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Microsoft DP-900: Azure Data Fundamentals Study Notes

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Data Types & Formats

Structured, semi-structured, and unstructured data

Domain Weight

Core Data Concepts accounts for 25% of the DP-900 exam.

Data Classification

Type	Description	Format Examples	Azure Store Examples
Structured	Data with a defined schema and fixed format	Relational tables, CSV	Azure SQL Database, Azure Synapse
Semi-structured	Data with flexible schema — some structure but variable fields	JSON, XML, YAML	Azure Cosmos DB, Blob Storage
Unstructured	Data with no predefined schema	Images, video, audio, documents	Azure Blob Storage, Azure Data Lake

File Formats

CSV

Comma-separated values. Simple, human-readable. Good for tabular data exchange.

JSON

JavaScript Object Notation. Flexible, hierarchical. Common for APIs and NoSQL stores.

Parquet

Columnar format. Highly compressed, efficient for analytics. Common in big data.

Avro

Row-based format with schema embedded. Good for data streaming and Kafka.

ORC

Optimized for Hive. Columnar with advanced compression. Less common than Parquet.

Delta

Parquet + transaction log. Used in Delta Lake for ACID transactions on big data.

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Batch vs Streaming Workloads

Transactional (OLTP) vs Analytical (OLAP) processing

Processing Models

Aspect	Batch Processing	Stream Processing
When data is processed	Collected over time, processed as a group	Processed continuously as it arrives
Latency	High (minutes to hours)	Low (milliseconds to seconds)
Azure services	Azure Data Factory, Synapse Pipelines	Azure Stream Analytics, Event Hubs
Use cases	Nightly reports, ETL loads, billing runs	Fraud detection, IoT alerts, real-time dashboards

OLTP vs OLAP

Aspect	OLTP (Transactional)	OLAP (Analytical)
Purpose	Record day-to-day business transactions	Analyze large volumes of historical data
Operations	INSERT, UPDATE, DELETE individual rows	Aggregations, GROUP BY, complex queries
Schema	Normalized (3NF) — reduces redundancy	Denormalized (Star/Snowflake) — optimized for reads
Azure service	Azure SQL Database, Azure SQL MI	Azure Synapse Analytics (Dedicated SQL Pool)
Row count per query	Small — few rows	Large — millions/billions of rows

Exam Tip

Know the difference between OLTP (transactional, Azure SQL) and OLAP (analytical, Synapse) workloads. A common exam scenario asks which service to use for a given workload type.

Data Roles & Responsibilities

Database Administrator, Data Engineer, Data Analyst

Key Data Roles

Database Administrator (DBA)

Manages database infrastructure. Responsible for backup/restore, security, performance tuning, and availability. Uses Azure SQL, SQL MI.

Data Engineer

Builds and maintains data pipelines and infrastructure. Works with ETL/ELT, Data Factory, Synapse, Databricks. Ensures data is available for analysis.

Data Analyst

Explores data, creates reports and dashboards, derives insights. Uses Power BI, SQL, Excel. Consumes data prepared by data engineers.

Also Know

Data Scientists build ML models. Application Developers use data services via APIs. These roles may overlap — the exam tests your ability to match a task to the appropriate role.

Type

Description

Format Examples

Azure Store Examples

Structured

Data with a defined schema and fixed format

Relational tables, CSV

Azure SQL Database, Azure Synapse

Semi-structured

Data with flexible schema — some structure but variable fields

JSON, XML, YAML

Azure Cosmos DB, Blob Storage

Unstructured

Data with no predefined schema

Images, video, audio, documents

Azure Blob Storage, Azure Data Lake

Aspect

Batch Processing

Stream Processing

When data is processed

Collected over time, processed as a group

Processed continuously as it arrives

Latency

High (minutes to hours)

Low (milliseconds to seconds)

Azure services

Azure Data Factory, Synapse Pipelines

Azure Stream Analytics, Event Hubs

Use cases

Nightly reports, ETL loads, billing runs

Fraud detection, IoT alerts, real-time dashboards

Aspect

OLTP (Transactional)

OLAP (Analytical)

Purpose

Record day-to-day business transactions

Analyze large volumes of historical data

Operations

INSERT, UPDATE, DELETE individual rows

Aggregations, GROUP BY, complex queries

Schema

Normalized (3NF) — reduces redundancy

Denormalized (Star/Snowflake) — optimized for reads

Azure service

Azure SQL Database, Azure SQL MI

Azure Synapse Analytics (Dedicated SQL Pool)

Row count per query

Small — few rows

Large — millions/billions of rows