DataHub

“When we recently used this feature to evaluate a family of tables that had historically been used by our data science and product management teams, we found that as many as 40% of them, amounting to approximately 100 million rows, were no longer needed.”

““Before bringing DataHub on board, MYOB's data teams would see multiple breaking changes per week. Since integrating DataHub into our workflow about a year ago, even though our overall usage of Snowflake has gone up 4 times, DataHub has helped us significantly reduce the number of breaking changes, to the extent that they are no longer a burden on all teams.””

““We believe the customer journey from really exploring, understanding, and then finally getting access to data should have one entry point.””

““DataHub aligns really well with our mission statement. So, what we wanted to offer to our stakeholders, our customers, within the data area is really flexibility and the ability to customize solutions for them.””

““Analytics accuracy is not a code generation problem. LLMs are good already at writing SQL. The problem is context and routing. You give them enough business context, and you give them the table they should use, and they can write the SQL properly.””

“Some people want to explore data broadly… Some people can say, I want only data products or semantic views that are well documented, and this gives us a higher than 90% level of accuracy.”

“When we started analyzing why zombie evals were failing, one of the most common reasons was because it was getting the information from the raw table that was not ready.”

““AI assertions were creating a lot of noise, and it was relatively easy to have something misplaced. We have a business in a certain market that’s relatively minor compared to other markets. A data pipeline for it could go bust, feeding into the main fact table, and we would not know because an AI assertion would assume that this less-than-1% downscale was a normal business cycle.””

““We want everyone in the product part of the company to use it on a regular basis. Wherever there’s a data question DataHub Cloud can answer — lineage, where does something come from, who owns it, how often is it processed, are there any outstanding DQ issues — we’ll expect them to use it and not depend on the manual Q&A.””

““Because it was detected basically immediately, the on-call data engineer proceeded to fix it, and in the process discovered an underlying issue with another part of the data ingestion pipeline that would not have been a quick fix otherwise.””

““It is now seconds for people to see the definition of the KPI figure they’re looking at and who owns it — a process that prior to DataHub took orders of magnitude more time.””

““DataHub Cloud overall had the best results in our data quality, lineage, and catalog PoC. They’ve also simply had the best team supporting us throughout that process, both from classical sales but more importantly, from the engineering perspective. All of our questions have been promptly answered, with a handful of them resulting in subsequent platform tweaks and updates.””

““Since launch, the overall organisation grew significantly while the number of questions about 'what does this table do', 'who owns this figure', or 'what is this column used for' dropped significantly.””

““The DataHub community is really extraordinary. And I have to be honest, I'm not trying to sell this to anyone, but it's really one of a kind in my opinion.””

“I begged my colleague, our team lead for the platform, and got a DevOps engineer to help me out to deploy it and probably spent like a month on that thing and got absolutely nowhere.”

“We knew basically at that point already that we needed something to keep track of our metadata. And lineage was maybe the thing I was most interested in.”

““This tool is so flexible. The data model itself is extensible, which I love. It’s based on a graph network, which is the way metadata should be represented. It is, to me, just mind-blowingly good.””

““All I have to do is inject the data as we have it either through recipes that already exist or my own custom data that I’m injecting. We can set up governance policies to help ease the burden on data engineering, empower our users, really do important things for them.””

““It was easy to deploy, and everything just worked… We were able to do in three days what we had trouble doing for those entire six years.””

“Where I think things get complicated is that there’s this data context layer on top of [the stack]… we don’t often see that. If we just get our system architecture right, it’s just gonna magically come together. But really, there’s a lot of complexity.”

““We looked around for a data catalog tool, and DataHub was a clear winner. We created a small POC to evaluate its capabilities and saw a huge potential for it to be much more than a data catalog.””

“We needed a tool to help us know our data better and answer questions, such as, ‘Where does the data from this report come from?’ ‘What experiment will be affected if I change my dataset?’ ‘When was this dataset last updated?’ ‘What are the dependencies between datasets and jobs?”

““DataHub Cloud is such a wonderful tool for lineage, especially since it can track through multiple hops – a few steps up or a few steps below. It’s the easiest place for us to see lineage.””

““DataHub feels fairly intuitive. I learned it all without needing documentation. It’s a good product.””

““We rely on DataHub to gain insights and ensure our critical data is reliable. DataHub’s managed product takes DataHub to the next level through automation and emphasis on time-to-value.””