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    In-Memory: Speeding Up Value by Using Operational Intelligence

    In-memory technology was around long before Big Data became a buzzword. Parallel computing dates back to the 1980s. With lower cost memory and greater workload demand, in-memory computing has emerged as a key technology. A diverse array of industries, such as finance, retail and ecommerce, looking to capture business opportunities in real-time, are turning to in-memory.

    As Big Data has come to the forefront of the next wave of technological innovation, in-memory data grids have become a powerful tool. ScaleOut Software has identified five ways organizations can use in-memory technology. 

    In-Memory: Speeding Up Value by Using Operational Intelligence - slide 1

    Click through for five ways in-memory technology can be used to benefit the organization, as identified by ScaleOut Software.

    In-Memory: Speeding Up Value by Using Operational Intelligence - slide 2

    Using in-memory data grids for ETL on streaming data.

    A key challenge for any data warehouse is to supply data in a format for easy ingestion and analysis. This is the role of the well-known process called “extract-transform-load.” In today’s Hadoop world, this often means extracting data from external sources and transforming it into a form that can be stored in HDFS. Traditional ETL processes don’t work for streaming data that continuously flows into the data warehouse.

    An in-memory data grid with an integrated MapReduce engine can capture the data stream in real time, perform ETL, and offload the data warehouse.

    In-Memory: Speeding Up Value by Using Operational Intelligence - slide 3

    A secret weapon for brick-and-mortar retailers.

    To compete with online retailers, brick-and-mortar stores are responding by personalizing the shopping experience. 

    In-memory technology can create greater personalization by providing immediate feedback to sales staff based on a current shopper’s history, combined with their current activity in the store. Used with RFID tags, in-memory technology enables retailers to follow items from dressing room to checkout for complementary suggestions or to determine up-to-date demand.

    In-Memory: Speeding Up Value by Using Operational Intelligence - slide 4

    Financial services: Taking action in a changing market.

    Real-time analytics with in-memory data grids offers the ability to examine live, fast-changing data and obtain feedback in milliseconds to seconds. 

    A hedge fund in a financial services organization can track the effect of market fluctuations on its portfolios. Long and short equity positions can be quickly evaluated for strategies and rebalancing. 

    In-Memory: Speeding Up Value by Using Operational Intelligence - slide 5

    eCommerce: Putting the desired outcome in front of customers.

    In-memory computing can be used for integrating real-time analytics into an eCommerce system. The browser can be instrumented to send detailed information about which products customers are examining or the time they are spending on each product. Combining all of this information, the system can build a dynamic history of site usage for each customer and collect a set of preferences for that customer. The information can be used for real-time offers that are specific to the customer’s individual profile.

    In-Memory: Speeding Up Value by Using Operational Intelligence - slide 6

    Using in-memory with the cloud.

    Cloud computing plays an important role in scaling infrastructure to support increased demand. In-memory data grids have emerged to scale application performance to address rapidly growing demand. The technologies are complementary.

    In-memory data grids can scale out their storage and performance linearly as servers are added to the grid or scaled back. In-memory data grids enable cloud-hosted applications to quickly and easily be deployed on an elastic pool of cloud servers. The result is scalable performance, maintaining fast data access even as workloads increase. 

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