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The Business Process Management Software (BPMS) market is an extremely crowded one. Some of the players have been around for decades. Camunda is an attempt to bring a modern, open source take to the market in what the author of this article calls a “David vs. Goliath” battle.
Like many new software companies, Camunda is “open core.” Open core software is an attempt to answer the age-old question, “How do you make money selling free software?”
With open core, your central software is standard open source, but you charge for closed-source enterprise features or hosting. If a company is offering both “community” and “enterprise” versions, then it’s probably open core. Some examples of open core companies:
Note that Camunda is extremely Java-centric, so if you don’t use Java, that’s a challenge.
How is Camunda is disrupting the BRMS market with its new open core offering?
The open core business model is not without its criticism. To give you a well-rounded view, we’re including this article that explains why not everyone is a fan of open core and some of the problems that it creates.
Find out why open core is being criticized
Microsoft made over 20 feature announcements for Power BI in the spring. The ones that stand out are that Microsoft reworked KPIs in the form of Goals, reworked Tooltips, mobile optimizations, and paginated reports inside PBI dashboards.
We’re providing a link so you can scroll through to access short descriptions of each feature. You’ll also find other information, such as links to sessions from the Microsoft Business Application Summit that discuss different aspects of Power BI.
See a recap of 2021 Power BI feature announcements
Snowflake announced the ability to connect to the database via Rest APIs. This is a major convenience for developers because they don’t have to worry about drivers or custom libraries. Snowflake is also cleaning up their admin interface and marking some of their programmability extensions as public preview.
Learn what you should know about the new Snowflake features
Feature engineering is one of the most important, yet trickiest tasks in machine learning. In layman’s terms, feature engineering is formatting the data in a way that makes it easier for the ML algorithm to find patterns and relationships.
For example most algorithms can’t do much with a date like “05/06/2021,” but might have more luck with it broken down into year, month, and day of week.
Alteryx’s new offering will create some commonly used features for you. It’s a way to get a jump start on creating features, but a data scientist will still need to curate and add their own.
Find out how to use automated feature engineering in Alteryx Intelligence Suite
