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  • metadata management

    Metadata management is the oversight of data associated with data assets to ensure that information can be integrated, accessed, shared, linked, analyzed and maintained to best effect across an organization. Continue Reading

  • data context

    Data context is the network of connections among data points. Those connections may be created as metadata or simply identified and correlated. Continue Reading

  • Staring down the security issues in cloud computing

    Adoption of cloud computing has no doubt been slowed by worries about the security of those out-of-sight servers and resources. While reasonable, those worries have given way over time to acceptance and even optimism.

    In this month's Modern Infrastructure cover story, TechTarget’s Trevor Jones writes about why security issues in cloud computing are not the impediment to adoption that they once were. In fact, some organizations are coming to the conclusion that their workloads run more securely in a public cloud than in an on-premises environment. Cloud service providers possess security expertise and experience at levels that aren’t as readily available on a typical IT staff, and certifications give confidence that providers can actually do all they claim to do.

    The public cloud is not a risky place to do business, though its safeguards are different from those you've put in place to protect your own data center. These differences are most clearly seen when looking at the shared-responsibility model, which addresses many of the security issues in cloud computing. Users and providers must each do their part. Otherwise, the risks will become apparent, and cloud computing won't be what you need it to be.

    This issue also looks at how a new wave of products in development brings memory and storage technologies closer together. Nonvolatile dual-inline memory modules, for example, combine the speed of memory with the persistent qualities of storage in some interesting ways. Also included is an article on how some IT shops that have adopted flash storage are simultaneously impressed and disappointed with the results. Costly flash products invariably improve performance, but they won't solve every problem or clear every bottleneck.

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  • Seven ways to unlock the power of all types of unstructured data

    New storage services have emerged to help IT pros maintain and deliver access to ever-larger and more valuable data sets, particularly all types of unstructured data. These include data sets for big data analysis, DevOps, storage self-service and cost-control purposes, among other things. This data explosion is the result of several trends, including growth of files and file systems, machine-generated data streams, web-scale application exhaust, endless file versioning, finer-grained backups and rollback snapshots. If you're architecting a new approach to storage and dealing with various types of unstructured data storage at a global enterprise scale, we have seven advanced storage capabilities you should consider.

    The on-demand nature of the cloud has not only changed the way enterprises consume and pay for IT resources, it has profoundly altered our understanding and expectations of IT services and infrastructure in general. More enterprises expect to obtain the features they want when they want them and to pay for those features only when and if they need them. This has been acutely felt by vendors and consumers alike in the fragmented and dynamic storage market. It has altered how storage providers deliver expected core features, such as deduplication, replication, compression and encryption, and has led to the development of niche products that meet application-specific needs and use cases. This issue clarifies the new and evolving data storage landscape to help you buy and deploy the right storage products with the features you require and more easily acquire the products you may need in the future.

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  • Learn storage techniques for managing unstructured data use

    Rearchitect storage to maximize unstructured data use at the global scale for larger data sets coming from big data analytics and other applications. Continue Reading

  • Pure FlashBlade

    Pure Storage FlashBlade is optimized for unstructured data analytics. Its system-on-a-chip architecture uses custom NAND flash blades to accelerate data and its metadata. Continue Reading

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  • IT teams take big data security issues into their own hands

    Data security needs to be addressed upfront in deployments of big data systems -- and users are likely to find they have to build some security capabilities themselves. Continue Reading

  • Managing the big data ecosystem requires agility amid disruptions

    In some regards, the term big data management can be viewed as an oxymoron. In fact, oxymorons abound in this industry and society -- virtual reality, artificial intelligence, science fiction and awfully good, the latter of which can apply to the challenges encountered in managing the onslaught of big data from multiple sources. There are countless tools, techniques and practices available for the big data ecosystem to properly gather, mine, prep, store and analyze data and help smooth operations, build marketing campaigns, improve customer service and develop the next new product disruptor. As simplistic as this may sound, it's up to data managers to sort it all out as their data lakes swell beyond capacity.

    "The data lake isn't where data goes to die," Gartner analyst Merv Adrian said at the 2017 Pacific Northwest BI & Analytics Summit, "it's where data goes to live."

    October's Business Information opens with our editor's note and advice for data managers to move beyond traditional data control to the critical task of improving data quality and delivery -- taking all that raw data and making it useful. Whether for internal or external business use, the demands for instantaneous data access continue to accelerate, spurred on by mobile apps, artificial intelligence (AI), machine learning and internet of things (IoT).

    In that vein, our cover story examines companies that use their big data ecosystem to divert data lakes toward developing new strategies, products and revenue streams -- in the process, smashing their old business patterns. In another feature, IoT and machine learning technologies help take the guesswork out of estimated times of arrival for transport companies whose businesses depend on shipping and receiving goods.

    Also in this issue, a business intelligence project combined three data warehouses into one to reduce warehouse size by 80% and data load time from several weeks to just days while causing IT staffing problems in the process. In other features, learn how metadata programs can ease mega management woes; semantic technology could be a blessing or curse to AI; companies are gearing up for greater big data management deployments; and all data must be treated equally in the search to find value.

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  • A strong metadata management process eases big data woes

    Big data often comes with big data management problems. Clean, well-defined metadata can make the difference in analyzing big data and delivering actionable business intelligence. Continue Reading

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