Mistake to avoid using tableau #dataanlytics #datascience #tech #analytics

Mistake to avoid using tableau #dataanlytics #datascience #tech #analytics Scene 1 (Hook - 5s) Text on screen: "New to ... source

https://quadexcel.com/wp/mistake-to-avoid-using-tableau-dataanlytics-datascience-tech-analytics/

Mistake to avoid using tableau #dataanlytics #datascience #tech #analytics - QuadExcel.com

Mistake to avoid using tableau #dataanlytics #datascience #tech #analytics Scene 1 (Hook - 5s) Text on screen: "New to ... source

QuadExcel.com
Learn Python for Data Analysis #shorts - QuadExcel.com

python #dataanlytics. source

QuadExcel.com
How to use Filter Formula for Stock inventory #excel #exceltutorial #datascience #dataanlytics

My team has a few positions open. We've been working through our candidate lists but there is still a little time to apply if you're interested.

All are new positions in a growing national non-profit, established back in the 90s and still going strong.

Data Analytics Manager
IT Project Manager
Helpdesk Technician

https://www.aanp.org/aanp-careers

#FediHire #DataAnlytics #ProjectManagement #Helpdesk #JobOpening

Careers at AANP

Be a Part of Something Big. Come Join Our Dynamic Team! The American Association of Nurse Practitioners ® (AANP) is looking for talented individuals to join us as we build the premier organization for nurse practitioners (NPs) everywhere. In addition to competitive benefits, AANP offers a…

American Association of Nurse Practitioners

Confused by the difference between ETL and ELT? Take a look at this nice article from IBM explaining the similarities, differences, and benefits between the two.

#ETL #ELT #dataanalysis #dataanlytics #datascience #databases

https://www.linkedin.com/posts/charlesdebarros_elt-vs-etl-whats-the-difference-activity-7063198462024003584-esOu?utm_source=share&utm_medium=member_desktop

Charles De Barros on LinkedIn: ELT vs. ETL: What’s the Difference?

Confused by the difference between ETL and ELT? Take a look at this nice article from IBM explaining the similarities, differences, and benefits between the…

Question for #data people -- I'm trying to understand headless BI. In particular, what it solves that SQL doesn't. In other words, why dbt (pre-analytics layer) isn't enough.

From what I can tell, it solves the problem that SQL can't easily be parameterized. You can make views that slice and dice your transactional data, but those views would hardcode a bunch of decisions better left up to the consumer. You can also denormalize the heck out of your data to make all conceivable queries easy, but then you end up with an analytical table that's way too tall and wide.

It seems like what these semantic layer / headless BI tools do is apply the metadata to your transactional data that allow for BI tools to offer slick query builder interfaces, which ultimately are generating SQL. Furthermore, the logic for different types of analysis can be standardized and controlled, compared to people handwriting their queries.

Do I have this right?

#semanticlayer #analyticslayer #headlessbi #businessintelligence #analytics #dataanlytics #datamastodon