Hi Rajat, I have worked for years in democratizing data quality for some of the top organizations and I'm also an Apache Griffin Contributor and PMC - so I know a lot about this space. :)
Coming back to your original question, there are a lot of data quality options available in the market today and I'm listing down some of my top recommendations with some additional comments, *Proprietary Solutions* - MonteCarlo <https://www.montecarlodata.com/> - Pros: State of the art DQ solution with multiple deployment models, lots of connectors, SOC-2 compliant and handles the complete DQ lifecycle including monitoring and alerting. - Cons: Not open source, cannot be a "completely on-prem solution" - Anomalo <https://www.anomalo.com/> - Pros: One of the best UI for DQ management and operations. - Cons: Same as monte carlo - not open source, cannot be a "completely on-prem solution" - Collibra <https://www.collibra.com/us/en/products/data-quality-and-observability> - Pros: Predominantly a data cataloging solution, Collibra now offers full data governance with its DQ offerings - Cons: in my opinion, connectors can be a little pricey over time with usage. Also the same cons as monte carlo apply to Collibra as well. - IBM Solutions <https://www.ibm.com/in-en/data-quality> - Pros: Lots of offerings in DQ space, comes with a UI, has profiling and other features built in. It's a solution for complete DQ management. - Cons: Proprietary solution which can result in vendor lock in. Customizations and extensions may be difficult. - Informatica Data Quality tool <https://www.informatica.com/in/products/data-quality.html> - Pros: Comes with a UI, has profiling and other features built in. Its a solution for complete DQ management. - Cons: Proprietary solution which can result in vendor lock in. Customizations and extensions may be difficult. *Open Source Solutions* - Great Expectations <https://greatexpectations.io/> - Pros: built for technical users who want to code DQ as per their requirement, easy to extend via code and lots of connectors and "expectations" or checks are available out of the box. Fits nicely in a python environment with or without Pyspark. Can be made to fit in most stacks. - Cons: No UI, no alerting or monitoring. However, see the recommendation section below for more info on how to get around this. - Note: They are coming up with Cloud offering as well in 2023 - Amazon Deequ <https://github.com/awslabs/deequ> - Pros: Actively maintained project that allows technical users to code checks using this project as a base library. Contains profiler, anomaly detection etc. Runs checks using Spark. Pydeequ is available for python users. - Cons: Like great expectations, it's a library not a whole end to end DQ platform. - Apache Griffin <https://github.com/apache/griffin/> - Pros: Aims to be a complete open source DQ platform with support for lots of streaming and batch datasets. Run checks using spark. - Cons: Not actively maintained these days due to lack of contributors. *Recommendation* - Make some choices like below to narrow down the offerings, - Buy or build the solution? - Cloud dominant, mostly On Prem or hybrid? - For technical users, non-technical or hybrid end users? - Automated workflows or manual custom workflows? - For Buy + Cloud dominant + hybrid users + Automation kind of choices my recommendation would be to go with Monte Carlo or Anomalo. Otherwise one of the open source offerings. - For Great Expectations, there is a guide available to push DQ results to the open source Datahub <https://datahubproject.io/> Catalog. This combination vastly extends the reach of great expectations as a tool, you get a UI and for the missing things you can connect with other solutions. This Great Expectations + Datahub combination delivers solid valud and is basically equivalent to a lot of proprietary offerings like Collibra. However this requires some engineering. *Other Notable mentions* - https://www.bigeye.com/ - https://www.soda.io/ Hope this long note clarifies things for you. :) On Thu, 29 Dec 2022 at 10:03, infa elance <infa.ela...@gmail.com> wrote: > You can also look at informatica data quality that runs on spark. Of > course it’s not free but you can sign up for a 30 day free trial. They have > both profiling and prebuilt data quality rules and accelerators. > > Sent from my iPhone > > On Dec 28, 2022, at 10:02 PM, vaquar khan <vaquar.k...