Data Warehouse Events Uncertainty 
CIO Business Intelligence
NOVEMBER 14, 2022
A DSS supports the management, operations, and planning levels of an organization in making better decisions by assessing the significance of uncertainties and the tradeoffs involved in making one decision over another. The size of the DSS database will vary based on need, from a small, standalone system to a large data warehouse.
DataKitchen
SEPTEMBER 21, 2023
Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability In a world where 97% of data engineers report burnout and crisis mode seems to be the default setting for data teams, a Zen-like calm feels like an unattainable dream.
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CIO Business Intelligence
OCTOBER 15, 2024
Some speculate that Databricks wanted to slow the cruising Iceberg ecosystem with a dose of uncertainty. Another hypothesis: Databricks execs were billion-dollar stoked to stick it to Snowflake by drowning out its event with a buyout its rival reportedly sought. And in theory, at least, it all happens without vendor lock-in.
Timo Elliott
MARCH 6, 2023
He explains that automation and innovation have become critical as the world experiences supply chain disruptions, inflation, extreme weather events, worker shortages, and uncertainty. He focuses on three big opportunities: faster innovation, empowering business people, and moving from analytics to action.
AWS Big Data
AUGUST 7, 2023
Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. Forecasting acts as a planning tool to help enterprises prepare for the uncertainty that can occur in the future.
Cloudera
OCTOBER 3, 2018
The tremendous growth in both unstructured and structured data overwhelms traditional data warehouses. We are both convinced that a scale-out, shared-nothing architecture — the foundation of Hadoop — is essential for IoT, data warehousing and ML. We have each innovated separately in those areas.
O'Reilly on Data
SEPTEMBER 11, 2019
Increasingly, the term “data engineering” is synonymous with the practice of creating data pipelines, usually by hand. In quite another respect, however, modern data engineering has evolved to support a range of scenarios that simply were not imaginable 40 years ago. Different kinds of sensors generate different types of data.
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