Dimensional modeling is a popular approach for designing data warehouses that are easy to understand, query, and maintain. However, when dealing with big data, dimensional modeling faces some challenges, such as scalability, performance, and complexity. In this article, you will learn how you can scale a dimensional model for big data by applying some best practices and techniques.
One of the key decisions in dimensional modeling is choosing the level of detail or granularity for your fact and dimension tables. The granularity determines how much information you can store and analyze in your data warehouse. However, it also affects the size, complexity, and performance of your data model. If you choose a too low granularity, you will end up with huge tables that are difficult to load, query, and manage. If you choose a too high granularity, you will lose some valuable insights and flexibility in your analysis. Therefore, you need to balance the trade-offs between granularity and scalability. A good practice is to start with a moderate granularity that meets your business requirements and then adjust it as needed.
One must also understand that changing grain of a fact table is not an easy operation. One must quite likely repopulate the fact table again after making changes in the table definition.
Scaling a dimensional model for big data involves adapting to the challenges of handling vast and diverse datasets. Use distributed storage and processing frameworks, partition data for parallel processing, be flexible with data formats, apply compression, and consider data sampling. Integrate data from various sources and prioritize data governance and security. Invest in scalable hardware or cloud solutions, employ distributed processing engines, and focus on monitoring, optimization, and metadata management to maintain a performant and reliable dimensional model in the face of big data complexity.
Partitioning is a technique that divides a large table into smaller and more manageable chunks based on some criteria, such as date, region, or category. Partitioning can improve the scalability and performance of your dimensional model in several ways. First, it can reduce the amount of data that needs to be scanned, loaded, or updated in each operation. Second, it can enable parallel processing and distribution of the data across multiple nodes or servers. Third, it can simplify the maintenance and backup of the data by allowing you to operate on individual partitions. Partitioning can be applied to both fact and dimension tables, depending on the size and usage of the data.
Analytical database management system is one solution, this means that tables are distributed by different data distribution methods such as hash - distribution. Partitioning can be added to this.
A surrogate key is a unique identifier that is generated by the data warehouse and assigned to each row in a dimension table. Surrogate keys are not derived from the source data and have no business meaning. They are used to link the dimension tables to the fact tables and to handle changes in the dimension attributes over time. Surrogate keys can help you scale your dimensional model for big data by avoiding some problems that arise from using natural keys or composite keys. For example, surrogate keys can avoid data duplication, inconsistencies, and conflicts that may occur when the source data changes or has errors. Surrogate keys can also simplify the queries and joins between the tables by reducing the number of columns and values involved.
Surrogate keys unfortunately create also an dependency because lookups must be done against dimension tables to get surrogate keys by matching business keys from staging tables against dimension table values. When there are many such processes it can be difficult to parallelize operations.
A late-arriving dimension is a situation where a dimension record arrives after the corresponding fact record in the data warehouse. This can happen due to delays, errors, or missing data in the source systems. Late-arriving dimensions can cause issues in your dimensional model, such as missing or inaccurate data, orphaned facts, or incorrect aggregations. To handle late-arriving dimensions, you need to implement a strategy that can update the dimension and fact tables accordingly. One common strategy is to use a placeholder value for the surrogate key in the fact table until the dimension record arrives. Then, you can replace the placeholder with the actual surrogate key and update the dimension attributes as needed.
The final step to scale your dimensional model for big data is to optimize your queries that access and analyze the data. This can improve the performance, efficiency, and accuracy of your data warehouse. Factors such as the database engine, the data structure, the query logic, the indexing, the caching, and the tuning can all affect query optimization. To optimize your queries, you should use filters and predicates to limit the amount of data that needs to be processed and returned. Additionally, you should use joins and subqueries wisely to avoid unnecessary or redundant data retrieval and manipulation. Aggregations and groupings can reduce the number of rows and columns that need to be processed and returned. Analytical functions and window functions can be used to perform complex calculations and transformations on the data. Finally, you can use EXPLAIN or ANALYZE commands to examine the query execution plan and identify potential bottlenecks or opportunities for improvement.