What skills should you have to excel in dimensional modeling for big data?

1 Understanding business requirements

The first skill you need is to understand the business requirements and goals of the data warehouse. What are the key performance indicators, metrics, and dimensions that the business users want to analyze? How do they want to slice and dice the data? What are the sources, formats, and quality of the data? By asking these questions, you can identify the most relevant and valuable data elements and how they relate to each other.

2 Choosing the right model type

The second skill you need is to choose the right type of dimensional model for your big data scenario. There are two main types of dimensional models: star schema and snowflake schema. A star schema has a central fact table that contains the measures and foreign keys to the dimension tables, which contain the attributes. A snowflake schema normalizes the dimension tables into multiple levels of hierarchy. The choice of model type depends on factors such as data complexity, query performance, storage efficiency, and maintenance effort.

3 Designing facts and dimensions

The third skill you need is to design the facts and dimensions of your dimensional model. A fact is a numerical value that represents a business event or measurement, such as sales, revenue, or clicks. A dimension is a descriptive attribute that provides context for the facts, such as date, product, or customer. You need to decide what facts and dimensions to include, how to define them, how to handle updates and changes, and how to ensure consistency and accuracy.

4 Applying best practices

The fourth skill you need is to apply best practices for dimensional modeling for big data. Some of these best practices include: using surrogate keys for dimensions, avoiding nulls and blanks, using conformed dimensions for cross-subject analysis, using degenerate dimensions for transactional facts, using bridge tables for many-to-many relationships, using outriggers for rarely used attributes, using mini-dimensions for rapidly changing attributes, and using junk dimensions for low-cardinality attributes.

5 Implementing ETL processes

The fifth skill you need is to implement ETL (extract, transform, load) processes to populate your dimensional model from the source data. ETL processes are responsible for extracting data from various sources, transforming data into the desired format and quality, and loading data into the target data warehouse. You need to design and execute ETL processes that are efficient, reliable, scalable, and secure.

6 Optimizing performance

The sixth skill you need is to optimize the performance of your dimensional model for big data. Performance optimization involves tuning the data warehouse structure, configuration, and query execution to achieve fast and accurate results. Some of the techniques for performance optimization include: partitioning large tables, indexing key columns, compressing data, caching data, using columnar storage, using parallel processing, and using materialized views.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?