Data Quality.
With data constantly changing in source and destination databases, how do you understand the quality of your analysis? Is your data integration tooling providing you any data quality guarantees?
All Posts
- March 9, 2021
Introduction Imagine that you’re the analytics team lead at an e-commerce company. Your company has seen rapid growth in the last few months, with thousands of new users who are highly engaged on your website. Your leadership team is pushing for the analytics team to dig into user activity data that has been carefully collected inside your production database to figure out how to make your website’s experience even better. Excitedly, one evening you spin up a data warehouse and set up data connectors to replicate data from your database into your data warehouse. The next morning, you wake
- March 4, 2021
Banish metadata silos, fragmented expertise, and long-term data management complexity.
- August 31, 2020
Data analysts use Datacoral connectors to replicate data from many kinds of data sources (databases, SaaS APIs, file systems, event streams, etc) into the data warehouse of their choice (Redshift, Snowflake or Athena). This allows them to combine, join and transform these different kinds of data to find meaningful insights. However, when connectors are syncing data from different data sources, how can they figure out if the data is being copied over correctly? In this post, we will describe how one might systematically determine the fidelity of the data being replicated in the warehouse instead of just relying on
- July 19, 2019
Building an ETL pipeline can be a significant undertaking, and sometimes it needs to be rebuilt when a better option becomes available. In this episode Aaron Gibralter, director of engineering at Greenhouse, joins Raghu Murthy, founder and CEO of Datacoral,
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