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The Ultimate Guide to Enterprise Data Warehouse

Astera

But have you ever wondered how data informs the decision-making process? The key to leveraging data lies in how well it is organized and how reliable it is, something that an Enterprise Data Warehouse (EDW) can help with. What is an Enterprise Data Warehouse (EDW)?

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Teradata Storage Optimization

BizAcuity

Teradata is based on a parallel Data Warehouse with shared-nothing architecture. Data is stored in a row-based format. It supports a hybrid storage model in which frequently accessed data is stored in SSD whereas rarely accessed data is stored on HDD. Plan for system and table space. Collect ResUsage data.

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Teradata Storage Optimization

BizAcuity

Teradata is based on a parallel Data Warehouse with shared-nothing architecture. Data is stored in a row-based format. It supports a hybrid storage model in which frequently accessed data is stored in SSD whereas rarely accessed data is stored on HDD. Not being an agile cloud data warehouse.

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6 Ways to Remove Complexity and Embrace Agility in Your Planning Process

Insight Software

As any CFO knows, budgeting and planning processes are complex. In addition, external market factors require that your planning process not only be able to address your current goals but also be agile enough to quickly respond to industry innovations, economic shifts, and more.

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Leveraging the Benefits of Sales and Operations Planning

Insight Software

To achieve better alignment between these two functions, many companies have adopted a different approach, sales and operations planning (S&OP). It’s about coordinating and streamlining all functions in the value chain–from strategic planning to forecasting and demand planning, inventory management, strategic sourcing, and distribution.

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What is a Data Pipeline?

Insight Software

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

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Data Science vs Data Analytics: Key Differences

Astera

Two key disciplines have emerged at the forefront of this approach: data science vs data analytics. While both fields help you extract insights from data, data analytics focuses more on analyzing historical data to guide decisions in the present. Data warehouses and data lakes play a key role here.