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What is Data Architecture? A Look at Importance, Types, & Components

Astera

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.

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What Is Data Governance and Why It Matters?

Astera

The Importance of Data Governance Data governance facilitates accessibility by establishing clear guidelines for who can access the data under what circumstances. These guidelines ensure that every employee has access to data required for their roles, promoting collaboration and informed decision-making across the organization.

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Data Integrations and Use Cases

The BAWorld

Properly executed, data integration cuts IT costs and frees up resources, improves data quality, and ignites innovation—all without systems or data architectures needing massive rework. How does data integration work? This captures and processes data, essentially putting it into the target system in real time.

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Enterprise Data Management: Strategy, Benefits, Best Practices

Astera

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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Should You Have Separate Document, Time-Series, NoSQL and SQL Databases or Can a Single Database Support All of These Data Types and Requirements?

Actian

During the era of edge computing and a wholesale flip of the majority of data being created and emanating from the edge instead of from the data center or a virtualized image in the cloud, specialized applications and platforms have an essential purpose in business process enablement.

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Information Marts: Enabling Agile, Scalable, and Accurate BI

Astera

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.

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Data Science vs Data Analytics: Key Differences

Astera

Analysts use data analytics to create detailed reports and dashboards that help businesses monitor key performance indicators (KPIs) and make data-driven decisions. Data analytics is typically more straightforward and less complex than data science, as it does not involve advanced machine learning algorithms or model building.