Redefining Hadoop Data Strategy to Achieve More Insight with Less Cost (English)

Presented by

Seng Joo Lim, Digital Insights Solution Engineer; Derrence Chan, Technical Expert for Data Intelligence

About this talk

With the current pandemic situation, organizations are now being force to relook and prioritize their IT investment. In this webinar, we will explore the challenges our customers are facing with storing data long term in Hadoop, and discuss what Hitachi Content Platform (HCP) and Lumada Data Optimizer able to help our customers solve the exabyte data challenge in reducing cost of Hadoop Data Repository. For years Hadoop has been the go-to data processing platform because it is fast and scalable. While Hadoop has solved the data storage and processing problem for many years, it achieves this by scaling storage and compute capacity in parallel. As a result, Hadoop environments have continued to expand compute capacity well beyond their needs as more and more of the storage is consumed by older, inactive data. Although HDFS is effective at storing small-to-mid size repositories of data, HDFS becomes vastly more costly and inefficient as storage needs expand, since this requires increasing both storage and compute. HDFS also relies on data replication (storing multiple copies of each object) for protection. As these data sets grow into the petabytes the growing cost of old data and idle compute in your Hadoop ecosystem will become unsustainable. Key take away from this webinar: - Lower Hadoop Cost with Lumada Data Optimizer for Hadoop - Seamless Access to Hadoop Data with No Disruptions - Scale Compute and Storage Independently - Optimize Hadoop Resource Consumption and Utilization - Avoid Data Protection Headaches - A Secure, Flexible and Cost – Effective Storage Solution
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