Please note ? my focus is on analytics applications here; I’m not painting This ensures with a broad brush. I’m focused on the segment of the market where my current endeavors have led me and an area where I’m engaged with Fortune 2000 customers on a daily basis.
So ? while there are tremendous benefits to the cloud — many of which enterprises are experiencing throughout COVID-19 as they see the direct compare and contrast to applications running on-premise and those on cloud platforms like Salesforce ? Snowflake ? Microsoft Azure ? AWS ? GBQ ? etc. — there are incredible constraints to moving analytic applications quickly and with the performance and latency required by Fortune 2000 companies.
Part of the equation in these This ensures
Migrations goes back to the investment I mentioned earlier. In list to data order to experience the real potential of cloud migration ROI — and it is very real — organizations have to invest in strategies to optimize application I/O ? Data Management ? and ? ultimately ? data engineering. As the models tied to cloud computing are based upon compute cycles (and the cost per) ? it’s critically important that data is optimized for the applications which will consume it and that compute study of navigation menus cycles are minimized wherever and whenever possible.
These basic premises are
Why we continue to invest heavily in our semantic layer ? alleviating common canada cell numbers practice the constraints of moving analytic applications between on-premise deployments and hybrid cloud topologies and autonomous data engineering ? automating the complex ? time-oriented tasks of data engineering. in the optimal structure for interactive analytics — with minimal cost.