Data Architecture Data Security Monitoring 
Astera
JULY 3, 2024
What is Data Architecture? Data architecture is a structured framework for data assets and outlines how data flows through its IT systems. It provides a foundation for managing data, detailing how it is collected, integrated, transformed, stored, and distributed across various platforms.
Astera
DECEMBER 16, 2024
Pre-Built Transformations: It offers pre-defined drag-and-drop and Python code-based transformations to help users clean and prepare data for analysis. Scalability: It can handle large-scale data processing, making it suitable for organizations with growing data volumes. Integrate.io Ratings: 3.8/5 5 (Gartner) | 4.4/5
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Sisense
JULY 6, 2020
These regulations, ultimately, ensure key business values: data consistency, quality, and trustworthiness. Data architecture creates instructions that guide you through the data collection, integration, and transformation processes, as well as data modeling. Data security is also a part of this field.
Dataversity
DECEMBER 9, 2021
We live in an era where data is progressively becoming the essential and the only fuel for thriving business success. And the rapidly growing volumes of data have raised several security concerns that can’t be overlooked.
Astera
APRIL 24, 2024
It helps establish policies, assign roles and responsibilities, and maintain data quality and security in compliance with relevant regulatory standards. The framework, therefore, provides detailed documentation about the organization’s data architecture, which is necessary to govern its data assets.
Astera
JUNE 14, 2024
Here’s a breakdown of its key components: Data Quality: Ensuring that data is complete and reliable. This involves regular monitoring, validation, and cleansing processes to maintain high standards. Define Your Goals Start by defining clear, achievable goals for your data governance program.
Astera
MARCH 20, 2024
Data Quality Management Not all data is created equal. Data quality management enables you to implement processes for data cleansing, validation, and ongoing monitoring, providing your teams with reliable data that’s fit for analysis. Moving data seamlessly from on-premises systems to the cloud environment.
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