As we covered in part 1 of this blog series, Snowflake’s platform is architecturally different from almost every traditional database system and cloud data warehouse. Snowflake has completely separate compute and storage, and both tiers of the platform are near instantly elastic. The need to do advanced resource planning, agonize over workload schedules, and prevent new workloads on the system due to the fear of disk and CPU limitations just go away with Snowflake.

Today SEO is much more than just finding high converting keywords for better ranking. Most marketers and content writers nowadays rely on different strategies to stay in the game. Imagine handling such intricate tasks manually or shuffling through several tools daily to get this done. Sounds hectic, right? But what if we told you, there’s a single package out there to make your work easier.

Let’s precisely define the different kinds of data repositories to understand which ones meet your business needs. October 29, 2020 A data repository serves as a centralized location to combine data from a variety of sources and provides users with a platform to perform analytical tasks. There are several kinds of data repositories, each with distinct characteristics and intended use cases. Let’s discuss the peculiarities and uses of data warehouses, data marts and data lakes.

With the massive explosion of data across the enterprise — both structured and unstructured from existing sources and new innovations such as streaming and IoT — businesses have needed to find creative ways of managing their increasingly complex data lifecycle to speed time to insight.

As a product feature for your app, embedded analytics is undoubtedly a valuable tool. But historically, many product managers and software developers have approached it as a standalone capability. This has led to dashboards and reporting modules added as an afterthought, rather than as a founding strategic component of the core application.

We’ve all heard the war stories born out of wrong data: These stories don’t just make you and your company look like fools, they also cause great economic damages. And the more your enterprise relies on data, the greater the potential for harm. Here, we take a look at what data quality is and how the entire data quality management process can be improved.