It would be interesting if we think about creating a contract validation library written in JSON format. This would ensure a validation mechanism that will rely on this library and could be shared among relevant parties. Will that be a starting point?
HTH Mich Talebzadeh, Lead Solutions Architect/Engineering Lead Palantir Technologies Limited London United Kingdom view my Linkedin profile <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> https://en.everybodywiki.com/Mich_Talebzadeh *Disclaimer:* Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction. On Wed, 14 Jun 2023 at 11:13, Jean-Georges Perrin <j...@jgp.net> wrote: > Hi, > > While I was at PayPal, we open sourced a template of Data Contract, it is > here: https://github.com/paypal/data-contract-template. Companies like GX > (Great Expectations) are interested in using it. > > Spark could read some elements form it pretty easily, like schema > validation, some rules validations. Spark could also generate an embryo of > data contracts… > > —jgp > > > On Jun 13, 2023, at 07:25, Mich Talebzadeh <mich.talebza...@gmail.com> > wrote: > > From my limited understanding of data contracts, there are two factors > that deem necessary. > > > 1. procedure matter > 2. technical matter > > I mean this is nothing new. Some tools like Cloud data fusion can assist > when the procedures are validated. Simply "The process of integrating > multiple data sources to produce more consistent, accurate, and useful > information than that provided by any individual data source.". In the old > time, we had staging tables that were used to clean and prune data from > multiple sources. Nowadays we use the so-called Integration layer. If you > use Spark as an ETL tool, then you have to build this validation yourself. > Case in point, how to map customer_id from one source to customer_no from > another. Legacy systems are full of these anomalies. MDM can help but > requires human intervention which is time consuming. I am not sure the role > of Spark here except being able to read the mapping tables. > > HTH > > Mich Talebzadeh, > Lead Solutions Architect/Engineering Lead > Palantir Technologies Limited > London > United Kingdom > > view my Linkedin profile > <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> > > > https://en.everybodywiki.com/Mich_Talebzadeh > > > *Disclaimer:* Use it at your own risk. Any and all responsibility for any > loss, damage or destruction of data or any other property which may arise > from relying on this email's technical content is explicitly disclaimed. > The author will in no case be liable for any monetary damages arising from > such loss, damage or destruction. > > > > > On Tue, 13 Jun 2023 at 10:01, Phillip Henry <londonjava...@gmail.com> > wrote: > >> Hi, Fokko and Deepak. >> >> The problem with DBT and Great Expectations (and Soda too, I believe) is >> that by the time they find the problem, the error is already in production >> - and fixing production can be a nightmare. >> >> What's more, we've found that nobody ever looks at the data quality >> reports we already generate. >> >> You can, of course, run DBT, GT etc as part of a CI/CD pipeline but it's >> usually against synthetic or at best sampled data (laws like GDPR generally >> stop personal information data being anywhere but prod). >> >> What I'm proposing is something that stops production data ever being >> tainted. >> >> Hi, Elliot. >> >> Nice to see you again (we worked together 20 years ago)! >> >> The problem here is that a schema itself won't protect me (at least as I >> understand your argument). For instance, I have medical records that say >> some of my patients are 999 years old which is clearly ridiculous but their >> age correctly conforms to an integer data type. I have other patients who >> were discharged *before* they were admitted to hospital. I have 28 >> patients out of literally millions who recently attended hospital but were >> discharged on 1/1/1900. As you can imagine, this made the average length of >> stay (a key metric for acute hospitals) much lower than it should have >> been. It only came to light when some average length of stays were >> negative! >> >> In all these cases, the data faithfully adhered to the schema. >> >> Hi, Ryan. >> >> This is an interesting point. There *should* indeed be a human >> connection but often there isn't. For instance, I have a friend who >> complained that his company's Zurich office made a breaking change and was >> not even aware that his London based department existed, never mind >> depended on their data. In large organisations, this is pretty common. >> >> TBH, my proposal doesn't address this particular use case (maybe hooks >> and metastore listeners would...?) But my point remains that although these >> relationships should exist, in a sufficiently large organisation, they >> generally don't. And maybe we can help fix that with code? >> >> Would love to hear further thoughts. >> >> Regards, >> >> Phillip >> >> >> >> >> >> On Tue, Jun 13, 2023 at 8:17 AM Fokko Driesprong <fo...@apache.org> >> wrote: >> >>> Hey Phillip, >>> >>> Thanks for raising this. I like the idea. The question is, should this >>> be implemented in Spark or some other framework? I know that dbt has a >>> fairly >>> extensive way of testing your data >>> <https://www.getdbt.com/product/data-testing/>, and making sure that >>> you can enforce assumptions on the columns. The nice thing about dbt is >>> that it is built from a software engineering perspective, so all the tests >>> (or contracts) are living in version control. Using pull requests you could >>> collaborate on changing the contract and making sure that the change has >>> gotten enough attention before pushing it to production. Hope this helps! >>> >>> Kind regards, >>> Fokko >>> >>> Op di 13 jun 2023 om 04:31 schreef Deepak Sharma <deepakmc...@gmail.com >>> >: >>> >>>> Spark can be used with tools like great expectations as well to >>>> implement the data contracts . >>>> I am not sure though if spark alone can do the data contracts . >>>> I was reading a blog on data mesh and how to glue it together with data >>>> contracts , that’s where I came across this spark and great expectations >>>> mention . >>>> >>>> HTH >>>> >>>> -Deepak >>>> >>>> On Tue, 13 Jun 2023 at 12:48 AM, Elliot West <tea...@gmail.com> wrote: >>>> >>>>> Hi Phillip, >>>>> >>>>> While not as fine-grained as your example, there do exist schema >>>>> systems such as that in Avro that can can evaluate compatible and >>>>> incompatible changes to the schema, from the perspective of the reader, >>>>> writer, or both. This provides some potential degree of enforcement, and >>>>> means to communicate a contract. Interestingly I believe this approach has >>>>> been applied to both JsonSchema and protobuf as part of the Confluent >>>>> Schema registry. >>>>> >>>>> Elliot. >>>>> >>>>> On Mon, 12 Jun 2023 at 12:43, Phillip Henry <londonjava...@gmail.com> >>>>> wrote: >>>>> >>>>>> Hi, folks. >>>>>> >>>>>> There currently seems to be a buzz around "data contracts". From what >>>>>> I can tell, these mainly advocate a cultural solution. But instead, could >>>>>> big data tools be used to enforce these contracts? >>>>>> >>>>>> My questions really are: are there any plans to implement data >>>>>> constraints in Spark (eg, an integer must be between 0 and 100; the date >>>>>> in >>>>>> column X must be before that in column Y)? And if not, is there an >>>>>> appetite >>>>>> for them? >>>>>> >>>>>> Maybe we could associate constraints with schema metadata that are >>>>>> enforced in the implementation of a FileFormatDataWriter? >>>>>> >>>>>> Just throwing it out there and wondering what other people think. >>>>>> It's an area that interests me as it seems that over half my problems at >>>>>> the day job are because of dodgy data. >>>>>> >>>>>> Regards, >>>>>> >>>>>> Phillip >>>>>> >>>>>> >
