As a data analyst helping navigate my company through these choppy economic waters, I understand the conflicting needs of providing actionable insights while keeping costs low. Luckily, with the expanding array of open-source data analytics tools, you can provide the analysis your team needs on a budget.
From small garage start-ups to large conglomerates, many businesses use open-source data analytic tools as part of their everyday work process. These tools provide the insights they need without the headache of invoices and licensing requirements. Good tools also have a thriving community that can provide support when things go wrong, while the tech-savvy among you can take advantage of the open-source nature to build customized tools for your organization’s specific needs. However, these benefits do come with some costs. Though the community may be helpful, it doesn’t provide the same level of care or service that a dedicated service will offer. Additionally, some tools may have reliability and stability issues - especially those that are not backed by an organization and are staffed by volunteers.
Navigating these waters to choose a solution can be difficult, so we compiled a list of our favorite open-source data analytic tools that may be a good fit for your organization.
Apache Superset – an enterprise-grade but technical solution
Created as a hack-a-thon project by Maxime while working at Airbnb, Apache Superset has now spread throughout the data analytics field after graduating from the Apache Incubator program in 2021.
This is a modern, enterprise-ready business intelligence web application and it shows. However, that does come with some caveats, namely you will need to have a technical skill set to utilize this powerful tool. From its technical installation requirements to a steep learning curve, Apache Superset may not be right for you if you are looking for a quick and dirty solution. However, if you’re willing to put in the time and effort to learn this beast, you will be a step ahead of your competitors.
Metabase – No SQL? No problem!
For those who don’t want to dive deep into the depths of SQL code, there is the perfect data visualization tool for you. Metabase will help your team answer their own questions about their data with no SQL required.
As data analysts, we all know the competing needs of different teams across the organization. Instead of spending time and effort for every single report they need, you can help your teammates help themselves with this solution. This simple platform is easy to install and get running within minutes.
There are some downsides, though. This tool is simple to use, but that also means it may not be able to answer your more complicated questions. But for the data analysts pulled every which way, this can be a life saver.
KNIME – a partnership of visualization and coding
If you want to have a combination of drag and drop analytics and integration with your python/R code base, then KNIME might be the right tool for you. The ease of use does come with some downsides, as you do not gain as much flexibility as you would if you were running python or R by itself. However, many users find KNIME attractive for its combination of low cost, ease of use, and code integrations.
R Markdown – for the R scientist
If your team is an R shop, then what better tool to use than one already integrated into your work process? With R Markdown you can create visualizations while writing your code. Not only is it natively integrated into your work process, but it also updates with any changes you made with your code, making re-running reports a breeze. This close integration has its downsides as well; since it’s so tightly connected with R, it’s hard to access and view the visualizations outside of the R environment.
Grafana - real-time alerting
For the data analyst dealing with real-time data, Grafana’s alerting features can be a lifesaver. With this feature, users can perform real-time monitoring of their data and can quickly act upon any issues they find. Users gave glowing reviews of their wide variety of visualizations and real-time monitoring. There are some limitations on what a user can query and a learning curve to the software.
Bokeh – for the Python geek
If you’re looking for something similar to R Markdown but you work with Python, I have good news for you. Bokeh is the go-to open source data analytics tool. Since it’s integrated with Python, all you need to do is import the Bokeh library. With their interactive visualizations and great documentation, users' only fault with the software is that it can be a little bit more involved compared to other visualization libraries and the interactivity can cause performance issues with large data sets.
Looker Studio - the Google shop
Google users can rejoice! Google has their own data visualization tool. Since it’s a Google product Looker Studio has strong integrations with the rest of the G-suite such as Google Ads, Google Analytics and Big Query. Some users do complain about a lack of support if they are not a premium user, and presentation options are limited.
Bipp - SQL-focused BI tool
Bipp was created from day one as a business intelligence tool designed for SQL analysts. Its platform simplifies SQL queries and assists users with their AutoSQL generator while fostering collaboration through their version control. Users warn that Bipp’s custom SQL language does have a slight learning curve for those who are unfamiliar with the syntax.
I like to say that when it comes to tools, there isn’t one that rules them all, but there will be one that fits your needs. I understand that navigating the vast ocean of open-source tools can be a daunting one and I hope this article provides the guidance you need to find the tool that’s perfect for you.
There’s still one piece of the puzzle missing here - where all your data is being stored. Here at Panoply, we not only provide you a simple and easy solution to store your data but also consolidate your information from all your other platforms. If you have data from Shopify, Facebook Ads, Square, and Hubspot, we can consolidate it all into one single source of truth that your data analytics tools can draw from. Book a demo to see how!