OpenAI DevDay 2023 is upon us. We're excited by what some of the announcements mean for Continual and will be revealing more about Continual soon. In the meantime, you can watch the livestream and replay here: There's never been a better time to build an AI copilot for your application.

In 2023, data analysts have access to more data than at any other time in history. Experts believe the amount of data generated in 2023 totaled 120 zettabytes, and humans will create around 463 exabytes every day by 2025. That's an unimaginable volume of data! All this data, however, is worthless unless you can process it, analyze it, and find insights hidden within it. Data pipelines help you do that.

Data warehouses are now critical to efficiently utilizing data to derive deep insights. The big question is, which data warehouse is best for my business? There are three data warehouse giants that you need to consider: Amazon Redshift, Google BigQuery, and Snowflake.

As data becomes an increasingly important asset for organizations, ensuring its quality and reliability is critical. Data observability is a practice that helps organizations maintain visibility into their data pipelines and quickly identify and resolve any issues that arise.

In an era defined by rapid technological advancements and increasing consumer expectations, the automotive industry faces unprecedented challenges and opportunities. Vehicle manufacturers must constantly innovate to remain competitive, and one indispensable tool in their arsenal is product analytics. This article explores compelling reasons why product analytics is essential for the automotive sector and suggests some additional considerations. So let's dive in!

When running automated tests frequently on your website, at one point it may be essential to keep your website statistics consistent with correct visitor counts, conversions, and geo-location data. The impact of such skewed data from automation can lead to pricy mistakes for incorrect ad targeting and the business economy statistics, hence it can be important to exclude test automation from analytics data.

Imagine looking at a bland spreadsheet filled with hundreds of columns containing nothing but some raw numbers… Be honest – how well would you understand the data presented in front of you? Even if you could, it’ll probably take some time until you connect the dots of how everything relates to one another. And on another note, not everyone in your business will be as data-savvy as you are. So, how can we fix this and make data understandable to all of the key members?