Simplify ETL pipelines on the Databricks Lakehouse

In this excerpt from The Best Data Engineering Platform is a Lakehouse, you’ll learn why the lakehouse is the best place to build and run modern data pipelines, and learn Delta Live Tables best practices. Watch the full webinar: dbricks.co/3nCP3Jm
Watch the full webinar to learn how to:
-Leverage lakehouse architecture for a simple, unified approach to data engineering
-Meet your needs for real-time data streaming
-Improve the latency, throughput, accuracy and cost-effectiveness of your data pipelines
-Serve your entire data team with one solution
You can watch the full webinar here: dbricks.co/3nCP3Jm

[@thedatamaster]

Michael, thank you very much for creating Delta Live Tables, which is one of the most innovative features.
Thank You Databricks. ❤

[@lostfrequency89]

Best presentation! Love databricks team ❤

[@akhtarmiah]

So does databricks provide ingestion process like ADF. ADF has 90+ data connectors to pull data from data sources

[@ChirgalHansdah]

What is the difference between live table vs streaming live table ?

[@fb-gu2er]

All good but, the one thing I don’t like is limitations. 100 concurrent pipelines updates per workspace is very low. I have many more than that. It’s never go work out with such limit

[@shanhuahuang3063]

after all this ads, could apply change be applied more than one time in a single one way data pipeline?

[@shahzadb]

Michael can we parameterize input paths for Delta Live Tables ,it appears Delta Live Tables enforce a static path ? how can DLTs work against data partitioned over a file structure for e.g. yyyy-mm-dd/hh/*.csv , all the examples I have seen in Documentation assume a fixed input path, what if the input path changes over time e.g. hourly
given data partitioned over day hours on storage , can a trigger DLT pipeline path update through parameterization ?

[@Kinnoshachi]

❤

[@fengliang4787]

❤