Describe common data workloads

Question 1

Transactional Workloads

Answer 1

A transactional system records transactions that encapsulate specific events that the organization wants to track. Think of a transaction as a small, discrete, unit of work.

Transactional systems are often high-volume, sometimes handling many millions of transactions in a single day. The data being processed has to be accessible very quickly. The work performed by transactional systems is often referred to as Online Transactional Processing (OLTP). Examples include e-commerce transactions and banking operations.

Question 2

OLTP (Online Transaction Processing)

Answer 2

OLTP solutions rely on databases optimized for efficient read and write operations to support transactional workloads involving Create, Read, Update, and Delete (CRUD) operations. These operations adhere to ACID semantics:

-Atomicity: Transactions are treated as a single unit, either succeeding completely or failing entirely.

-Consistency: Transactions transition the database from one valid state to another. Using the fund transfer example, the completed transaction must accurately reflect the transfer of funds between accounts.

-Isolation: Concurrent transactions don’t interfere with each other, ensuring a consistent database state. For example, while one transaction transfers funds, another checking account balances must provide consistent results, avoiding values from different stages of the transfer.

-Durability: Once committed, a transaction remains committed. After completing a fund transfer, the updated account balances persist even if the database is switched off.

-Single data source
-Many/Short transactions
-Latency sensitive
-Small payloads

Question 3

Analytical Workloads

Answer 3

Analytical workloads involve complex queries and aggregations performed on large datasets. The goal is to gain insights and perform data analysis.

Use Cases: Online Analytical Processing (OLAP) systems, data warehousing, and business intelligence applications. Examples include data mining, reporting, and trend analysis.

Question 4

OLAP (Online Analytical Processing)

Answer 4

An OLAP model is an aggregated type of data storage that is optimized for analytical workloads. Data aggregations are across dimensions at different levels, enabling you to drill up/down to view aggregations at multiple hierarchical levels; for example to find total sales by region, by city, or for an individual address.

-Because OLAP data is pre-aggregated, queries to return the summaries it contains can be run quickly.

Different types of user might perform data analytical work at different stages of the overall architecture. For example:

-Data scientists might work directly with data files in a data lake to explore and model data.
-Data Analysts might query tables directly in the data warehouse to produce complex reports and visualizations.
-Business users might consume pre-aggregated data in an analytical model in the form of reports or dashboards.

-Multiple data sources
-Long/Few transactions
-Throughput sensitive
-Large payloads