However, there is no “one tool fits all” when it comes to data analysis, and it is even more challenging to find quality open-source tools. You can easily find a variety of high-end, expensive tools that serve different analytics purposes on Google or another search engine, and Github can find you repos, however the chances of them being flawed are high.
For instance, financial data and geospatial data require diverse tools, modules, and platforms to successfully analyze their respective data. To assist you’re your search for useful data analytics tools, I have highlighted the top five open-source tools that you can use for data analytics, as well as their unique functionalities and weak spots.
# 1 - PostHog
Posthog is mainly built for developers in product analytics. It is self-hosted and supports seamless self-deployment. For instance, you can quickly and seamlessly deploy PostHog on Heroku with only one click. This makes it easier for inexperienced or beginner developers to activate their product without any prior experience or in-depth knowledge. For anyone new to open source, PostHog is a must try. However, I have found that PostHug doesn't work well with email links or ad campaign tracking.
# 2 - Wasabi
Wasabi is a real-time API driven platform that should be second on your list of open-source tools when working with large datasets. It is a fast and easy to use platform with very little instrumentation required. With Wasabi, you own your own data and can run projects across web, mobile devices, or desktops. Wasabi is designed in a way that it is possible to run it on cloud or on any remote networks or devices. Wasabi’s REST API is language and platform independent with features that support data analysis and metrics visualization. The best part? Your data is solely within your control.
#3 - DataDistillr, Inc.
My personal favorite is DataDistillr, Inc. It Is backed by the CEO of Kaggle and Foundation Capital. Using DataDistillr, you can explore data without any support from a data engineering team, and there is no need for complex ETL (Extraction, Transformation, & Loading) maneuvers. You can query any kind of data and visualize it, all on one platform. You can create your own personal organization, projects or teams within the platform, upload your data, or use APIs to collect data in real-time. API configuration is super easy on the platform and all you need is basic knowledge of APIs to get started. Publishing your data products as an APIs is even easier. DataDistillr also makes it possible for teams to share or work on a project simultaneously. DataDistillr makes it possible to easily understand the data and to quickly derive insights from the data using its visualization feature. Using DataDistillr’s chat feature reduces the time required to convey messages to your team members. DataDistillr is your one-stop-data-shop to queury all kinds of data and deploying it as an API.
# 4 - Hastic
When working with data, anomalies are a common occurrence. It becomes difficult to detect anomalies when working with large datasets. In fact, data scientists often wish for a tool that makes the detection of anomalies easier. Here comes Hastic to your rescue! Hastic is an anomaly detection tool that can search for anomalies or outliers in your data and notifies you of the occurrence and reoccurrence almost immediately. To get started, merely set up the predefined parameters to help the platform detect them. Hastic, however, works with Grafana only, which precludes you from visualizing the plots in Superset or Metabase. You may also find it a little difficult to set up and maintain since tha user documentation is sparse.
# 5 - Timescale
Finally, Timescale is an open-source platform designed to assist businesses in achieving results and scaling rapidly. Timescale is specifically designed for time series management of data. It mainly relies on PostgreSQL with full SQL reliability. You can manage Timescale easily and host it remotely. Its low cost along with compression rates of about 94 - 97% helps with the improvement of
performance over AWS, Azure, or GCP in more than 75 regions. The drawback of the tool is the time required to develop an understanding of the product. Additionally, while Timescale has a relational database model, due to its complex time series management algorithms, it can be difficult to get started for new users.