Big Data Storage Challenges

Big data is big news, but many companies and organizations are struggling with the challenges of big data storage. To be able to take advantage of big data, real-time analysis and reporting must be provided in tandem with the massive capacity needed to store and process the data. Data sensitivity is also a huge issue, pushing many to consider a private HPC infrastructure as the best way to accommodate security requirements. Unfortunately, as datasets grow, many legacy software, hardware and transmission techniques can no longer meet these demands rapidly enough.

Big Data Storage Challenge #1 – Data Transfer Rates

In a research environment, data must be moved from primary sources to multiple researchers quickly for any time sensitive analysis. Many Data Scientists who’ve utilized public or general-purpose cloud resources are realizing how data transfer rates pose a major limitation. Therefore, many are moving back to private HPC to surpass these limitations. Whether the requirement is high availability in the face of hardware or infrastructure failure, or reliable and immediate retrieval of archived data, systems must be designed to accommodate these requirements.

Big Data Storage Challenge #2 – Security

Simultaneously, high value data must be protected. Data scientists must also protect their data from intrusion, theft, or malicious corruption. Due to the sensitivity of the subject matter involved in many areas of research, privacy, security and regulatory compliance are enormous factors – compelling many to move away from public and shared cloud environments and toward private cloud and protected infrastructure.

Big Data Storage Challenge #3 – Legacy Systems

Legacy systems tended to be centralized and involved serial processing of data. This is not ideal for Big Data, which is expanding geometrically. Recently, huge improvements in performance have been achieved across parallel filesystems. These networked processors and storage disks using parallel application and file systems (GPFS is an example) offer almost unlimited scalability.

In addition to scalable filesystems, high performance computing systems utilize clusters of computers to address the complex operations required of technical computing in research and Big Data environments. These computer clusters can contain many individual high density servers created for cluster computing.

HPC also requires the fastest, low-latency, high-bandwidth networks. This infrastructure also demands both fast and high bandwidth shared storage access to all of the individual computes in the cluster.

Don’t go it alone!

Working with an HPC vendor like RAID, Inc. is critical when creating or upgrading current systems. Our team speaks the language of researchers and understands how to translate a scientific problem into a computational process. A vendor agnostic approach is also best in order to leverage best of breed products to future-proof systems and ensure that they are scalable per changing needs.

See the various filesystems, servers, block storage and JBOD options we prescribe for Big Data + HPC.