re-querying to confirm rollback.

Using Gold tables with Power BI

To close the pipeline-to-analytics loop:

  • In Databricks, use partner connect to Microsoft Power BI and download the connection file
  • In Power BI Desktop, open the connection file and authenticate using a Databricks access token
  • Generate token in Databricks (Settings → Developer → Access tokens → Generate new → copy)
  • Load Gold tables and build dashboards/reports

Overall Takeaways

By the end, the video frames Azure data engineering as a combination of:

  • Orchestration (ADF and related data services)
  • Storage and lakehouse structure (Bronze/Silver/Gold, file formats like Parquet and Delta)
  • Governance and security (Key Vault, managed identity, and Unity Catalog concepts referenced at a high level)
  • Robustness and maintainability (triggers, event-driven pipelines, parameterization, retry-safe patterns)
  • Interview-ready pattern design, including scenario-based pipeline strategies and Delta behaviors

The instruction is led by the course host/presenter Anlamba, with Databricks (Delta Lake and Power BI integration) and Microsoft Power BI repeatedly appearing as key tools for end-to-end analytics.

Original video