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intermix Blog

Best practices and lessons learned for cloud ETL and data engineering.

Amazon Redshift Concurrency Scaling - A Guide and Our Test Results

4 min READ
April 3rd 2019
Summary The Amazon Redshift architecture allows to scale up by adding more nodes to a cluster. That can cause over-provisioning of nodes to address peak query volume. Unlike adding nodes, Concurrency Scaling adds more query processing power on an as-needed basis.  The typical Concurrency Scaling for Amazon Redshift gives Redshift clusters additional capacity to handle […]
Stefan Gromoll Stefan Gromoll

How We Use AWS IAM to Generate Temporary Amazon Redshift Passwords

5 min READ
February 27th 2019
At, we run a fleet of over ten Amazon Redshift clusters. In this post, I’ll describe how we use IAM authentication for our database users. By using IAM credentials, we can enforce security policies on users and passwords. It’s a scalable and secure way to manage access to your cluster(s). The approach we describe […]
Christopher MacGown Christopher MacGown

A DBA’s Guide to the Amazon Redshift Architecture

7 min READ
February 23rd 2019
In this post, we’ll lay out the 5 major components of Amazon Redshift’s architecture. Data applications Clusters Leader nodes Compute nodes Redshift Spectrum Understanding the components and how they work is fundamental for building a data platform with Redshift. In the post, we’ll provide tips and references to best practices for each component. ————- Since […]
Lars Kamp Lars Kamp

Announcing Query Insights

3 min READ
January 28th 2019
The number one driver of change for our customers is that they are experiencing huge growth in data and query volume. We observe three main drivers behind that growth: Growth in the number of data sources connected to a Redshift cluster and the volume of data coming from those sources. We observe a data growth […]
Paul Lappas Paul Lappas

14 Examples of Data Pipelines Built with Amazon Redshift

14 min READ
January 17th 2019
At, we work with companies that build data pipelines and data lakes in the cloud. Some start “cloud-native”, others migrate from on-premise solutions like Oracle or Teradata. What they all have in common though is the one question they ask us at the very beginning: “How do other companies build their data pipelines?” And […]
Mike Pavloski Mike Pavloski
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