Data Engineering with Datameer

Some of the challenges Data Engineering managers face are that there aren’t enough Data Engineers to meet the current demands from business and the lack of reuse of code. We are looking for a project within Datameer, and an example use-case of a data engineer tasked to pivot on some data.

So here, we can use Datameer’s point and click transformations to do various filters and data extractions as part of the discovery process. And we can see Datameer’s interactive workbench in play. And also, we can combine this with inserting SQL; it’s a combination.

So we can enter a new SQL view, and here we can put in a query to do some various filters and some joins. And then see the preview of that and the available columns and the outputs. It is a very visual interactive workbench.

We also keep track of the lineage flow, so it’s effortless to visually see which steps we’ve taken and how to combine various code and non-code transformations.

Some things are just simply easier to do with point-and-click. So the delivery mechanism will be this pivot table that I’m constructing. Here is Datameer’s interactive workbench. You see that it’s very easy to create a new pivot, something that is not super straightforward when writing SQL.

Here we have our pivot table constructed, and once we have that constructed within the design environment, we can then publish that to Snowflake – as a view or as a table.

We can look on the Snowflake site that all that code that we didn’t have to write is there.

We can also consume that with various BI tools.

We have taken a quick look at how we can use Datameer from a Data Engineering persona and the combination of point-and-click and SQL to deliver and turn around use cases much faster.

Learn more about Data Engineering with Datameer by requesting a free demo today!