intermix Blog

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

Data Products: Stephen Bronstein @ Fuze

1 min READ
November 21st 2019
“Used by over 300 people”This is part of a series of interviews on how companies are building data products. In these interviews, we’re sharing how data teams use data, with a deep dive into a data product at the company. We also cover tech stacks, best practices and other lessons learned.AboutS […]
Lars Kamp Lars Kamp

Data Products: How Envoy’s Customer Success Team Grows Account Penetration and CLV

6 min READ
November 21st 2019
SummaryArvind Ramesh is the manager of the data team at Envoy. In this post, we’re sharing how Arvind’s team has built a data platform at Envoy, with a deep dive into a data product for the Customer Success Team. Arvind believes that the most important skill in data is storytelling and that data teams s […]
Lars Kamp Lars Kamp

Modern ETL Tools for Amazon Redshift

6 min READ
November 13th 2019
Amazon Redshift was introduced in 2012 as the first Cloud Data Warehouse. It quickly became the fastest-growing service among Amazon Web Services. Amazon Redshift and these ETL tools forever changed the methods of working with analytical data. The focus shifted from the classical ETL approach to ELT. Let's […]
Igor Bobriakov Igor Bobriakov

Redshift's Automatic WLM with Query Priority: A First Look at Performance

5 min READ
October 11th 2019
IntroductionThe AWS team recently released a new feature of their Automatic WLM that we're really excited about: Query Priorities. This feature aims to address the main limitation of Auto WLM head-on, i.e. it allows you to prioritize some queries over other queries. This is a crucial performance enhance […]
Stefan Gromoll Stefan Gromoll

How Mode and intermix.io Deliver Fast Queries on Amazon Redshift

3 min READ
October 8th 2019
The New Data StackIn the past few years, we’ve seen a rapid shift to a “new” or “modern” analytics stack, built around cloud warehouses like Amazon Redshift, BigQuery and Snowflake. This stack has three layers:A data integration layer that ingests raw data from the source into a staging area in the […]
Lars Kamp Lars Kamp
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