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Build an Agile Data Warehouse with an Iterative Approach

Astera

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.

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Top 20 Data Warehouse Best Practices in 2024

Astera

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.

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6 Benefits of Adopting a Cloud Data Warehouse for Your Organization

Astera

For this reason, most organizations today are creating cloud data warehouse s to get a holistic view of their data and extract key insights quicker. What is a cloud data warehouse? Moreover, when using a legacy data warehouse, you run the risk of issues in multiple areas, from security to compliance.

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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. Information marts are data structures optimized for reporting and analysis.

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Data Vault 101: Your Guide to Adaptable and Scalable Data Warehousing

Astera

Data vault is an emerging technology that enables transparent, agile, and flexible data architectures, making data-driven organizations always ready for evolving business needs. What is a Data Vault?  A data vault is a data modeling technique that enables you to build data warehouses for enterprise-scale analytics.

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How Databricks and Tableau customers are fueling innovation with data lakehouse architecture

Tableau

Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions. In all of these conversations there is a sense of inertia: Data warehouses and data lakes feel cumbersome and data pipelines just aren't agile enough.

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Sisense’s Q2 Release: A Modern Data Experience Across the Analytics Continuum

Sisense

A modern data experience means organizations should be able to: Connect to any dataset, live or cached, wherever it resides Analyze data in any environment, whether it’s code-first, low code, or no code Deploy anywhere: In the cloud, on-prem, or hybrid Embed analytics anywhere. Additional capabilities.