Migrating a data warehouse from a legacy environment requires a massive upfront investment in resources and time. There is a lot to consider before and during migration. You may need to replan your data model, use a separate platform for tasks scheduling, and handle changes in the application’s database driver. Therefore, organizations must take a strategic approach to streamline the process. This article presents a step-by-step approach for migrating a data warehouse to the cloud.

It’s no exaggeration to say that today’s internet is built on JavaScript. Around 95% of all websites have been built using the language, according to the latest figures. JavaScript has evolved beyond the client side and is now used to construct entire technology stacks, not to mention support databases like pouchdb and RethinkDB.

The device landscape is as vast as it is complex. With at least 63,000 possible device profiles reported—a number growing at almost 20% per year—the scale of device fragmentation is staggering. New models, operating systems, browsers, screen resolutions, etc., make it extremely difficult for web and app developers to deliver a consistently flawless user experience across all combinations.

Thanksgiving holiday is upon us. For many of our customers, this is one of the most important periods of the year, with more than 189.6 million U.S. shoppers buying up bargains from Thanksgiving day through Cyber Monday last year. For them and for us, it’s crucial that internal systems can handle high traffic volume without downtime or performance degradation.

Implementing a modern, cloud-based analytics stack doesn’t have to be hard — you can do it in three steps, actually. Implementing a modern data stack (MDS) — data integration tool, cloud data warehouse and business intelligence platform — is the best way to establish a successful analytics program as data sources and data volumes multiply.