More and more, the ability to provide customers with a real-time analytics experience has become a most requested feature for many products. Embedding analytics into the SaaS solution is a critical aspect of staying competitive and providing customers with the most up to date data. In this article, we hope to provide those professionals with a process for using SQL databases for real-time dashboards by focusing on the embedded analytics use case.
Real-time dashboards offer a dynamic and instantaneous view of data, allowing businesses to make informed decisions each and every day (or perhaps every hour!). SQL (Structured Query language) serves as the backbone for retrieving and manipulating data efficiently. Understanding the relationship between real-time dashboards and SQL databases is essential for creating a seamless analytics experience.
The first step in using SQL databases to create real-time dashboards is to connect to the database where your data is stored. The most important step of connecting to your database is database security. Depending on the type of data stored within your database, you might need additional protocols for securing your data. Whether your data is stored on-prem or in the cloud, will also inform the actions you need to take in regards to security. It is always best to set up users with specific permissions that allow them to only access certain databases, schemas, and/or tables. For example, you might not want every team member to be able to access your business’s finance data. This can all be accomplished through security protocols. Each database is different, so you’ll need to reference their documentation for your specific use cases.
There are a number of SQL databases that can be used for analytics. In fact, depending on your latency and real-time requirements, most SQL databases can provide live, real-time insights. There are also specific operational databases such as Materialize and databases built for scalable, real-time analytics that leverage SQL as well such as Rockset. If you’re working on a greenfield project and deciding which database to use, it’s important to evaluate the pros and cons of each database out there today.
Once you are connected to your database, you’ll have access to all the data you need to create a real-time dashboard. At this point, you might find it difficult to know where to start. This is why, before you start creating charts and tables, we recommend you first define your key metrics and KPIs. This process involves not only defining the relevant metrics for your dashboard, but also structuring the underlying data efficiently and ensuring real-time updates for a dynamic user experience.
Begin by identifying your organization’s overarching goals. Whether it’s increasing revenue, improving user engagement, or optimizing operational efficiency, align your key metrics with these broader objectives. It’s also a good idea to consult those who will be using the dashboard. Understanding diverse perspectives ensures a well-rounded selection of metrics. Plus, you want to be aligned so that you don’t spend your time creating something that isn’t used. Once you’ve defined the metrics you want to display on your dashboard, you’ll need to prioritize them. You will want to place the highest priority metrics on the top and lower priority metrics towards the bottom. Also, remember that users will read your dashboard left to right, so your most important metric should be in the top left.
Finally, ensure that the selected metrics are relevant, actionable, and aligned with the objective of your real-time analytics initiative.
Once key metrics and KPIs are identified, the next step is to organize and structure the underlying data. This involves creating tables and/or views that serve as the foundation for your real-time dashboard. Start by designing a database schema that accommodates the selected metrics and KPIs. Consider the relationship between different data points, ensuring a logical and efficient structure.
In SQL, views act as virtual tables, offering a simplified and abstracted layer for accessing complex data. They play a crucial role in streamlining data access for real-time dashboards. You can use views to present data in a way that is most relevant to the intended audience, whether that be executives, analysts, or operational teams. By creating views, you can select only the data you need from the underlying tables and merge different datasets together to create an organized structure for your KPIs.
Each time a view is queried, it pulls fresh data from the underlying tables, which makes them a great solution for real-time data processing. As long as the underlying tables are updating with new data, it will flow through to your newly created views.
You can also pre-aggregate data so that relevant data is precalculated, reducing live load times. Think through at what level of aggregation will be most beneficial to your dashboard. For example, if you have individual order data, which would have many orders each day, you could roll it up to a daily view. You would then have the flexibility to display the data in your dashboard at the daily, weekly, monthly, etc level. If you chose to aggregate your data to the monthly level, you would lose the ability to display data by day. Each case will be different and you’ll need to judge for yourself which strategy will work best.
Keep in mind that the cadence at which you aggregate the data will determine how “Real-time” it is. Understanding user needs is crucial here as there is always a tradeoff between how real-time the data is and how quickly you can load the data.
Implementing real-time updates requires a nuanced understanding of SQL and data triggers. Triggers are mechanisms that allow automatic execution of predefined actions in response to specific events. When it comes to real-time updates, understanding and harnessing triggers can significantly enhance the responsiveness of your dashboard.
Triggers are event-driven, responding to actions such as data modifications, insertions, or deletions. Triggers are instrumental in initiating updates when relevant data changes occur. Unlike scheduled jobs, triggers provide an immediate response to data changes. This instantaneous reaction ensures that real-time updates are seamlessly integrated into the dashboard, offering the latest insights.
While triggers offer immediate responsiveness, careful consideration must be given to their impact on performance and cost. Striking a balance between real-time updates, efficiency, and cost is essential to avoid unnecessary overhead.
Scheduled jobs play a crucial role in automating tasks at predefined intervals. When it comes to real-time updates, setting up scheduled jobs ensures that data refreshes regularly, maintaining the relevance and accuracy of the dashboard. Define the frequency of scheduled jobs based on the needs of your business and the criticality of receiving updates. Whether it’s hourly, daily, or somewhere in between, periodic data refreshes keep the dashboard synchronized with the latest information.
Another benefit to scheduled jobs is that they offer resource optimization by distributing the load of data updates over time. Instead of real-time triggers that respond immediately to changes, scheduled jobs provide a controlled and distributed approach to maintaining data relevance.
A final consideration in regards to scheduled jobs is process monitoring. You will want to implement logging and notification mechanisms in order to regularly check the performance and be notified of any issues that may arise if a process fails.
A user-friendly and customizable dashboard is key to maximizing the impact of embedded real-time analytics. Choosing a product that meets your specific needs means considering the type of database your data is stored in, how you will want to display your data, and how you will want to share this data with others. Explo empowers businesses to tailor dashboards to specific user needs, connects to all relational databases, and has several capabilities for sharing data.
Explo’s drag-and-drop interface allows creators to effortlessly customize dashboards. Whether arranging widgets, adjusting layouts, or choosing branded color schemes, the platform’s ease of use facilitates easy customization without the need for extensive technical expertise.
Explo connects directly to SQL databases, allowing for real-time updates to charts and graphs. This feature is particularly valuable for industries where up-to-the-minute insights are critical, such as finance, healthcare, or e-commerce.
As you can see, leveraging SQL databases for real-time dashboards requires a strategic approach, and this guide provides a roadmap for product leaders to navigate this journey successfully. There are many considerations around developing a data structure to support your real time dashboards. Make sure you’ve considered your business needs, KPIs, and current data structure. As technology continues to advance, the balance between SQL databases, real-time analytics, and analytics platforms becomes pivotal for organizations aiming to create embedded analytics solutions for their customers.
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