Astera
FEBRUARY 28, 2024
52% of IT experts consider faster analytics essential to data warehouse success. However, scaling your data warehouse and optimizing performance becomes more difficult as data volume grows. Leveraging data warehouse best practices can help you design, build, and manage data warehouses more effectively.
Astera
APRIL 13, 2023
In the digital age, a data warehouse plays a crucial role in businesses across several industries. It provides a systematic way to collect and analyze large amounts of data from multiple sources, such as marketing, sales, finance databases, and web analytics. What is a Data Warehouse?
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Astera
NOVEMBER 27, 2023
It includes format checks, range checks, and consistency checks to ensure data is clean, correct, and logically consistent. Understanding the Difference: Data Profiling vs. Data Mining Data profiling and data mining are two distinct processes with different objectives and methodologies.
Astera
JANUARY 23, 2024
Businesses need scalable, agile, and accurate data to derive business intelligence (BI) and make informed decisions. Their data architecture should be able to handle growing data volumes and user demands, deliver insights swiftly and iteratively. The combination of data vault and information marts solves this problem.
Astera
FEBRUARY 27, 2024
52% of IT experts consider faster analytics essential to data warehouse success. However, scaling your data warehouse and optimizing performance becomes more difficult as data volume grows. Leveraging data warehouse best practices can help you design, build, and manage data warehouses more effectively.
Astera
FEBRUARY 27, 2024
52% of IT experts consider faster analytics essential to data warehouse success. However, scaling your data warehouse and optimizing performance becomes more difficult as data volume grows. Leveraging data warehouse best practices can help you design, build, and manage data warehouses more effectively.
Smart Data Collective
OCTOBER 17, 2022
A point of data entry in a given pipeline. Examples of an origin include storage systems like data lakes, data warehouses and data sources that include IoT devices, transaction processing applications, APIs or social media. The final point to which the data has to be eventually transferred is a destination.
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