Business Intelligence Data Engineering Groups Open Source 
CIO
JUNE 7, 2022
More specifically: Descriptive analytics uses historical and current data from multiple sources to describe the present state, or a specified historical state, by identifying trends and patterns. In business analytics, this is the purview of business intelligence (BI). Data analytics tools.
Invid Group
AUGUST 28, 2024
They can be proprietary, third-party, open-source, and run either on-premises or in the cloud. External metrics can be implemented using Business Intelligence (BI) tools and shared with the clients to measure performance. Either way, the solution has to bring value to the day-to-day operations.
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Lacework
NOVEMBER 1, 2023
In many cases we see that customers prefer to have their data stored and managed locally in their home region, both for reasons of regulatory compliance and also business preference. Curated data are highly granular points of data, which while useful individually, are difficult to derive a “big picture” view from.
Apiumhub
AUGUST 12, 2021
He has spoken at many conferences, user groups, etc., Gema Parreño Piqueras – Lead Data Science @Apiumhub Gema Parreno is currently a Lead Data Scientist at Apiumhub, passionate about machine learning and video games, with three years of experience at BBVA and later at Google in ML Prototype.
Altexsoft
OCTOBER 29, 2020
Here, we introduce you to ETL testing – checking that the data safely traveled from its source to its destination and guaranteeing its high quality before it enters your Business Intelligence reports. What is Data Engineering: Explaining the Data Pipeline, Data Warehouse, and Data Engineer Role.
Altexsoft
JANUARY 22, 2020
Please note: this topic requires some general understanding of analytics and data engineering, so we suggest you read the following articles if you’re new to the topic: Data engineering overview. A complete guide to business intelligence and analytics. The role of business intelligence developer.
O'Reilly Media - Ideas
FEBRUARY 11, 2020
Key survey results: The C-suite is engaged with data quality. Data scientists and analysts, data engineers, and the people who manage them comprise 40% of the audience; developers and their managers, about 22%. Data quality might get worse before it gets better. An additional 7% are data engineers.
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