@ashoklodha1916
If are already a data guy skip to @18:30. Example at 38:06
@duncanreid1977
Excellent presentation and explanation for why a shift in how we manage data (and data architectural thinking) is needed. Powerful stuff. Thank you.
@frudev
Excellent talk. This is a powerful paradigm shift
@ThanosVassilakis
A really nice presentation from Zhamak Dehghani with lots of great points but no "Paradigm Shift" more like "Atomic Clock Drift"
@dietzmn8264
She is declaring the crisis of Data without prove. Although its an interesting concept, and I'm open to it, the Kuhn's paradigmen-shift for solving the crisis of data has a longer history. Just recently the crises of data was solved by introducing the datalake, before that the crisis of data was solved by the datawarehouse. Will we, like in our beloved JScript "Ecosystem", get every year a new revolutionary framework? Let's see . But give Data Mesh a try :-)
@jnktech7752
excellent explanation
@SoMahn
this is easier said than done
@AbdulrahmanAlQallaf
I think the key idea here is to have a domain/product owner team that owns an area end to end and their KPI is to keep their customers happy. We should not have an explosion of copies of data like in her approach. A data warehouse/lake is still sufficient, what is needed is a "meta" paradigm shift, not an architectural one.
@AjaxsonXX
Inspiring talk.. Thanks!
@florcinha1234
Love you <3
@AbodhaAanand
Thank you! Few interesting take away from this sessions. Thiis boils down to two important things why you are doing what you are doing ! I am not advocating a pipeline first approach ever because that is the how and not the why! How can be spelt out, tech stack can be defined. Scale, governance and other requirements can be addressed. In my view, we cannot opt for a completely data decentralised domain orientation as well as we have seen historically with our journey with data Marts and looking at both top down and bottom up approach to data warehouses. We need a common data model to address certain business requirements and have a single version of truth across the enterprise for specific business problems. So a mix of current and proposed architecture pardigmn will prove relevant as we continue to evolve in our data journey and continue to innovate at scale.
@lawrencefernandes3311
I'd really like to know where is the improvement from Data Warehouses to Lakes, at most they are complementary! And the new trend is the Data Lakehouse approach, wich is actually quite promissing: "Big Data" technology applied with the proven concepts and methods of Data Warehousing. I'm not sure about the data mesh, to me it sounds too good to be true ...
@anttipikkusaari4855
For Data Excellence - that is, for Scale, Speed, Agility, Quality and Value - the legacy got to go. It's time for paradigm shift. Seems that Data Mesh is the one that can deliver what we need. Strong Buy.
@cyclogenisis
Starts at Data Mesh starts @ 18:10
@srikanthjnr
Aren't these the very basics ?
@fabiodesalles2732
I liked it as a new voice into the debate but as far as what she has shown, Data Mesh is not a real need since most of the problems she highlights have not been a problem for quite some time - about a decade or more. The way she reports about BI/DW situation sounds more like she hasn't been in touch with the field for a couple of decades and do not know how things are really working today. There are a LOT of failed projects around (Gartner ranges it into 70-80%) but most of those failures stem from bad choices and bad management, and not lack of tech options. Those problems are not solved by some word shuffling but rather with hard and steady work with people, not technology. Just to make it clearer: DW/BI project problems are with people, not technology and have been so for at least more than a decade (well before Hadoop, for instance.)
@minma02262
What is risk for "over-meshing"?
@imadyoubiidrissi85
Would anyone have a comprehensive "digest" of the evolution of operational & OLTP architectures that evolved from monolithic to micro-services' oriented? The DevOps evolution timeline would be awesome aswell, I'd like to compare them with the evolution of analytics data oriented architectures.
@JavidKagzi-jt1me
Is the data mesh not a siloed approach? Just saying
@TheAgb01