SQL Data Visualization Guide: Tools, Examples & Alternatives

Anyone that interacts with their database knows the importance of data visualization for reporting and analysis. While traditional BI tools are a great way to handle the task, they can often be complex. Business users or small teams will often rely on their technical team members to avoid learning how to use SQL data visualization tools. This, over time, can lead to productivity problems.

We wrote this article to go over all the different categories of tools that enable data visualization, and help teams build a stack that works for their unique needs. From the more common data exploration tools to the newer categories of conversational BI, we will briefly go over the tools you can explore. Let’s get started.

What is SQL data visualization?

SQL data visualization is the process of turning data points stored in databases (like PostgreSQL) into visually appealing charts, graphs and dashboards. This can make it easier to review and compare data at a glance instead of relying on raw tables and queries. 

SQL, the language which underlies the visuals, handles the logic, while the visualization presents an interactive package. It makes the work of analysts easier to understand and therefore contributes to faster insights, and improved decision making. This is especially true for teams where not everyone is SQL-literate. 

The challenges when charting with SQL

SQL is great at answering specific questions. However, it has a hard time communicating these answers in a visual way. As we mentioned above, SQL outputs answers in the form of tables. Turning these tables into visually appealing dashboards requires actions that SQL was not designed to handle. As a result, you end up with bulky queries that lack clarity, making it hard to read and edit. 

Charting with SQL is very prevalent when it comes to time-based data, which is a core SaaS metric. Date issues, inconsistent intervals, and comparison period all require precision when charting with SQL. Writing and adjusting these queries can be challenging, and templates are often adjusted and readjusted. Over time, this makes it hard for teams to understand what insights the charts attempt to convey. 

The result is that data visualization becomes static. Charts do not display data in a way that conclusions can be drawn, and they create more questions than answers. For small teams this can be a bottleneck that slows them down. Hence why we have specific data visualization tools.

Understanding SQL visualization tools

There are several categories of data visualization tools. Each is built for a specific purpose, and your decision to explore them depends on the needs of your business. Some tools require in-depth SQL knowledge, which is why they are best for Business Intelligence officers or data analysts. Others are more aimed towards business users who need the data but lack the SQL skills to interact with the database.

(BI) data exploration tools 

Business Intelligence tools are by far the most popular category. It is the type of tools that come to mind when we talk about SQL. In short, (BI) data exploration tools sit between the data and the team. They connect to your database and create a layer of semantics or metrics on top of the tables you would normally export. These enable users to filter, slice, group, and compare data in a simple manner.

BI tools primarily translate query results into charts, tables, and visuals through structured templates. That said, they are also great for non-technical users who need quick insights without reliance on devs, like founders or product managers who wish to track KPIs. Learning how to use these tools qualifies one from the “what” to the “why”, leading to better decisions.

Some of the common BI tools include Tableau and Power BI, which are popular in larger companies. There are also tools that work better with small teams and startups. Tools like Mode are quite popular make it easier for small teams to work without relying on technical members. If you are hesitant to connect your database to external tools, you may also explore newer options to find a Power BI open source alternative. The same is true when looking for an open source Tableau alternative.

Open Source reporting tools

Open Source reporting tools are generally considered a great option for SQL data visualization. Unlike data exploration tools which are aimed towards discovering new questions, reporting tools are designed to answer known ones. They center on predefined queries, and recurring outputs, with SQL remaining at the core. They are made for regular reporting that is stable and repeated over time.

Similar to the previous category, reporting tools connect to the database or warehouse of an organization, and rely on SQL. They are more common with smaller teams, startups, and teams that want regular reporting embedded into their product. The more technical part of the team defines the queries and the resulting reports, and the rest of the team reviews the data visually through team meetings or automated emails.

The SQL data visualisation process is quite similar to BI tools, but are often considered “minimal” in comparison to the latter. While there is not much flexibility to go with, it is a great way to avoid getting lost in over-customization and eventual confusion. Popular open source reporting tools include Apache Superset, which is perhaps the oldest and most popular tool in this category, Redash, which is a lightweight alternative, and Grafana which is great for time-series and operational metrics. It might be worth mentioning that some BI tools are open source and great at reporting, which makes this part of both categories. Metabase is one such tool, and therefore consider a great alternative to the more traditional BI tools.

