In-Memory: Speeding Up Value by Using Operational Intelligence

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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 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. 


Related Topics : APC, Resellers, Data Replication, Extract Transform and Load, Structured Data Integration

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