SQL is a popular programming language used to interact with relational data in domains such as business analytics. It is widely used by professionals for tasks such as data manipulation, data analysis, and data visualization. Being able to interact with data is one thing, but translating that data into meaningful information is another.
In general, SQL allows you to ask questions and receive answers about your data, and each BI tool offers a different approach to doing so. You will then be able to make data-driven decisions about your business that will help you to achieve a significant competitive advantage.
We’ve compared different business intelligence tools before, but knowing how each interacts with SQL in order to create data visualizations will help you decide on the right tool for your organization. Keep in mind that all tools pretty much have the basic toolkit to create the visualizations you need, which tool you choose will thus depend on which challenges, the tools put forward, you find will be the easiest to tackle for your company.
The following are the 6 widely used data visualization tools for SQL:
Toucan
Toucan is a 0-code embedded analytics and business intelligence tool that can integrate into any database in just one click.
With an easy-to-use interface built for non-technical users, Toucan is the only solution on this list with a primary focus on end-user analytics. To top it off, according to G2, Toucan is also the highest-rated business intelligence solution here.
Challenges
- Toucan offers customization and white labeling for your visualizations and dashboards. But if you need extensive customizations for every visualization then a Toucan expert is needed. The one-click database integration applies to all the popular data storage systems in the market but if you have a specialty database the connections will have to be done by a data expert.
- It is advised for all team members to have their own access to the platform as it comes with the comments feature, where users can talk, question and resolve issues surrounding insights obtained from visualizations. To send them to people outside the organization you have the annotate and share feature where you can send it as a pdf or slack message but not as a PowerPoint.
Power BI
Microsoft’s Power BI is a business intelligence tool that’s integrated into the Microsoft ecosystem alongside Excel, Access, SQL Server, and others.
With the Power Query Editor, you can build queries in Power BI using a series of menus and options, or you can go to advanced settings and use SQL directly. The key here is little code so you will need some level of technical knowledge.
Challenges
- Power BI is powerful, but will always work best with tools in the Microsoft ecosystem. If your technology stack doesn’t revolve around Microsoft products, you may run into small hiccups here and there. The major issue tho is that Power BI isn’t well suited to handle relational databases, which SQL relies on. James Anderson’s TrustRadius review said, “The relational database only allows one true join so you have to get creative.”
- After gathering your data with SQL, Power BI has a proprietary XML language that’s used to model and visualize data. This means that the data is locked in an ivory tower and kept away from non-technical. They are unable to create the visualizations they need and end up being completely reliant on data analytics to make informed decisions. This leads to slower workflows and unnecessary delays eventually affecting the bottom line.
Looker
Looker is a business intelligence tool with some powerful proprietary technology used to visualize data. It was acquired by Google cloud in 2020 and is now the main paid analytics platform offered by Google.
Looker uses a proprietary language called LookML to create SQL queries and model data. It was an exciting new feature when it was first introduced but since then it has become another language to learn alongside a decent knowledge of SQL greatly increasing the complexity needed to create visualizations.
Challenges
- LookML can speed up workflow for data analysts, but it’s built for them, not end business users. This ends up alienating non-technical end users and greatly reducing the adoption of analytics in the company. Further, the lack of actionable insights obtained by end business users and the delay in getting this information eventually affects the growth of the company.
- Looker Blocks are a robust library of pre-built code blocks that help you accomplish a wide variety of data visualizations. Though this seems like a great feature at the start, quickly you will realize that they are static visualizations that do not convey enough information. The lack of customization means not all teams can actively use them and again end up relying on data analysts and experts for their data needs.
- Looker has its own way of referring to the process of data visualization, which some find unintuitive. Bill Ulammandakh on Quora said, “Expect to be confused and learn a lot of weird and arbitrary terminology when ramping up with Looker.”
Sisense
Sisense is an enterprise-level business intelligence tool designed for efficient data analysis. Sisense has a fairly standard SQL editor but can streamline querying with shortcuts and saved snippets.
The biggest difference in the way Sisense handles data is the use of elasticubes. This is a columnar database that can handle terabytes of data making it good for SQL since SQL has higher storage needs.
Challenges
- Elasticubes come with some major drawbacks. First, you cannot manipulate data on the go. To simply add a column the entire cube needs to be restructured. Two, only the admin has the right to restructure cubs, essentially making data manipulation and visualization create inaccessible to end users. This makes them heavily dependent on the data team leading to delays in actionable insights and affecting overall company performance.
- The dashboards and visualizations are fairly basic and need a knowledge of R or Python to customize them. This increases the workload on the dev team and even after the customizations, the dashboards are not on par with the other solution on this list. An IT administrator on G2 said, “The actual … dashboards are relatively basic.” Another G2 reviewer echoed this by saying, “Formatting controls are very limited.”
- Sisense since the beginning has positioned itself as an enterprise tool focusing on analytics and visualizations for extremely large datasets. Thus their pricing reflects the same. If you are a small, medium, or even large size business, you will be paying for storage and features that you may never need or use.
Tableau
Tableau is a giant of the business intelligence world, with legacy visualization features for SQL. Once pioneers, all the analytics tools on this list have caught up to them since being acquired by Salesforce.
Tableau has a set of selections and filters to query data, as well as a Custom SQL option to code your queries. Tableau was built for data experts and continues to remain a tool where a high level of technical knowledge is needed.
Challenges
- Tableau is a legacy tool and as such has had multiple features squeezed in to keep up with the competition. The problem is all the features were made with data experts in mind leading to a lack of useability. This is especially counterproductive when the entire analytics ecosystem is moving towards end-user analytics and increased adoption rates. Even a Tableau fan said you’re “require[d] to invest 2-4 weeks, and you will gain 80% of the good parts of Tableau.” And Sara D, in her G2 review said, “There is a very steep ramp-up to use [Tableau].”
- Over the years Tableau has completely shifted its pricing to focus on larger enterprises. If the price appears low that is because all the important features are locked behind a paywall. Once you get all the features you need, it will be twice as expensive, if not more, compared to any other tool on this list. One user on Reddit summed it up best, “Is it just me or is licensing crazy expensive?”
Domo
Domo is a business intelligence tool founded with a mobile-first philosophy that peaked alongside mobile analytics and business intelligence. Now it has become a standard feature offered in most analytics solutions.
Queries in Domo are run through their DataFlow function, where you select multiple databases and can choose to transform that data directly with SQL.
Challenges
- Domo’s biggest drawback is its customer support. This wouldn’t be a major issue if it wasn’t for the cumbersome integration process that generally needs a lot of help from Domo experts. One Capterra reviewer said, “The support case has been open for nearly a month and has just been escalated to someone that appears to know how to read the code I have been sending them to troubleshoot THEIR integration.” And since the entire SQL visualization in Domo relies on connecting to multiple databases, the projects come to an unnecessary halt.
- The other issue is with the customization of visualizations. Domo does not offer a lot of wiggle room in how your end users see the data and this can lead to a lack of actionable insights or teams not getting the analytics view that they need.
Visualizing your tabular data is essential as you will not be able to derive meaningful insights from it without it. Business intelligence tools each have their own approaches to querying data using SQL, and it all comes down to which challenges your company is equipped to face. Objectively here are the best tools for each scenario, for data experts, it is Tableau, for development teams it is Sisense and overall best tool for data visualization for SQL is Toucan.
