Data Architecture Data Modelling Data Quality Information 
Astera
JANUARY 12, 2024
Data volume continues to soar, growing at an annual rate of 19.2%. This means organizations must look for ways to efficiently manage and leverage this wealth of information for valuable insights. Enterprises should evaluate their requirements to select the right data warehouse framework and gain a competitive advantage.
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. What are Information Marts?
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Astera
APRIL 24, 2024
A data governance framework is a structured way of managing and controlling the use of data in an organization. It helps establish policies, assign roles and responsibilities, and maintain data quality and security in compliance with relevant regulatory standards.
Astera
FEBRUARY 14, 2024
Only 5% of businesses feel they have data management under control, while 77% of industry leaders consider growing volume of data one of the biggest challenges. Data Vault Architecture A data vault follows a three-layered architecture containing the raw, business, and information vault.
Dataversity
JUNE 19, 2023
In part one of this article, we discussed how data testing can specifically test a data object (e.g., table, column, metadata) at one particular point in the data pipeline.
Astera
NOVEMBER 30, 2023
With rising data volumes, dynamic modeling requirements, and the need for improved operational efficiency, enterprises must equip themselves with smart solutions for efficient data management and analysis. This is where Data Vault 2.0 It supersedes Data Vault 1.0, What is Data Vault 2.0? Data Vault 2.0
Astera
MAY 31, 2024
Here are the critical components of data science: Data Collection : Accumulating data from diverse sources like databases, APIs , and web scraping. Data Cleaning and Preprocessing : Ensuring data quality by managing missing values, eliminating duplicates, normalizing data, and preparing it for analysis.
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