20 MORE Hot Data Tools and What They Don’t Do

In the past few months, the data ecosystem has continued to burgeon as some parts of the stack consolidate and as new challenges arise. Our first attempt to help stakeholders navigate this ecosystem highlighted 25 Hot New Data Tools and What They DON’T Do — clarifying specific problems the featured companies and projects did and did NOT solve.

In the past few months, the data ecosystem has continued to burgeon as some parts of the stack consolidate and as new challenges arise. Our first attempt to help stakeholders navigate this ecosystem highlighted 25 Hot New Data Tools and What They DON’T Do — clarifying specific problems the featured companies and projects did and did NOT solve.

This effort was positively received by the data science, engineering and analytics communities, and spurred more engagement than we originally anticipated. Further, we were flattered to see the original post motivate other thought-provoking pieces such as 20 Hot New Data Tools and their Early Go-to-Market Strategies.

Taking it Further

Regardless, we quickly recognized our original post did not go far enough as we received dozens of emails, Twitter messages and Slack DMs about other solutions that were not covered. We had shed light on a small corner of the expanding universe of data tools and platforms, yet there was an opportunity to cover even more.

Although we cannot chronicle every additional data tool in just one follow-up post, here we continue our efforts to cultivate this ecosystem by highlighting a few more. The creators of these tools are not only occupying meaningful parts of the ever-evolving modern data stack, they graciously responded to our requests to help us understand where they fit in.

They sound-off here in their own words.

More Tools and Responses

1. Shipyard: Shipyard is a workflow orchestration platform that helps teams quickly launch, monitor, and share data solutions without worrying about infrastructure management. It lets users create reusable blueprints, share data seamlessly between jobs, and run code without any proprietary setup, all while scaling resources dynamically. Shipyard is NOT a no-code tool and does not support data versioning or data visualization.
2. Count: Count is a data notebook that replaces dashboards for reporting and self-service, and supports data transformation. Count is uniquely good at team collaboration, enabling technical and non-technical users to work within the same notebook. Count is NOT a data science notebook.
3. Castor: Castor is uniquely good at organizing information about data to support data discovery, GDPR compliance, and knowledge management. Through a plug-and-play solution, Castor builds a comprehensive and actionable map of all data assets. Castor is NOT a data visualization or BI tool.
4. Census: Census is uniquely good at syncing data models from a warehouse to business tools like Salesforce. It complements existing warehouses, data loaders & transform tools to enable data teams to drive business operations. It is NOT a no-code tool nor does it automagically model your data; it relies on analysts writing models in SQL.
5. Iteratively: Iteratively is a schema registry that helps teams collaborate to define, instrument, and validate their analytics. With Iteratively, you can ship high-quality analytics faster and prevent common data quality & privacy issues that undermine trust. Iteratively is NOT a BI tool, data pipeline, or transformation tool.

data-engineering data-science data

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

Understand how data changes in a fast growing company makes working with data challenging. In the last article, we looked at how users view data and the challenges they face while using data.

Intro to Data Engineering for Data Scientists: An overview of data infrastructure which is frequently asked during interviews

🔵 Intellipaat Data Science with Python course: https://intellipaat.com/python-for-data-science-training/In this Data Science With Python Training video, you...

Understanding how users view data and their pain points when using data. In this article, I would like to share some of the things that I have learnt while managing terabytes of data in a fintech company.