Redshift is Amazon Web Services’ data warehousing solution. They’ve extended PostgreSQL to better suit large datasets used for analysis. When you hear about this kind of technology as a Python developer, it just makes sense to then unleash Pandas on it. So let’s have a look to see how we can analyze data in Redshift using a Pandas script! Setting up Redshift If you haven’t used Redshift before, you should be able to get the cluster up for free for 2 months. As long as you make sure that you don’t use more than 1 instance, and you use the smallest available instance. To play around, let’s use

If you’re following our recent news, you already know that last week we released the new massive IntelliJ IDEA update, 2017.2. However, we didn’t mention its database tools much in the release announcement, so now it’s time to catch up on that, because there’s plenty of new features in that area. Amazon Redshift and Microsoft Azure These cloud database providers are now supported out of the box with dedicated drivers and their specific SQL syntaxes. If you’re already using any of them via the standard SQL Server or PostgreSQL drivers, make sure that you migrate to the new dedicate

Hello! We are thankful to the Amazon Redshift users for reporting the issue with DataGrip not showing the objects contained in schemas in the Database view - the problem has been fixed and we are delivering the new minor update as soon as we can. Other important fixes of this update are: Now DataGrip shows the correct number of rows if the PageSize parameter is set to 0. DBE-3076 Exporting the result-set affects the whole set, not only one page. DBE-3091 Fixed bug in comparing schemas with different names. DBE-2328 The Flip intention action (did you know about it, by the