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Key Advantages of DBMS for Efficient Data Management

Scalegrid

The DBMS is key to maintaining these aspects by offering a storage system that allows users to perform operations such as data insertion, deletion, and selection, thereby promoting enhanced data integration across diverse applications and platforms.

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The Power of Integrated Analytics Within an IMDG

ScaleOut Software

For more than fifteen years, ScaleOut StateServer® has demonstrated technology leadership as an in-memory data grid (IMDG) and distributed cache. Typical uses include storing session-state and ecommerce shopping carts, product descriptions, airline reservations, financial portfolios, news stories, online learning data, and many others.

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The Power of Integrated Analytics Within an IMDG

ScaleOut Software

For more than fifteen years, ScaleOut StateServer® has demonstrated technology leadership as an in-memory data grid (IMDG) and distributed cache. Typical uses include storing session-state and ecommerce shopping carts, product descriptions, airline reservations, financial portfolios, news stories, online learning data, and many others.

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Use Parallel Analysis – Not Parallel Query – for Fast Data Access and Scalable Computing Power

ScaleOut Software

Whether it’s ecommerce shopping carts, financial trading data, IoT telemetry, or airline reservations, these data sets need fast, reliable access for large, mission-critical workloads. Looking beyond distributed caching, it’s their ability to perform data-parallel analysis that gives IMDGs such exciting capabilities.

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Use Parallel Analysis – Not Parallel Query – for Fast Data Access and Scalable Computing Power

ScaleOut Software

Whether it’s ecommerce shopping carts, financial trading data, IoT telemetry, or airline reservations, these data sets need fast, reliable access for large, mission-critical workloads. Looking beyond distributed caching, it’s their ability to perform data-parallel analysis that gives IMDGs such exciting capabilities.

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Using Parallel Query with Amazon Aurora for MySQL

Percona

now has a version which will support parallelism for SELECT queries (utilizing the read capacity of storage nodes underneath the Aurora cluster). Aurora Parallel Query response time (for queries which can not use indexes) can be 5x-10x better compared to the non-parallel fully cached operations. AWS Aurora (based on MySQL 5.6)

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