Big Data Data Analytics Data Modelling Data Warehouse 
Astera
NOVEMBER 14, 2023
If you have had a discussion with a data engineer or architect on building an agile data warehouse design or maintaining a data warehouse architecture, you’d probably hear them say that it is a continuous process and doesn’t really have a definite end. What do you need to build an agile data warehouse?
Sisense
MARCH 14, 2021
Attempting to learn more about the role of big data (here taken to datasets of high volume, velocity, and variety) within business intelligence today, can sometimes create more confusion than it alleviates, as vital terms are used interchangeably instead of distinctly. Big data challenges and solutions.
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Smart Data Collective
NOVEMBER 18, 2020
The Bureau of Labor Statistics estimates that the number of data scientists will increase from 32,700 to 37,700 between 2019 and 2029. Unfortunately, despite the growing interest in big data careers, many people don’t know how to pursue them properly. Where to Use Data Mining? Practical experience.
Astera
APRIL 27, 2023
The modern data stack (MDS) is a collection of tools for data integration that enable organizations to collect, process, store and analyze data. Being based on a well-integrated cloud platform, modern data stack offers scalability, efficiency, and proficiency in data handling.
Astera
OCTOBER 27, 2023
What is a Cloud Data Warehouse? Simply put, a cloud data warehouse is a data warehouse that exists in the cloud environment, capable of combining exabytes of data from multiple sources. A cloud data warehouse is critical to make quick, data-driven decisions.
The BAWorld
SEPTEMBER 2, 2021
Non-technical users can also work easily with structured data. Structured Data Example. can be grouped in a data warehouse for marketing analysis. This is a classic example of structured data and can be efficiently managed through a database. Unstructured Data. of organizations are investing in big data.
Actian
MAY 21, 2019
The refinement process starts with the ingestion and aggregation of data from each of the source systems. This is often done in some sort of data warehouse. Once the data is in a common place, it must be merged and reconciled into a common data model – addressing, for example, duplication, gaps, time differences and conflicts.
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