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The importance of storage infrastructure in energy workloads

Published by , Editor
Energy Global,

From performing extensive seismic analyses to developing sustainable biofuels, modern energy advancements are fuelled by data. For organisations in the oil and gas and renewable energy sectors, concerns over data volumes, movement, and security continue to grow. These energy companies must be able to store and manage data according to the unique workload characteristics that drive their breakthrough dis-coveries. As such, they need a stable, easy-to-manage, and high-performance data storage foundation that they can rely on.

Poor data storage and data management impact business success across any industry. However, because energy companies rely on so many high-performance computing (HPC) and artificial intelligence and machine learning (AI/ML) applications, they especially cannot afford to have an improper storage infrastructure in place.

Below are the five main data challenges energy companies face – from performance bottlenecks to high costs associated with storage waste – along with suggestions on how the right infrastructure choices can help.

1. Lengthy collection and analysis of data

The extensive volumes of data that companies gather and process during the prospecting phase is essential to identify the best places to dig, drill, build, or store. Modelling and simulations rely on this data to extract and refine valuable information. However, with the wrong storage solution, data processing and simulation runtimes can form a bottleneck that massively slows time to results and delays business initiatives.

To quickly gain insights from these massive data volumes, proper storage infrastructure is key. Solutions that adapt to changing workloads will significantly improve efficiency, with auto-tuning capabilities reducing runtime and accelerating the collection and analysis of data. There are now solutions available to optimise analytics of AI/ML workloads which will maximise the efficiency and performance of the storage media, saving the business money and speeding time to market.

2. Data silos

Siloed data can result in incomplete datasets and inefficient processes, as well as impact an organisation’s ability to meet stringent compliance requirements, such as GDPR, that vary by region. Storing data in silos prevents companies from taking a holistic approach to managing their environments. This ultimately reduces data visibility, prevents easy access, and runs counterintuitive to team collaboration.

Consider instead a consolidated storage infrastructure that allows data to be more easily managed and shared between teams. One of the most effective ways to achieve this is with a single-tier scale-out architecture that automatically places data on the most efficient underlying storage media. Taking a consolidated approach to your storage environment promotes control, collaboration, faster times to market.

3. High costs associated with overflowing data deposits

It has been reported that unstructured data makes up 80% of enterprise data, including machine-generated data, and this is growing at a rate of 55 – 65% per year. These massive volumes of data can easily get out of hand. Companies need to know what data they have, where it is, and how much it is costing to store it. Proper tools are needed to reduce storage waste costs and empower enterprise teams to easily locate and access their data.

To make data visible, actionable, and economical, it is vital to have granular and analytical visibility into data environments. Knowing where data resides ensures it is stored efficiently. Additionally, it helps businesses make smart storage decisions and ensure that stale, unused data does not take up valuable high-performance storage that is required for active applications.

4. Poor data mobility

Once data has been collected and processed, it often is moved to a core data centre, transferred to a client, or shifted between onshore and offshore sites, sometimes across large geographical distances. The cloud is another component that must be taken into consideration as it is a viable option for many business applications, but certainly perhaps not all. This could involve the movement of data, particularly when it comes to development and moving production data to different cloud sources or leveraging the cloud as a repository for backing up and archiving data. The process can be complex and both time and resource intensive.

The concept of data gravity comes into play here. The idea that data has ‘mass’ highlights a critical point that moving a single file or single megabyte from A to B is very different from transferring terabytes or petabytes. The magnitude of data processing for high-performance workloads such as those in the energy industry requires a physical infrastructure and energy level that exceeds most standard storage solutions.

When considering data storage infrastructure for high-performance energy workloads, it is critical to factor in the ability to copy, sync, and move large output files easily and reliably between different locations. High-performing and secure data mobility capabilities will result in reduced data movement time and complexity, improving company agility.

5. Securing high-performance data

With ransomware and cyberattacks becoming increasingly frequent and sophisticated, the security and reliability of high-performance storage systems is critical. Typical recovery times in the event of a storage failure can range from one day to an entire week, and depending on the organisation, a single day of downtime can cost the organisation more than US$1 million in lost revenue.

As the storage system can be tapped by thousands of nodes requesting data simultaneously, energy organisations must ensure that their data – for small and large files alike up to petabyte-sized volumes – is accurate and readily available. Storage infrastructure should incorporate scalable backup options, and accurate data tagging and archiving for fast, reliable access. A strong data protection strategy will save energy businesses immeasurable time and money.

Managing and analysing the massive volumes of data associated with energy workloads and applications requires a high-performance technology infrastructure, including a sophisticated storage system. The right one will energise both the data scientists and technicians, accelerating their research and discoveries.

Written by Jeff Whitaker, VP Product Strategy and Marketing, Panasas.



For more news and technical articles from the global renewable industry, read the latest issue of Energy Global magazine.

Energy Global's Spring 2023 issue

The Spring 2023 issue of Energy Global hosts an array of technical articles focusing on offshore wind, solar technology, energy storage, green hydrogen, waste-to-energy, and more. This issue also features a regional report on commodity challenges facing Asia’s energy transition.

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