A data warehouse is essential to a company’s business intelligence strategy. It’s the core of all enterprise data. The data warehouse organizes, stores, analyzes, and manages the enterprise’s data for better business decisions. Optimizing your data warehouse can save you time by automating repetitive operations and freeing up your team to spend more on what matters. In this article, I share my top five hacks for optimizing your data warehouse.
1. Load multiple types of tables into one table.
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Data warehouses can store both operational metrics and statistical models. To optimize the loading process, you need to load numerous types of tables at once by adding additional columns or joining existing ones in different formats: SQL files, Excel spreadsheet, CSV files. This allows users to easily extract valuable insights from the data through various data analysis tools or create dashboards via a graph visualization tool. It also prevents errors, as many people use tables with only one or two columns. Here’s how it looks like:
2. Automate repeated work.
Your data warehouse should have its own script that runs several times throughout the day, every hour, and during weekends. Executing these scripts automatically loads them and creates reports with charts showing KPIs. When you can automate repetitive tasks, you don’t need to spend more time on them. And they are easy to delete. That means you spend less time on running your scripts, which frees up resources in your other jobs. If you find yourself repeating a few of these processes over and over again, consider migrating them in bulk by using third-party scripting platforms. Alternatively, run this batch job manually to free your staff to focus on complex operations, not tedious repeatable steps.
3. Create temporary storage.
If you do not want to allocate big chunks of memory for storing your daily logs and metrics, you can use temporary storage. They are located outside of main databases and help you keep track of historical observations without worrying about crashing your server when they don’t add value anymore. Temporary storage allows teams to quickly restore historical records, identify missing fields and make necessary changes. As an example, you can move your log table to another place if it has lost values due to poor management or because there has been a power outage. You could also write some custom logic to determine if certain columns are old or stale. These columns can automatically be migrated by their unique values across your entire database. Another approach is to use temporary storage as a copy of older metrics. Either way, you can leverage the features by setting a file name, creating triggers when specific actions occur to execute the above action, and so on. This approach will save you lots of trouble and will speed up your reporting.
4. Extract important values.
Data warehouses often contain large volumes of unstructured text data such as emails, forms, messages, receipts, etc. The extraction of information is crucial in extracting relevant insights and providing relevant recommendations to the user. With simple regex for email/message filtering, you can extract important values by separating the content of each message, e.g., first name and last name, or phone number and mailing ID. Also, use regular expressions to locate non-numeric characters like HTML code and stopwords like “and”, “or”, etc. (e.g., match the words containing the word “and” to the first occurrence of the word “a”). Once you know where to look for things, you can start analyzing them and take appropriate actions.
5. Monitor access patterns.
Data warehouses often consist of multiple tables with multiple dimensions. Therefore, some data can be accessed over a long period of time by multiple users, while others only by one person in a short time window. To monitor access, add views to the dashboard of your analytics or query tool. To check whether someone had accessed our metric record X in the past minute, set event triggered to validate access. Furthermore, you can define roles within your data warehouse so other users can perform searches.
When you implement any of my suggested solutions, you get tons of useful insights, especially when you analyze data in real-time. So, go ahead and implement these methods and see how much more you can achieve in just a little extra time!
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