IBM Storage Ceph: The Perfect Backbone for Data Lakehouses

In the past year, IBM has made significant strides in integrating Red Hat storage products into its own storage products. This integration has become crucial as organizations face unprecedented challenges in scaling AI due to the exponential growth of data in various formats and locations, often with compromised quality. To address this issue, IBM has focused on modernizing infrastructure through solutions as part of digital transformations. A key component of this effort is the adoption of cloud-native architectures, which offer benefits such as cost-efficiency, speed, and elasticity.

Formerly known as Red Hat Ceph and now rebranded as IBM Storage Ceph, this open-source software-defined storage platform plays a pivotal role in these endeavors. Software-defined storage (SDS) has emerged as a game-changer in data management, offering unparalleled flexibility and scalability that are well-suited for modern use cases like generative AI. With IBM Storage Ceph, storage resources are abstracted from the underlying hardware, enabling dynamic allocation and efficient utilization of data storage. This not only simplifies management but also enhances agility in adapting to evolving business needs and scaling compute and capacity for new workloads.

IBM Storage Ceph is a self-healing and self-managing platform designed to deliver unified file, block, and object storage services at scale using industry-standard hardware. This unified storage approach helps bridge the gap between legacy applications running on independent file or block storage and a common platform that includes object storage. The scalability, resiliency, and security of IBM Storage Ceph make it an ideal choice for supporting large-scale deployments, including traditional and newer generative AI workloads.

The explosive growth of unstructured data and generative AI are interconnected, with each influencing and benefiting the other. According to Gartner, large enterprises are expected to triple their unstructured data capacity by 2028 across on-premises, edge, and public cloud locations. Unstructured data, such as text, images, and videos, serves as a valuable source for training generative AI models. In turn, generative AI helps make sense of and extract insights from this vast pool of unstructured data. This symbiotic relationship fuels innovation and advancements in both fields.

To meet the growing demands of data and AI, organizations require a storage management solution capable of accelerated data ingest, data cleansing and classification, metadata management and augmentation, and cloud-scale capacity management and deployment. IBM Storage Ceph seamlessly scales out to meet these demands while ensuring hassle-free operations and data integrity.

To truly leverage the power of data and AI across an organization and drive better business outcomes, companies must adopt a hybrid approach. This involves consuming storage services on-premises with a cloud-native operating model to address specific requirements such as enterprise feature sets unavailable on public clouds, data sovereignty considerations, and cost optimization. IBM Storage Ceph’s plug-and-play architecture simplifies integration with existing infrastructures, various platforms, cloud environments, hypervisors, and open-source data repositories. Adding new nodes or devices to the cluster is seamless and does not disrupt services, making it an efficient choice for building a data lakehouse with next-generation AI workloads.

IBM’s recent updates to Ceph, including the latest version 7.0, have introduced important features such as NVMe/TCP capabilities. This enables faster data transfer between storage devices, servers, and cloud platforms while retaining the low Latency and high bandwidth characteristics of traditional NVMe. NVMe/TCP is particularly beneficial for applications that require ultra-fast storage access, such as databases, analytics, and content delivery. It simplifies infrastructure by leveraging existing network technology investments and enables a software-defined approach that delivers cloud-like speed, agility, and economics.

IBM Storage Ceph’s ability to store data as objects within logical storage pools allows for easier and faster access to data with content and context classifications. A single cluster can have multiple pools, each tailored to different performance or capacity requirements. This eliminates hardware restrictions and enables cost reductions at scale compared to traditional storage array architectures.

IBM has also made deployment for Ceph easier than ever before with the introduction of IBM Storage Ready Nodes for Ceph. These pre-configured software and hardware solutions come in various capacity configurations optimized for running IBM Storage Ceph workloads. This simplified deployment process ensures faster time to value, allowing clients to optimize costs while achieving scaled capacity and performance.

Overall, IBM Storage Ceph is a powerful solution that addresses the data challenges faced by organizations in scaling AI. Its flexibility, scalability, and compatibility with cloud-native architectures make it an essential component of modern infrastructure and digital transformation strategies.

Background Information

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About IBM:

IBM, or International Business Machines Corporation, is a globally American multinational technology company with a storied history dating back to its founding in 1911. Over the decades, IBM has consistently been at the forefront of innovation in the field of information technology. The company is known for its pioneering work in computer hardware, software, and services, with breakthroughs like the IBM System/360 and the invention of the relational database.

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Technology Explained

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Latency: Technology latency is the time it takes for a computer system to respond to a request. It is an important factor in the performance of computer systems, as it affects the speed and efficiency of data processing. In the computer industry, latency is a major factor in the performance of computer networks, storage systems, and other computer systems. Low latency is essential for applications that require fast response times, such as online gaming, streaming media, and real-time data processing. High latency can cause delays in data processing, resulting in slow response times and poor performance. To reduce latency, computer systems use various techniques such as caching, load balancing, and parallel processing. By reducing latency, computer systems can provide faster response times and improved performance.

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NVMe: Non-Volatile Memory Express (NVMe) is a newly developed technology that has been gaining traction in the computer industry. This technology is a standard interface which allows for high-speed storage and retrieval of data from solid state drives (SSDs). NVMe is designed to increase the speed of data transfers in storage systems by enabling a direct connection to PCI Express (PCIe) bus, resulting in significantly faster access times compared to traditional interface protocols such SSDs. NVMe is particularly useful for applications that require lightning-fast access to large amounts of high-value data. NVMe-based SSDs are being widely adopted in the computer industry and are being employed to power data centers, high-end workstations, and gaming machines to support lightning-fast data processing and retrieval, which unlocks possibilities for machine learning, real-time analytics, edge computing, and other cutting-edge applications. NVMe is proving to be an invaluable tool in the field of computing, offering immense

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