No code analytics dashboard builders

Low or no-code analytics dashboard tools are built with the intention to simplify SQL data visualization. They enable anyone to connect to the database and create dashboards or charts without technical requirements. These types of tools work like most no-code tools - users can simply drag and drop interfaces, embed pre-built widgets, and add connectors to popular databases or SaaS tools. They are popular with small teams that handle many processes and may thus not have the technical background to handle analytics tasks. The whole idea is that no-code analytics tools offer the most important functions to track the performance of the organization without spending too much time on it. 

The same is true about these tools when it comes to the visual aspect. While the charts and dashboards are not as detailed as the prior two categories, they offer customizable charts, tables, and numerous metrics, with added interactive features to segment the data further. Popular options that are worth looking into include Chartio, Grow, Klipfolio, and Cumul.io. All of these tools work in a similar manner and are easy to learn, making the choice primarily one of UX/UI rather than features available. 

Developer-focused visualization tools

Dev-friendly visualization tools are great for customized data visualizations on top of SQL data tables. They give full programmatic control to the user and integrate with multiple application stacks. This makes them more flexible than no-code interfaces and prebuilt templates found in other categories. On the flip side, while they can handle complexity and non-standard visualization requirements, they are not suitable for non-tech users. This makes the particular category best suited for growing organizations and developer-friendly teams.

Some popular examples of developer-focused visualization tools include D3js, Plotly, and Vega.

Notebook visualization tools

Notebook visualization tools operate in a spreadsheet-style environment. They are great for testing and exploratory analysis, allowing users to execute queries, investigate results, and display them visually. Users can then rerun, modify, or expand analyses to attain the desired results.

Visualizing data in this manner is best suited for data analysts/scientists who want to explore data rather than present it for reports. They are often used to test hypotheses, ad-hoc analysis, quality checks, and exploration of prototype metrics. Again, this category has a high degree of customization and is therefore best for technical users. Examples of tools include Jupyter Notebook, Observable, and Apache Zeppelin.

Embedded analytics tools

Embedded analytics tools integrate visuals within other apps and software product. Their primary focus involves the delivery of analytics as a feature of said apps, enabling authentication, permissions, and customizations as required by each tool. Instead of focusing on BI, embedded analytics tools provide an analytics infrastructure layer within a broader user experience.

These tools are best for software companies and apps that want to embed data visualization without having to create their own, custom layer. Their core function involves customer facing dashboards or internal analytics. Examples include Sisense, and Embeddable.

Database monitoring tools

If you are more interested in database performance instead of business data, then this is your category of tools. Database monitoring tools visualize the operational metrics of your database and provide insights for query execution, latency, and resource usage. They also spot errors and diagnose slow queries, both of which may affect the health of your database. Visualization are usually presented in time frames, helping teams fix issues and optimize performance.

Database monitoring tools are commonly used by database admins, data infrastructure managers, and platform engineers. Examples of such SQL query visualizer tools include pgAdmin, Redgate SQL Monitor, and Datadog.

Emerging category: Conversational BI (NLQ)

The categories described above are most commonly related to data visualization. However, with the introduction of AI, new categories start to emerge. One of them, known as Conversational BI tools (Natural Language Queries), simplifies data visualization for non-technical team members in smaller organizations. These tools do not replace traditional BI tools but, instead, offer, a self service analytics option that anyone can use to access database insights. Here is how it works for TalkBI: an organization links their PostgreSQL database to the tool. They can then interact with TalkBI as they would with any LLM. They simply ask a question written in natural language. The tool then automatically converts the prompt into SQL and interacts with the linked database, giving you the insights you are looking for. The data can then be exported and presented into a structured SQL data visualization report that is much “leaner” than Metabase and Power BI. TalkBI is a great solution for product or marketing managers who need access to data but do not have sufficient SQL knowledge to do so.

Wrapping up

If you’ve made it this far you should now have a better idea of SQL data visualization. In this article we briefly described the importance of visualizing data, both for performance and database health. For those with little SQL knowledge looking to understand how to get started, we’d recommend checking AI tools that automate the visualization process of your datasets. It is both easier and faster, especially if you are part of a smaller team. To learn more about TalkBI and how it performs compared to more traditional BI tools, make sure you book a demo with our team.