When requirements change for your product, there arises a need to change not only the codebase but also the existing data that already lives in production. If you’re performing the changes locally, the whole process seems fairly simple. You test your new feature against a sparkling clean database, the test suite is green, and the feature looks great. Then you deploy, and everything goes to hell because you forgot that production was in a slightly different state.

Have you ever been neck-deep building a new feature? You're working at capacity. You need to test something out so you paste an API key into your source file with every intention of removing it later. But you forget. You push to GitHub. It's an easy mistake, and potentially a very expensive one. In this article, Julien Cretel explores the nuances of this kind of data leak, offers suggestions for recovery when leaks happen and gives us options for preventing them in the first place.

Deciding whether to hire and build an API analytics platform vs purchasing from a third party vendor can be a daunting task. Not only do you need to investigate ROI, you also have to navigate politics and may run into Not Invented Here syndrome, among other things. In the long run, by purchasing a ready made solution like Moesif, your product and engineering teams will be able to focus on what they do best: building products that customers love.

The PHP INI setting memory_limit is important to configure right for two reasons. To help you finding the right memory_limit setting Tideways now offers two additional ways to see the maximum memory in use.

Ongoing reports of the “Retail Apocalypse” were fueled once again in 2019 with more than a dozen well-known retail brands closing their doors forever. On the flip side, a “Retail Renaissance” is well underway – and signs indicate that retail leaders that have already invested in their digital transformation journey will continue to reap rewards well into the future.

Last week it was announced that Yellowfin jumped to Visionary in the Gartner 2020 Magic Quadrant for Analytics and BI Platforms. Toot toot! This is a massive achievement for the entire Yellowfin team putting us in the same room as products by the biggest players on the planet. What makes it so special is that we are a privately funded, Melbourne based organisation smashing it against huge, cashed-up tech giants.

Fresh off a new funding round which includes strategic cloud partner Microsoft, Databricks continues to make huge strides in its mission to ease Spark complexity and simplify analytics through its Unified Analytics Platform. Databricks has also graduated from “visionary” to “leader” in the latest Gartner Magic Quadrant for Data Science and Machine Learning Platforms in 2020.

We are excited to welcome the first release of 2020 which provides greater value through enhanced capabilities across our data analytics platform.