Getting Started with Azure Data Workloads

Published 2025-01-01

Types of Data

Structured data
- Students and Grades tables, i.e. CRM, ERP or admin systems
- Tabular in nature
- Table holds one type of data
- Each table has a primary key (field or set of fields to identify a record)
- Foreign keys are reference keys to other tables
Unstructured data
- Videos, images, audio files
- Harder to interpret using a computer system
- Analysis of images - gives structured or semi-structured data
Semi-structured data
- Has some observable structure
- Log files (follow some kind of format)
- XML data - it can be interpreted using computerized systems
- Not tabular in nature

Store data in tables
Interacting with data using SQL
All have schema that describe all tables, fields, field types and relationships
Schema is enforced on write
Examples
- Microsoft SQL Server - high performance, AD integration
- MySQL - free to use, open-source SQL
- PostgreSQL - free but more complex
Non-relational databases
- No tables used
- Collections or containers used
- Don't follow predefined schema
- Types
  - Document databases (XML, JSON)
  - Wide-column store
  - Key-value store
  - Graph databases
- Examples
  - Redis (key-value, fast)
  - Cassandra (free, open source, wide-column)
  - Azure CosmosDB (key-value store), distributed around the world

ACID properties
Committing a transaction means it is final
Two transactions mutating the same record
ACID
- Atomicity: All operations in a transaction succeed or all fail
- Consistency: The database remains in a consistent state before and after the transaction
- Isolation: Transactions are executed independently
- Durability: Once committed, changes are permanent
- Atomicity and Isolation are the most challenging to implement
Analytical workloads
- High volume of reads
- Large volumes of data
- Warehousing is also called OLAP (Online Analytical Processing)

Batch data
- Often easier to implement
- Executed on schedule
- All data is stored for analytical querying
- You query the data after loading
- Easy joining of datasets
- Efficiency opportunities
- Aligns with existing skills (with more traditional database schemas)
Streaming data
- Near real-time answers
- Only results are stored, no other data
- Queries are predefined
- Combining datasets is more difficult
Globoticket website
- Site
- Azure Data Factory
- Azure SQL DB or Azure Synapse
- PowerBI
Courses on Reporting/PowerBI
- Building Your First Power BI Report
- Building your First Data Pipeline in Azure Data Factory
- Understanding Azure Stream Analytics
Sales reports using streaming data
- New update whenever an order is placed
- Azure Event Hub
  - Can send messages to more than 1 consumer
    - Azure SQL DB
    - Stream Analytics
    - Understanding Azure Stream Analytics

Last updated on 2026-04-05