Systems | Development | Analytics | API | Testing

Hiding SOAP Legacy Applications Using the Mullet Pattern

In this episode of Kongcast, I had the pleasure of speaking with Aaron Weikle, the founder and CEO at MS3, about supporting legacy-based applications as companies add the next generation of microservices. Check out the transcript and video from our conversation below, and be sure to subscribe to get email alerts for the latest new episodes.

The road less traveled on the path to our $4.2B valuation

As we announce to the world today that ThoughtSpot is now worth $4.2B, I am overwhelmed with gratitude. I am grateful to the customers who had the courage to challenge the status quo and give us a shot inside their companies. I am equally grateful to the entrepreneurs and investors who had the courage to join us and build a company that is changing what's possible with data. Often, people say it's hard to be courageous in the face of adversity. I say that’s conformist bullshit.

Vanity Metrics for APIs vs Tracking Business Value From API Transactions

As an API product manager, you want your API to have a great developer experience. This means that developers can get up and running quickly, they get consistent behavior from your API, it’s easy for them to troubleshoot any errors they encounter, and your API makes it easy for them to address their business needs. Tracking your APIs is an important part of understanding how well they perform, which leads most organizations to build out their own internal API tracking systems.

How to Generate a Snowflake API

Customer demand is in a constant state of fluctuation. Companies must keep pace or risk losing their position in a crowded market. Digital transformation is the driver to helping companies remain agile to meet their customers’ needs. The Snowflake data warehouse provides massive storage capabilities to facilitate combining figures from disparate systems. These figures help inform decision-making and provide valuable insight into driving business strategies.

Paving the way for "Citizen Analysts" to drive healthier business decisions

Business intelligence (BI) has gotten so sophisticated that a variety of end users within an organization may be eager to use data to guide their decisions. Unfortunately, most businesses have a very small data analysis or BI team. How can companies like this enable more people to use more data more effectively without overwhelming their BI staff?