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Always keen on exploring the ever-changing world of data
Data quality is always evolving, so where is it in 2021? We asked and you answered - here are the results.
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It can be hard to even answer the question "is our data in good shape?" but these teams have gone on a journey towards improved data quality management. Here's how.
Doordash, Truebill, Appfolio, Evidently.ai, and Narrator share valuable insights at the fifth Data Quality Meetup hosted by Datafold.
SOC 2 compliance is a major step on our security journey. Here are some lessons we learned, as well as what Datafold's compliance means for your business.
In July 2021, Datafold co-founder and CEO Gleb Mezhanskiy went on the Data Engineering Podcast to share his thoughts about a proactive approach to data quality management.
If you're looking to build the ideal modern data stack for analytics using only open-source options, this is the blog for you. Find all the best open-source alternatives to your favorite paid tools.
The volume of data streaming in from different sources makes data quality hard to maintain during analysis. A survey of data professionals carried out by Dimensional Research.
Lightdash is an open-source alternative to Looker that natively integrates with dbt. It may not be as mature as other open-source products like Metabase, Querybook, or Superset, but it is different in a few essential ways.
Learn what steps your team needs to take to improve data quality and get the most out of your data.
Lyft vs. Shopify in testing ETL at scale, using fake data to align your stakeholders, and how to avoid nuclear meltdowns in your data platform.
Good Data: How Spotify, Shopify & Lyft approach data quality
Why implement regression testing for ETL code changes, how to align data producers and consumers, and what Data teams at Carta, Thumbtack, Shopify & Clari do to solve data quality.
Take your ETL workflow to the next level with Datafold and dbt integration that automates data testing and provides column-level data lineage
The more people that are looking at the data, and the more apps that are using the data, the faster data quality issues will be identified and resolved.
On the second Data Quality Meetup, we discussed three types of data testing and when to apply them, new-generation ETL frameworks and ROI of open-source data catalogs.
Over the past 10 years, we've seen a great advancement in technologies and tools for analytics and machine learning: with today’s modern analytics stack, we have fast and scalable data warehouses, dirt-cheap data storage, capable ETL orchestrators, and powerful BI tools.
Unlocking the next level with most popular ETL orchestrator
Put a comma in the right place
Objective criteria and subjective advice when choosing a data warehouse for analytics.
Picking tools for every step in the data flow
