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Tag: platform management

  • Latest Redis Update Streamlines Management of Platform

    Latest Redis Update Streamlines Management of Platform

    Redis made a unified release of its namesake data store available today for consistent deployment across all supported platforms. Previously, the pace at which upgrades were made was staggered across different offerings only as they became available.

    At the same time, Redis is moving to unify client libraries used to access its open source data store by working directly with the community maintainers of the five most popular client libraries—Jedis (Java), node-redis (NodeJS), redis-py (Python), NRedisStack (.Net), and Go-Redis (Go).

    Other capabilities added to version 7.2 of the platform include support for auto-tiering, to more easily migrate less frequently accessed data to less expensive storage devices. This is managed via an enhanced interface for the Redis cluster management and data integration tools, available in preview. Both make it possible to consume data in real-time from another data source, such as a SQL database. Developers can also take advantage of triggers and functions to build those types of applications.

    Redis is also previewing a search tool for vector data used to create generative artificial intelligence (AI) applications. Rather than employing a separate vector database to build these applications, the Redis platform supports this data type to enable generative AI applications to run alongside other classes of applications accessing the same data.

    Redis CEO Rowan Trollope said collectively these efforts will help DevOps teams reduce costs and streamline the management of upgrades to the platform and updates to applications accessing it.

    Most organizations initially adopt Redis to provide a layer of cache using a data store that runs in-memory. However, as more applications are developed many organizations wind up using Redis as their primary database for applications that require a lot of interactivity, noted Trollope.

    While developers primarily make that architectural decision, the overall management of the Redis data store falls to DevOps teams that will now find it easier to streamline workflows across multiple instances of Redis running in the cloud, or on a Kubernetes cluster, added Trollope.

    Of course, in some cases, organizations still rely on database administrators and data engineers to manage those workflows, but as DevOps continues to evolve, more instances of data stores are being incorporated into workflows managed by a DevOps team. The ultimate goal should be to align DevOps and DataOps best practices to increase the productivity of developers and data science teams building generative AI applications using vector data to customize an existing large language model (LLM).

    Regardless of the motivation, what’s clear is the volume of data that needs to run in-memory continues to increase substantially. As that shift continues to occur, more latency-sensitive applications are being deployed. Maintaining the performance of those applications across a highly distributed enterprise environment with multiple service dependencies requires a level of DevOps expertise that is still difficult to find and retain.

  • HPE Rises to Multi-Cloud Management Challenge

    HPE Rises to Multi-Cloud Management Challenge

    Hewlett-Packard Enterprise (HPE) is making available a suite of tools to unify the management of public and private clouds via a software-as-service (SaaS) applications.

    Bryan Jacquot, HPE worldwide user experience architect, says HPE OneSphere provides IT organizations with a means to manage a multi-cloud IT environment using a SaaS application they can consume in the same on-demand manner they employ to deploy cloud applications on-premises or on a public cloud service.

    HPE OneSphere makes uses of application programming interfaces (APIs) to unify the management of diverse IT environments under a common software-defined framework.

    Within that management application, HPE has also provided Project workspaces for different types of individuals and teams, including IT operations, business users, developers and data scientists.

    Jacquot says HPE makes use of Docker and Kubernetes container technologies to provide the foundation for HPE OneSphere. The goal is to make it easier for IT organizations to manage any combination of hybrid cloud computing environment they choose to craft.

    To one degree or another, most IT environments today are multi-cloud. They typically employ at least one public cloud alongside an on-premises IT environment that is becoming more cloud-like. At the same time, many IT organizations either already have or are planning to add an additional public cloud service, if for no other reason than to play Amazon Web Services (AWS) against Microsoft Azure. However, each stack of public and private cloud computing requires them to master a separate management plane, which drives up the total cost of ownership (TCO) of an IT environment—which creates a challenge at a time when many IT organizations are looking for both lower costs and increased agility. Through OneSphere, however, HPE is resolving that issue by providing access to one common management portal for both on-premises IT environment and public cloud services, says Jacquot.

    There are other means to develop a common management plane spanning multiple clouds. But building, deploying and maintaining a management framework spanning multiple clouds can be a complicated endeavor. HPE OneSphere presents IT organizations with a portal through which all the underlying APIs are abstracted away from the IT operations team, making the process simpler, says Jacquot.

    It’s unclear at what rate IT organizations will move to unify the management of multiple clouds in 2018. Different parts of an organization often fund adoption of different classes of public cloud services. At the some point internal IT teams are tasked with managing those services. But the constitutional crises that drive those events might not occur until 2019 or beyond within many organizations.

    Regardless of the approach taken to managing multiple clouds, there’s no doubt that the distribution of application workloads across multiple internal and external platforms represents arguably the biggest DevOps challenge of 2018. Organizations that get in front of this issue today are likely to find managing IT to be much easier in the years ahead. Those that don’t are likely to experience more pain than they might have ever bargained.

    — Mike Vizard