When compressing your data, you can choose between lossless or lossy methods. Since your primary CPUs are no longer being monopolized by data compression, you can continue analysis and processing without waiting. By queueing jobs, you eliminate the need to wait for resources to become available for compression. You can dedicate FPGA processing power to compressing data and queue compression jobs to these chips. ![]() You can use FPGAs to accelerate your hardware and share computation responsibilities with your primary Central Processing Units (CPUs) To avoid this loss of power and time, consider adding coprocessors to your system.įield-Programmable Gate Arrays (FPGAs) are microchips that can be custom configured as additional processors for your machines. If your resources are tied up in compression, your productivity will drop until the compression is complete. When you compress data, you must use computing resources and time that could be used for analytics or processing. To maximize the value of your data, you need to be able to maximize your storage and processing resources and minimize your costs. ![]() Tips and Considerations for Big Data Compression Compression can also remove irrelevant or redundant data, making analysis and processing easier and faster. DigitalGlobe’s databases, for example, expand by roughly 100TBs a day and cost an estimated $500K a month to store.Ĭompressing big data can help address these demands by reducing the amount of storage and bandwidth required for data sets. The growth of big data has created a demand for ever-increasing processing power and efficient storage.
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