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Modern enterprises go on data point , but move this information around and giving it the right physical body so it can be used in specific program program remains a complex undertaking . Definity , which is launching out of stealth Wednesday and announcing a $ 4.5 million seed support round , want to give these companies the tools to observe , fix and optimise their information line .
The twist here is that unlike many of its competitors , it does n’t only look at the data once it ’s transform and deposited somewhere — at which pointedness is becomes hard to troubleshoot when things go wonky — but while the datum is still in motion .
In an interview , Daniel stressed that the society center on the datum transformation plane on top of a data lake or warehouse , not the data ingestion part of the pipeline . Some of the issue the team experienced during its time working for these large go-ahead include datum quality job bring about by inconsistent datum , schema changes and moth-eaten data point . “ Those are data lineament issues that propagate downstream , ” he say . “ They bear on the commercial enterprise , whether it ’s example that are working on top of bade data now , or dashboards or BI that is broken and all of a sudden , the CFO is like , what ’s going on ? ”
Another problem is data pipelines that simply break , fail and then rerun , as well as word of mouth that have n’t been optimized and finish up cost far more to process than necessary .
“ We met through a reciprocal friend , ” Daniel told me when I enquire him how the founding team first met and decided to tackle this specific issue . “ We all add up from financial services , but in our first coming together , we already realized that we ’re actually fighting and are challenged by the same problem from two side of the coin . And this was the spark , and we think : ‘ Hey , we should do something about that . ’ ”
What make Definity abide out is that it monitors the data in move . This allows it to detect issue right at the reservoir , making it easy to troubleshoot and to optimise these grapevine . It may not be impossible to diagnose the base reason of an issue if all you have is the final result , but it ’s definitely a lot easier when you may look at all of the different step that led to it . This also means that Definity could break off a pipeline from ever running if the input data is corrupted , for example .
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“ Today ’s enterprisingness data leader face a serious pressure to see the reliability of the datum power the business , while increase scale of measurement , cutting costs , and dramatise AI applied science , ” said Nate Meir , a worldwide married person at StageOne Ventures , which lead Definity ’s seed around . “ But without disco biscuit - beam visual sensation into every data software program , information teams are left unsighted and reactionary . Definity is addressing this pauperism head - on with a substitution class - shifting solution that is both potent and unseamed for data engineering science and data platform team . ”
Since the avail practice an agent - based system , it also stays out of the way of the developer who build and maintain these systems . No code alteration are needed , and the agents simply run in railway line with every Python or data app in the pipeline . It ’s worth noting , though , that even for those client who use Definity ’s hosted serve , only metadata is ever remove to its servers .
The funding round was led by StageOne , with participation from Hyde Park Venture Partners and a number of strategical backer investor .
