Відео

By far the best introduction to Fabric I have seen. Cuts out the marketing fluff and covers real design insights.

Hi James, Thanks for this video, it complies well with my thoughts. FYI I had or have all 3 Mesh Types in different productions. Mesh Type 1 uniformity and centralization is good to start fast from scratch as a Data Mesh core. Mesh Type 2 distribution and its domain and storage total uniformity are too optimistic for real life and have limited application. Mesh Type 3 is too agile and diverse, so it may introduce a high total cost of ownership if it is selected as a core for Data Mesh, however, if there is a hybrid cloud, 3rd party SaaS API, a custom HPC cluster, a "good enough" legacy system, or security/privacy constraints IMHO it is reasonable to use Mesh Type 1 as a core and combine it with Mesh Type 3 plugins, where each Mesh Type 3 plugin mimics Mesh Type 1 approach for its input and output data products. I prefer using Mesh Type 1 + 3 as it offers centralized control, cost-efficiency, and fast development. This is achieved by utilizing the Mesh Type 1 core as a default and having the flexibility to extend the core with diverse Mesh Type 3 domain plugins, giving us agile capability.

Great information on this video. I have seen so many other channel and video saying bla bla about the Fabric but this video looks the most informative. guys like and subscribe for this effort

ua-cam.com/video/VYmjJe2gR1A/v-deo.html Unity Catalog in Databricks has solved the RLS and Column Masking. A very impressive and interesting video. For me it served a good recap :) Cheers Thank You

I work as a data engineer in Snapp Group in Iran. I recently read your book and I want to thank you for your wonderful book. The contents of your book were very close to my experiences in recent years and I enjoyed the events.

Great video, finally understand what this

End of the day, new warehouse FEELS almost like the serverless sql endpoint with the ability to also now write data with T-SQL in a DEFAULT DELTA format + the Polaris engine + the ability to use Snowflake like cloning to make test environments a lot easier in the same way Juan Soto should make things easier for the Yankees? 🤣

Nicely explained! Thanks

That was a great explanation. Thanks for this.

Crystal clear explanation.

Yes, that WAS a very clear presentation : I really was confused between L and Ws. He anticipated my question of why the 2 instead of just one. We need the behind the scenes details (like this) to grok what is going on.

Thank you sir, it's now much way more clear.

Thank you for the amazing video..

I want to say a big thank you for incredibly useful and easy explanations. Well done.

my ex husband Vincent is not doing very well now. I still love him very much. Please if you see this keep him in your prayers. Lee Runnels

Hi, thanks for the great content! in terms of performance of querying and transforming data, which of the two is preferred? Or does it come down to comparing Spark vs T-SQL?

Great! Thank u.

💃 "Promo SM"

Huge regrets for not finding this video earlier. The best have seen so far. yesterday got your book from O'Reilly. Spent night reading it and all I can say that such book and subject was very much required. thank you very much James. Have started following you on LinkedIn.

“things i like behind me” for some reason i thought to myself he likes printers 😂 … jk this is a great video Thank you!

Nice talk to understand the high-level how all connected together and compare with existing offering.

Amazing way of explaining with granular details. Thanks

Thanks for the explanation. It's really helpful.

Great intro video!! Fully agree on the comment regarding dedicated SQL pool!! We made a touch but wise decision to stay away from it for new analytics projects kicked off at the beginning of this year!!

Great Presentation , thank you

BY FAR THE SINGLE MOST EFFECTIVE EXPLANATION EVER ❤

BY FAR THE SINGLE MOST EFFECTIVE EXPLANATION EVER ❤

How can I get access to the links on the presentation?

This was incredibly helpful! I am prepping to teach the DP-500 and one of the things I am trying to figure out is how to compare/contrast/talk about "traditional" Synapse vs. Fabric. Your insights really help!

Awesome content which not only explain the technical nuances but also explain the use case of Lakehouse versus Warehouse from the architecture design perspective. Enjoyed watching video and got lots of clarity. Thanks James

Thank you! I was thinking that the Warehouse was more like a dedicated pool. I'm hoping it is a bit more robust than the current Synapse Serverless.. at least once we get more T-SQL support..

Finally, I understood the whole buzz around Fabric. Main differentiators from Synapse look like SaaS | OneLake | Dropping MPP aka Dedicated Pool | Compute/storage decoupling (official) |...plue few more. I am not sure how well "Auto discovery and registration of table" feature will work - specially if metastore already exists in Databricks. Will Databricks share its metastore with Fabric or we will recreate metastore here? Also, will metastore be at workspace level or tenant level?

If you were to create a shortcut to a CSV file , right now it seems that you have to manually create the table, and it does not update if the source file changes. It's quite common that we have folders in the datalake containing versions of a csv. Sales_01 , Sales_02 etc. In serverless sql you could just target them with openrowset and an asterisk. Sales_* would just load all the files in the folder. And since it was a view you would always gaurantee the data was not stale. Are there plans to improve the way data is loaded as tables in the lakehouse to support this?

as an old-school EDW guy, you cut through much of the terminology fuzziness and answered several questions I had. Thank you

Thanks for a simple yet insightful session

Great video! Can you please post the links in your Data Mesh section (46:37) or the presentation as a whole? Thanks.

Great overview! Thankful for the 'Azure-Translation' (i.e., showing how all the products fit into the overall data flow)!

good explanation, thank you

Does MS Fabric supports things like Private connections to on-prem, key vaulted credentials/configs like Azure does? Is it something we can expect to see in future if not there already?

That was as clear as you could get. Thanks for the great work!

very clear! Thank you James

Thanks for this, it was very helpful. Showing the sql endpoint on the lakehouse editor was a revelation.

Excellent content. I've been using Synapse for a while and it took me sometime to get through all the different options and limitations. This video helps clarifying a lot about Fabric. Can't wait for the book to be published. Thank you.

As always, you break down what has been a source of confusion with clarity - Thanks!

Hi Great content - Why not to use both Warehouse and Lakehouse? for example DataBricks that uses Lakehouse and AWS-RDS for Warehouse

Hi James, first of all, great video! Most of our current dashboards are built on Tableau which connects to our SQL database. If we moved to Fabric and used OneLake, would we still be able to connect to Tableau?

Hi James, thanks for the good introduction! Good insights into the architecture of Fabric/OneLake. Will it be possible to use data virtualization as a layer between on-premise SQL Server 2022 and OneLake (maybe, in conjunction with the new feature Shortcuts). I remind myself, that SQL Server 2022 is able to use the Polybase v3 feature in conjunction with ADLSv2 access (virtually), but since OneLake is somehow extra/segregated from ADLSv2, I doubt that it will be possible, at the moment. A workaround for me would be to data virtualize between on-premise and ADLSv2, and then shortcut/"bridge over logically" to OneLake (hosted in Fabric capacity). Thanks in advance!

6:39

ua-cam.com/video/a6A3jtvB62U/v-deo.html "Do not [...] use Pipelines within Synapse" I hope we are fine as long as we don't use (Synapse) Data Flow activities.

Once again, Great and useful video! Thank you James!