The Latest from Silectis
How to Approach Your Data Engineering Transformation
Should you build your own tooling, take a “best of breed” approach, or buy a turnkey data engineering platform? We’ve got you covered. Data Engineering Platforms: Build, Best of Breed, or Buy? Every company wants to be data-driven. Modern organizations that thrive based on data have a common strength: a solid data engineering practice. How…
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Setting Up Your Data Engineering Team for Success: Building Future-Proof Data Architecture
Yesterday’s data is predictable and easy. Our challenge as data engineers is getting ready for tomorrow’s unpredictable new data. With the push toward more flexible solutions, let’s discuss how you can set up your data engineering team for success by…
Tutorial: Creating a Multi-Cloud VPN with Terraform between AWS, GCP, and Azure
At Silectis, we deploy Magpie clusters across AWS, Google Cloud, and Azure. But because some of our internal infrastructure resides only on AWS, we need to establish private connections between these environments so that clusters on Google Cloud and Azure…
Improve Your SQL Skills: Master the Gaps and Islands Problem
The “gaps and islands” problem is a scenario in which you need to identify groups of continuous data (“islands”) and groups where the data is missing (“gaps”) across a particular sequence. The problem may sound arcane at first glance but…
Magpie Supports Google Cloud Platform
MAGPIE IS THE EASIEST WAY TO SETUP YOUR DATA LAKE ON GOOGLE CLOUD PLATFORM Today, Silectis is excited to announce our data engineering platform, Magpie, supports Google Cloud. Silectis Magpie brings together Google Cloud’s Big Data components so that organizations…
How to Implement a Data Lake with Apache Airflow and Silectis Magpie
Let’s face it, operating in a data-driven environment is hard. Teams, even small ones, can generate a painfully large number of batch processes that need to run on schedules. Drag-and-drop ETL tools become a maze of dependencies as business logic…
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