Posts Tagged data-warehouse

Integrating various infrastructure data sources into your data warehouse has long been a challenge for Platform Teams. Whether it’s dealing with multiple API endpoints, managing complex authentication processes, or just trying to get a consistent, reliable data feed, the process can be daunting and time-consuming. Especially when you factor in the various cloud providers, and the inconsistency in data formats across them all.

These pain points can slow down your ability to get actionable insights from your infrastructure data, leaving you with more questions than answers.

The tap-pulumi-cloud connector, announced today, is designed to address these challenges head-on by offering a simple solution for automating the process of accessing infrastructure data.

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Danny Zalkind is the Senior Director of Infrastructure Engineering for Skai, an award-winning intelligent marketing platform. He brings his 15 years of experience of managing tech teams to his current role where he’s dedicated to allow Skai R&D to efficiently produce and serve software. You can find him on Linkedin.

As Skai continues its journey towards fully migrating to the cloud using Pulumi, we’ve taken another large bite out of the migration pie, moving our most critical data to AWS on top of Amazon Keyspaces, an Apache Cassandra–compatible database service.

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In our last episode, Deploying a Data Warehouse with Pulumi and Amazon Redshift, we covered using Pulumi to load unstructured data from Amazon S3 into an Amazon Redshift cluster. That went well, but you may recall that at the end of that post, we were left with a few unanswered questions:

• How do we avoid importing and processing the same data twice?
• How can we transform the data during the ingestion process?
• What are our options for loading data automatically — for example, on a regular schedule?

These are the kinds of questions you’ll almost always have when setting up a data-processing (or ETL) pipeline — and every platform tends to answer them a little differently.

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It’s fun to think about how much data there is swirling around in the global datasphere these days. However you choose to measure it (and there are various ways), it’s a quantity so massive — hundreds of zettabytes, by some estimates — that it’s kind of a hard thing to quite get your head around.

If you could convert all the world’s data into droplets of water, for instance, at one megabyte per drop, you’d have enough 1MB drops to fill two more Lake Washingtons. If you could store all that data on 3.5" floppies, you’d need more than a hundred quadrillion floppies to capture it all — enough to cover the planet entirely (with much room for overlap) or to pave a nice bridge for yourself from your front porch well into interstellar space. If you could pull all that data into an HD movie, and you sat down to start watching that movie 2.5 million years ago (with your favorite saber-toothed friend, say), you’d still be watching the same movie today.

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