+1 Dr Mich Talebzadeh, Architect | Data Science | Financial Crime | Forensic Analysis | GDPR
view my Linkedin profile <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> On Wed, 9 Apr 2025 at 08:07, Peter Toth <peter.t...@gmail.com> wrote: > +1 > > On Wed, Apr 9, 2025 at 8:51 AM Cheng Pan <pan3...@gmail.com> wrote: > >> +1 (non-binding) >> >> Glad to see Spark SQL extended to streaming use cases. >> >> Thanks, >> Cheng Pan >> >> >> >> On Apr 9, 2025, at 14:43, Anton Okolnychyi <aokolnyc...@gmail.com> wrote: >> >> +1 >> >> вт, 8 квіт. 2025 р. о 23:36 Jacky Lee <qcsd2...@gmail.com> пише: >> >>> +1 I'm delighted that it will be open-sourced, enabling greater >>> integration with Iceberg/Delta to unlock more value. >>> >>> Jungtaek Lim <kabhwan.opensou...@gmail.com> 于2025年4月9日周三 10:47写道： >>> > >>> > +1 looking forward to seeing this make progress! >>> > >>> > On Wed, Apr 9, 2025 at 11:32 AM Yang Jie <yangji...@apache.org> wrote: >>> >> >>> >> +1 >>> >> >>> >> On 2025/04/09 01:07:57 Hyukjin Kwon wrote: >>> >> > +1 >>> >> > >>> >> > I am actually pretty excited to have this. Happy to see this being >>> proposed. >>> >> > >>> >> > On Wed, 9 Apr 2025 at 01:55, Chao Sun <sunc...@apache.org> wrote: >>> >> > >>> >> > > +1. Super excited about this effort! >>> >> > > >>> >> > > On Tue, Apr 8, 2025 at 9:47 AM huaxin gao <huaxin.ga...@gmail.com> >>> wrote: >>> >> > > >>> >> > >> +1 I support this SPIP because it simplifies data pipeline >>> management and >>> >> > >> enhances error detection. >>> >> > >> >>> >> > >> >>> >> > >> On Tue, Apr 8, 2025 at 9:33 AM Dilip Biswal <dkbis...@gmail.com> >>> wrote: >>> >> > >> >>> >> > >>> Excited to see this heading toward open source — materialized >>> views and >>> >> > >>> other features will bring a lot of value. >>> >> > >>> +1 (non-binding) >>> >> > >>> >>> >> > >>> On Mon, Apr 7, 2025 at 10:37 AM Sandy Ryza <sa...@apache.org> >>> wrote: >>> >> > >>> >>> >> > >>>> Hi Khalid – the CLI in the current proposal will need to be >>> built on >>> >> > >>>> top of internal APIs for constructing and launching pipeline >>> executions. >>> >> > >>>> We'll have the option to expose these in the future. >>> >> > >>>> >>> >> > >>>> It would be worthwhile to understand the use cases in more >>> depth before >>> >> > >>>> exposing these, because APIs are one-way doors and can be >>> costly to >>> >> > >>>> maintain. >>> >> > >>>> >>> >> > >>>> On Sat, Apr 5, 2025 at 11:59 PM Khalid Mammadov < >>> >> > >>>> khalidmammad...@gmail.com> wrote: >>> >> > >>>> >>> >> > >>>>> Looks great! >>> >> > >>>>> QQ: will user able to run this pipeline from normal code? >>> I.e. can I >>> >> > >>>>> trigger a pipeline from *driver* code based on some condition >>> etc. or >>> >> > >>>>> it must be executed via separate shell command ? >>> >> > >>>>> As a background Databricks imposes similar limitation where >>> as you >>> >> > >>>>> cannot run normal Spark code and DLT on the same cluster for >>> some reason >>> >> > >>>>> and forces to use two clusters increasing the cost and >>> latency. >>> >> > >>>>> >>> >> > >>>>> On Sat, 5 Apr 2025 at 23:03, Sandy Ryza <sa...@apache.org> >>> wrote: >>> >> > >>>>> >>> >> > >>>>>> Hi all – starting a discussion thread for a SPIP that I've >>> been >>> >> > >>>>>> working on with Chao Sun, Kent Yao, Yuming Wang, and Jie >>> Yang: [JIRA >>> >> > >>>>>> <https://issues.apache.org/jira/browse/SPARK-51727>] [Doc >>> >> > >>>>>> < >>> https://docs.google.com/document/d/1PsSTngFuRVEOvUGzp_25CQL1yfzFHFr02XdMfQ7jOM4/edit?tab=t.0 >>> > >>> >> > >>>>>> ]. >>> >> > >>>>>> >>> >> > >>>>>> The SPIP proposes extending Spark's lazy, declarative >>> execution model >>> >> > >>>>>> beyond single queries, to pipelines that keep multiple >>> datasets up to date. >>> >> > >>>>>> It introduces the ability to compose multiple >>> transformations into a single >>> >> > >>>>>> declarative dataflow graph. >>> >> > >>>>>> >>> >> > >>>>>> Declarative pipelines aim to simplify the development and >>> management >>> >> > >>>>>> of data pipelines, by removing the need for manual >>> orchestration of >>> >> > >>>>>> dependencies and making it possible to catch many errors >>> before any >>> >> > >>>>>> execution steps are launched. >>> >> > >>>>>> >>> >> > >>>>>> Declarative pipelines can include both batch and streaming >>> >> > >>>>>> computations, leveraging Structured Streaming for stream >>> processing and new >>> >> > >>>>>> materialized view syntax for batch processing. Tight >>> integration with Spark >>> >> > >>>>>> SQL's analyzer enables deeper analysis and earlier error >>> detection than is >>> >> > >>>>>> achievable with more generic frameworks. >>> >> > >>>>>> >>> >> > >>>>>> Let us know what you think! >>> >> > >>>>>> >>> >> > >>>>>> >>> >> > >>> >> >>> >> --------------------------------------------------------------------- >>> >> To unsubscribe e-mail: dev-unsubscr...@spark.apache.org >>> >> >>> >>> --------------------------------------------------------------------- >>> To unsubscribe e-mail: dev-unsubscr...@spark.apache.org >>> >>> >>