@gmail.com> wrote: > > > @ Gourav Sengupta why you are sending unnecessary emails ,if you think > snowflake good plz use it ,here question was different and you are talking > totally different topic. > > Plz respects group guidelines > > > Regards, > Vaquar khan > > On Wed, Dec 28, 2022, 10:29 AM vaquar khan <vaquar.k...@gmail.com> wrote: > >> Here you can find all details , you just need to pass spark dataframe and >> deequ also generate recommendations for rules and you can also write custom >> complex rules. >> >> >> https://aws.amazon.com/blogs/big-data/test-data-quality-at-scale-with-deequ/ >> >> Regards, >> Vaquar khan >> >> On Wed, Dec 28, 2022, 9:40 AM rajat kumar <kumar.rajat20...@gmail.com> >> wrote: >> >>> Thanks for the input folks. >>> >>> Hi Vaquar , >>> >>> I saw that we have various types of checks in GE and Deequ. Could you >>> please suggest what types of check did you use for Metric based columns >>> >>> >>> Regards >>> Rajat >>> >>> On Wed, Dec 28, 2022 at 12:15 PM vaquar khan <vaquar.k...@gmail.com> >>> wrote: >>> >>>> I would suggest Deequ , I have implemented many time easy and >>>> effective. >>>> >>>> >>>> Regards, >>>> Vaquar khan >>>> >>>> On Tue, Dec 27, 2022, 10:30 PM ayan guha <guha.a...@gmail.com> wrote: >>>> >>>>> The way I would approach is to evaluate GE, Deequ (there is a python >>>>> binding called pydeequ) and others like Delta Live tables with >>>>> expectations >>>>> from Data Quality feature perspective. All these tools have their pros and >>>>> cons, and all of them are compatible with spark as a compute engine. >>>>> >>>>> Also, you may want to look at dbt based DQ toolsets if sql is your >>>>> thing. >>>>> >>>>> On Wed, 28 Dec 2022 at 3:14 pm, Sean Owen <sro...@gmail.com> wrote: >>>>> >>>>>> I think this is kind of mixed up. Data warehouses are simple SQL >>>>>> creatures; Spark is (also) a distributed compute framework. Kind of like >>>>>> comparing maybe a web server to Java. >>>>>> Are you thinking of Spark SQL? then I dunno sure you may well find it >>>>>> more complicated, but it's also just a data warehousey SQL surface. >>>>>> >>>>>> But none of that relates to the question of data quality tools. You >>>>>> could use GE with Redshift, or indeed with Spark - are you familiar with >>>>>> it? It's probably one of the most common tools people use with Spark for >>>>>> this in fact. It's just a Python lib at heart and you can apply it with >>>>>> Spark, but _not_ with a data warehouse, so I'm not sure what you're >>>>>> getting >>>>>> at. >>>>>> >>>>>> Deequ is also commonly seen. It's actually built on Spark, so again, >>>>>> confused about this "use Redshift or Snowflake not Spark". >>>>>> >>>>>> On Tue, Dec 27, 2022 at 9:55 PM Gourav Sengupta < >>>>>> gourav.sengu...@gmail.com> wrote: >>>>>> >>>>>>> Hi, >>>>>>> >>>>>>> SPARK is just another querying engine with a lot of hype. >>>>>>> >>>>>>> I would highly suggest using Redshift (storage and compute decoupled >>>>>>> mode) or Snowflake without all this super complicated understanding of >>>>>>> containers/ disk-space, mind numbing variables, rocket science tuning, >>>>>>> hair >>>>>>> splitting failure scenarios, etc. After that try to choose solutions >>>>>>> like >>>>>>> Athena, or Trino/ Presto, and then come to SPARK. >>>>>>> >>>>>>> Try out solutions like "great expectations" if you are looking for >>>>>>> data quality and not entirely sucked into the world of SPARK and want to >>>>>>> keep your options open. >>>>>>> >>>>>>> Dont get me wrong, SPARK used to be great in 2016-2017, but there >>>>>>> are superb alternatives now and the industry, in this recession, should >>>>>>> focus on getting more value for every single dollar they spend. >>>>>>> >>>>>>> Best of luck. >>>>>>> >>>>>>> Regards, >>>>>>> Gourav Sengupta >>>>>>> >>>>>>> On Tue, Dec 27, 2022 at 7:30 PM Mich Talebzadeh < >>>>>>> mich.talebza...@gmail.com> wrote: >>>>>>> >>>>>>>> Well, you need to qualify your statement on data quality. Are you >>>>>>>> talking about data lineage here? >>>>>>>> >>>>>>>> HTH >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> 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, 27 Dec 2022 at 19:25, rajat kumar < >>>>>>>> kumar.rajat20...@gmail.com> wrote: >>>>>>>> >>>>>>>>> Hi Folks >>>>>>>>> Hoping you are doing well, I want to implement data quality to >>>>>>>>> detect issues in data in advance. I have heard about few frameworks >>>>>>>>> like >>>>>>>>> GE/Deequ. Can anyone pls suggest which one is good and how do I get >>>>>>>>> started >>>>>>>>> on it? >>>>>>>>> >>>>>>>>> Regards >>>>>>>>> Rajat >>>>>>>>> >>>>>>>> -- >>>>> Best Regards, >>>>> Ayan Guha >>>>> >>>>
