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intermix Blog

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

Zero Downtime Elasticsearch Migrations

6 min READ
July 12th 2018
Introduction At intermix.io, Elasticsearch is a final destination for data that is processed through our data pipeline. Data gets loaded from Amazon Redshift into Elasticsearch via an indexing job. Elasticsearch data then gets served to the intermix.io dashboard to data engineers, giving them a view of the performance and health of their own data pipelines. […]
Stefan Gromoll Stefan Gromoll

3 Steps for Fixing Slow Looker Dashboards with Amazon Redshift

6 min READ
July 10th 2018
Looker is a powerful tool for self-service analytics. A lot of of companies use Looker on top of Amazon Redshift for business intelligence. It helps companies derive value from their data by making it it easy to create custom reports and dashboards. “Slow Looker dashboards” is one of the most frequent issues we hear with […]
Lars Kamp Lars Kamp

Have Your Postgres Cake with Amazon Redshift and eat it, too.

7 min READ
July 6th 2018
Introduction At intermix.io, we use Amazon Redshift as part of our stack. Amazon Redshift is an OLAP database, and a valuable tool for data teams due to its low cost and speed for analytical queries. We have a particular use case though. We’re using Amazon Redshift in an OLTP scenario, i.e. we’ve built an analytical […]
Luke Gotszling Luke Gotszling

Why We Built intermix.io - “APM for Data”

5 min READ
June 27th 2018
To win in today’s market, companies must build core competencies in advancing their use of data. Data-first companies are dominating their industries. e.g. Netflix vs network TV; Tinder vs Match.com; Stitch Fix vs The Mall. Mentions of “AI” are often heard in advertisements, product launches, and earnings calls. Why is this happening now? The cloud […]
Paul Lappas Paul Lappas

Announcing App Tracing - Monitoring Your Data Apps With intermix.io

2 min READ
June 27th 2018
App Tracing surfaces important information about how apps & users interact with your data. It can help answer questions like: which user is responsible for this spike in concurrency? who is the most “expensive” Looker user? what is the average latency of a dashboard or model? Of all dashboards executed by a particular user? my […]
Paul Lappas Paul Lappas
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