Kong Summit has come and gone! Whether you joined us in San Francisco or missed this year’s big shindig, here are some of the highlights from the event and a round-up of all the Kong news dropped over the past two days. This post will continue to be updated with more details and links to videos from sessions as they’re available.

This is a guest post with exclusive content by Bill Inmon. Bill “is an American computer scientist recognized by many as the father of the data warehouse. Inmon wrote the first book, held the first conference, wrote the first column in a magazine, and was the first to offer classes in data warehousing.” -Wikipedia. The five critical considerations for corporate loyalty.

Modern digital users have the patience level of a typical 5-year-old. Apps that incorporate software testing tools have one core objective to meet: not to make the first encounter with a glitchy UI and unusable functionalities. Software quality in software engineering is a general expectation business stakeholders have. Unfortunately, testing is also “sacrifice” for time to market, postponing the release date and rejecting builds last minute in the development process.

Financial services innovation continues to progress at a breakneck pace. For example, fintech developers can programmatically spin up accounts, move money, and issue and manage cards with Increase or embed financial services into their marketplace with Stripe – capabilities that were unimaginable just a few years ago.

While the word “data” has been common since the 1940s, managing data’s growth, current use, and regulation is a relatively new frontier. Governments and enterprises are working hard today to figure out the structures and regulations needed around data collection and use. According to Gartner, by 2023 65% of the world’s population will have their personal data covered under modern privacy regulations.

The Extract, Transform, and Load process (ETL for short) is a set of procedures in the data pipeline. It collects raw data from its sources (extracts), cleans and aggregates data (transforms) and saves the data to a database or data warehouse (loads), where it is ready to be analyzed. A well-engineered ETL process provides true business value and benefits such as: Novel business insights. The entire ETL process brings structure to your company’s information.

Star schemas and snowflake schemas are the two predominant types of data warehouse schemas. A data warehouse schema refers to the shape your data takes - how you structure your tables and their mutual relationships within a database or data warehouse. Since the primary purpose of a data warehouse (and other Online Analytical Processing (OLAP) databases) is to provide a centralized view of all the enterprise data for analytics, data warehouse schemas help us achieve superior analytic results.

“What should you and your business focus on when trying to create better customer journeys and beat competition?” That was the question we asked Countly data captains (also known as Countly customers) when trying to determine how well they collect customer experience metrics and how well they were using that data to make data-driven decisions.

Software testability measures how simple it is to test both the system as a whole and each individual component. Testability might mean different things for different members of the software development lifecycle. Remember how deeply frustrating it was to rewrite dozens of UI tests when a front-end developer made a small change that broke all the locators? Or the several restless nights spent fixing minor performance issues that aren't reproducible on the local machine?