What are some common data warehouse design patterns and when to use them?

Common data warehouse design patterns include star schema, snowflake schema, data vault, and data lake. Star schema simplifies querying with a centralized fact table surrounded by dimension tables, ideal for BI. Snowflake schema normalizes dimension tables, reduces data redundancy, and supports complex data relationships. Data vault helps historical data tracking and flexibility through hub, link, and satellite tables, suitable for compliance-driven environments. Data lakes store raw, unstructured data, enabling flexible schema-on-read querying for exploratory analytics. Use star schema for retail sales analysis, snowflake schema for ERP systems, data vault for healthcare data tracking, and data lake for IoT sensor data aggregation.

- The most revealing thing about data warehouses tables is that they are not the same as transactional tables. This is fundamental in proper design of data warehouse tables. - Acknowledging this reality from the onset will help even seasoned database experts to approach each the right way and lead to achieving the objectives that each intends.

This schema is best utilized when dealing with straightforward query requirements, as it allows for easy and efficient data retrieval. The Star Schema's design enables fast aggregations and joins by minimizing the number of tables involved in queries, making it particularly suitable for reporting and analysis in scenarios where response time is critical. It is a go-to choice for many data warehousing scenarios, especially when simplicity and query speed are priorities.

The Snowflake Schema is ideal for scenarios where the database size is large and data redundancy needs to be minimized, although it can lead to more complex queries and potentially slower query performance due to the increased number of joins. It's a preferred choice when detailed data analysis is required and the organization prioritizes data accuracy and normalization over query speed.

The Data Vault model is indeed an innovative approach to data warehouse design, particularly for complex, large-scale environments. Its hybrid nature, blending normalized and dimensional modelling techniques, offers distinct advantages and some challenges. For organisations dealing with large, complex data sets, particularly those requiring high levels of auditability and flexibility in the face of changing data sources and business requirements, the Data Vault model offers a robust solution. However, it's essential to weigh its benefits against the potential complexity and resource requirements to ensure it aligns with the organisation's capabilities and needs.

This pattern is particularly well-suited for large, complex, and evolving data environments, where the ability to quickly integrate new data sources and adapt to changing business requirements is crucial. The Data Vault model excels in scenarios requiring a high degree of auditability and traceability, as it maintains a comprehensive history of data changes over time. It's ideal for businesses that anticipate frequent changes in their data structures and need a robust architecture that can accommodate these without significant redesign.

Data lakes exist because of the yet-to-be-tapped value in the unstructured data produced in an organization - for e.g. the "user clicks" on the website (a.k.a. 'digital exhaust'), metadata in numerous documents produced by its engineers, etc. Storing and analyzing this mostly unstructured data can yield so many insights. For a Data Lake to be successful (which most are not, for various reasons), you have to - Understand the end goals and align your resources to that goal - Catalog the sources and sinks and work through a 'data discovery' process - Use the right tools: Jupyter Notebooks, Streaming services, etc. are great - Use Cloud services such as AWS S3 or GCP's GCS to land this data and build around that - Build incremental success

I have rarely seen cases where data lakes are in isolation. Data Lakes come into existing and have become popular for a number of reasons. 1. Store source data in their original form, with little to very little transformations of the source data, so that different teams can use this data based on the problem they are trying to address. 2. Cloud object storage, which decouples compute from storage, allows to store any amount of data. 3. The (traditional) Data Warehouses which are schema-on-write, need ETL; any change in the requirements needs ETL to be changed, which is a time-intensive process. When new requirements come sometimes requires source data. 4. Importantly, data lakes can store variety of data as they are schema on read.

Data Lakes allow for storing data without a predefined schema, making them ideal for scenarios where data is diverse and the requirements for data analysis are not fully known or are subject to change. They are best used when there is a need to store large volumes of data cost-effectively while retaining the ability to perform different types of analytics – like machine learning, real-time analytics, or SQL queries – across varied data types.