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How do you optimize star schema for scalability and performance?

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Star schema is a popular data warehouse design that organizes data into a central fact table and multiple dimension tables. It simplifies queries, improves performance, and supports dimensional analysis. However, as your data volume and complexity grow, you may face some challenges with star schema, such as data redundancy, storage space, and query speed. How do you optimize star schema for scalability and performance? Here are some tips and best practices to consider.

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1 Use surrogate keys

Surrogate keys are artificial identifiers that link the fact table and the dimension tables. They are usually integers that have no meaning or relation to the business data. Using surrogate keys can improve performance and scalability by reducing the size of the fact table, avoiding joins on complex or composite keys, and enabling faster lookups and indexing. Surrogate keys also allow you to handle changes in the dimension data, such as updates, deletions, or insertions, without affecting the fact table.

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Sagar Navroop

Data Architect | AI | MLOps | AWS | SIEM | Observability | Technologist
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Optimizing a star schema for scalability and performance requires a multi-faceted approach. Surrogate keys enhance performance by simplifying joins and ensuring uniqueness. Partitioning the fact table based on time or another suitable criterion improves data retrieval efficiency. Implementing aggregate tables precomputes summary data, reducing query complexity and improving response time. Utilizing bitmap indexes on low-cardinality attributes accelerates query performance, especially for ad-hoc analysis. Optimizing queries involves proper indexing, query tuning, and utilizing database features like query caching. On AWS; Redshift offers options for efficient data retrieval, and optimizing star schema performance in cloud environments.

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Andreas Koblischke

CEO bei datenkultur | Data & Power BI & Fabric Enthusiast | Business Intelligence | Data Science
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Ein Surrogate Key übersetzt eine Zeichenfolge in genau eine Ganzzahl. Vorteile: ? Klein: In den Fakten und Dimensionen steht nur eine Zahl, und kein String aus Inhalten mehrerer Felder. ? Schnell: Es muss für eine Relation nur eine Zahl, statt einer langen Zeichenfolge auf Gleichheit geprüft werden ? Sicher: Relationen enthalten immer alle erforderlichen Felder. ? Einfach: Nur genau ein gleichlautendendes Feld für eine Relation erforderlich. ? Allgemein: Optimal für Tools wie Power BI, in denen zusammengesetzte Schlüssel nicht nativ unterstützt werden. Nachteile ? Das Bilden eines Surrogate Keys kostet Zeit beim Laden der Daten. ? Alle passenden Dimensionen und Faktentabellen müssen den Surrogate Key bilden.

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Lepakshi Nadh ("Nadh") J.

Sr Manager/IS Account Manager
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In my experience with SAP BW and many other RDBMS databases, I have extensively worked with Surrogate keys (SID). In my opinion, this is the most secure way to define the primary key, they are numeric with less storage space, simple to generate and they process faster than the keys with alphanumeric char. These keys are immutable as they never updated since no real business value. With the SID, we can avoid multi column relationships when we join the other tables. This is very helpful as it does not change underlying business data or rules. Query analysis can be faster due to less index to maintain with the SID. An example of a SAP table for product hierarchy field which is 18 char long, instead with SID, only the SID value is stored.

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2 Partition the fact table

Partitioning the fact table is a technique that divides a large table into smaller, manageable pieces based on some criteria, such as date, region, or product. Partitioning can improve performance and scalability by reducing the amount of data scanned, filtered, or aggregated in a query, allowing parallel processing, and facilitating data loading and maintenance. You can use different partitioning strategies depending on your data warehouse platform and your query patterns, such as range, list, hash, or composite partitioning.

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3 Implement aggregate tables

Aggregate tables are summary tables that store pre-computed values from the fact table, such as counts, sums, averages, or ratios. Aggregate tables can improve performance and scalability by reducing the number of rows and columns accessed in a query, avoiding complex calculations on the fly, and providing faster response times. You can create aggregate tables at different levels of granularity and detail, depending on your analytical needs and query frequency, and use them in conjunction with the base fact table.

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Andreas Koblischke

CEO bei datenkultur | Data & Power BI & Fabric Enthusiast | Business Intelligence | Data Science
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Aggregate Tables verwende ich nur, wenn die Datenmengen gigantisch sind. Durch das Zusammenfassen von Daten verliere ich per Definition Informationen. Das macht in Data Warehouse oder Data Lakehouse Szenarien nur Sinn, wenn die feiner granulierten Daten niemals ausgewertet werden müssen. Andernfalls übernehme ich alle Daten und führe die Aggregationen erst in der Auswertungsschicht aus. Microsoft Analysis Service, Power BI oder Fabric beispielsweise optimieren die Daten stark und laden dann alles in den flüchtigen Speicher (RAM). Zusammenfassungen sind sehr schnell und ich kann trotzdem bis auf die letzte Buchung hinunter zoomen. Erst wenn die Visualisierung nicht mehr performant l?uft, denke ich an Aggregationen. Niemals vorher.

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4 Use bitmap indexes

Bitmap indexes are a type of index that use bit vectors to represent the presence or absence of a value in a column. Bitmap indexes are especially useful for columns with low cardinality, meaning they have a small number of distinct values, such as gender, status, or category. Bitmap indexes can improve performance and scalability by compressing the index size, enabling fast bitwise operations, and supporting efficient join and filter operations. Bitmap indexes are often applied to dimension tables in star schema.

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5 Optimize query performance

Optimizing query performance is a general practice that involves writing efficient and effective SQL statements to retrieve data from the star schema. Some of the techniques that can help you optimize query performance are: using appropriate join types and conditions, limiting the number of columns and rows returned, applying filters and aggregations early, avoiding subqueries and correlated queries, using hints and explain plans, and tuning database parameters and settings.

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6 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?

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How do you optimize star schema for scalability and performance?

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1 Use surrogate keys

2 Partition the fact table

3 Implement aggregate tables

4 Use bitmap indexes

5 Optimize query performance

6 Here’s what else to consider

Data Warehouse Architecture

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