Accelerating Apache Spark with Intel QuickAssist Technology - Qi Xie (Intel) скачать в хорошем качестве

Accelerating Apache Spark with Intel QuickAssist Technology - Qi Xie (Intel)

Enterprise and cloud data centers are under pressure to continuously expand revenue-generating and value-added services, such as compute intensive and I/O-demanding Big Data solutions, which moves large amounts of data into and out of storage, and sends it across the networked clusters. A significant amount of time and network bandwidth can be saved when the data is compressed before it is passed between servers, as long as the compression/decompression operations are efficient and require negligible CPU cycles. Intel QuickAssist Technology allows compute-intensive workloads, specifically compression, to be offloaded from the CPU core onto dedicated hardware accelerators. Intel Quick Assist Technology enables developers to create software solutions that leverage compression/decompression acceleration, accessing the technology through APIs in the Intel QuickAssist Software. This talk provides developers with information on Intel QuickAssist Technology and presents some key use cases to provide background for them to understand how they can take advantage of the hardware-based compression acceleration and performance improvements available with Intel QuickAssist Technology in their Spark applications. About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business. Read more here: https://databricks.com/product/unifie... Connect with us: Website: https://databricks.com Facebook:   / databricksinc   Twitter:   / databricks   LinkedIn:   / databricks   Instagram:   / databricksinc   Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. https://databricks.com/databricks-nam...