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We have collected here some useful links to help you find out if data-diff is good.
Check the traffic stats of data-diff on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of data-diff on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of data-diff's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of data-diff on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about data-diff on Reddit. This can help you find out how popualr the product is and what people think about it.
As a data engineer, who is regularly fighting - "these two databases have different SQL dialects" - "did we miss a few rows due to poor transaction-isolation when trying to query recently changed rows on the upstream database" - "is there some checksum of a region of cells that accepts any arrangement of rows and columns that doesn't require me to think about ordering?" ...I've toying with trying to find a way to... - Source: Hacker News / 8 months ago
If the issue happen a lot, there is also: https://github.com/datafold/data-diff That is a nice tool to do it cross database as well. I think it's based on checksum method. - Source: Hacker News / about 2 years ago
First, let me introduce myself. My name is Erez. You may know some of the Python libraries I wrote in the past: Lark, Preql and Data-diff. Source: over 2 years ago
Https://github.com/datafold/data-diff might be worth a look. Source: over 2 years ago
I did data engineering for 6 years and am building a company to automate SQL validation for dbt users. First, by “testing SQL pipelines”, I assume you mean testing changes to SQL code as part of the development workflow? (vs. Monitoring pipelines in production for failures / anomalies). If so: 1 – assertions. Dbt comes with a solid built-in testing framework [1] for expressing assertions such as “this column... - Source: Hacker News / over 2 years ago
Hi HN: We at Datafold are excited to announce a new release of data-diff (https://github.com/datafold/data-diff), an open-source tool that efficiently compares tables within or across a wide range of SQL databases. This release includes a lot of new features, improvements and bugfixes. We released the first version 6 months ago because we believe that diffing data is as fundamental of a capability as diffing code... - Source: Hacker News / almost 3 years ago
For data mismatches, check out data-diff https://github.com/datafold/data-diff. Source: about 3 years ago
Looks useful! Do you have a way to validate that the data was copied correctly and entirely? If not, you might want to consider integrating data-diff for that - https://github.com/datafold/data-diff. - Source: Hacker News / about 3 years ago
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