europe SNIA
of scale pressure on existing infrastructures, especially the storage platform.
Home is Where the Data is
Storage providers play a critical role in the explosive data growth and increase in scale. After all, they store the data and they need to be able to provide a robust enough environment and solution offering to accommodate such datasets. The most effective solutions are ones that efficiently process, analyze, manage, and access data at scale. Specifically, solution portfolios that are organized by the primary use cases of analytics, bandwidth, and content are those that address the key bases for success.
Analytics for extremely large data sets focus on providing efficient analysis for those datasets that are significantly larger than any we’ve been accustomed to in the past, especially unstructured data. Analytics is all about gaining insight, taking advantage of the digital universe, and turning data into high-quality information, providing deeper insights about the business to enable better decisions. Bandwidth is related to the performance for data-intensive workloads. High-bandwidth applications include high-performance computing (HPC) and the ability to perform complex analyses at extremely high speeds. They also include high-performance video streaming for surveillance and mission planning as well as video editing and play-out in media and entertainment. Unlike legacy applications where low-latency, low-bandwidth solutions sufficed, data-intensive computing requires high bandwidth.
Finally, Content focuses on the need to provide boundless secure scalable data storage. Content solutions must enable storing virtually unlimited amounts of data, so that enterprises can store as much data as they want - but also find it when they need it.
Big Challenges Ultimately, today’s enterprises find it difficult or impossible to manage the exponential growth in big data. Traditional approaches can’t scale to the level needed to be able to ingest all of the data, analyze it at the speed at which it arrives, and store the relevant datasets efficiently for extended periods of time. The industry as a whole has started to get a handle on how to manage the increased infrastructure complexity in a
virtual world, but handling infrastructure in a scalable world presents some very serious challenges.
Time-to-information is critical for enterprises to derive maximum value from their data. If it takes weeks or months to run an analysis, it may not be timely enough to detect patterns that may affect the business in an instant. Compliance is also a significant challenge for many enterprises. Regulated organizations may have to keep data for very long periods of time - or forever. In addition, they are required to find the data quickly when needed for reporting, litigation events or during industry audits. Therefore, the challenges of Big Data are all about gaining business advantage, and specifically, how to obtain the most value for the enterprise from this immense digital universe of information. It’s also important to be aware of the fact that Big Data is breaking today’s storage infrastructure along three major facets:
Complexity. Data is no longer just about text and numbers; it’s also about real-time events and shared infrastructure, and the inherent relationships in the data. The information is now linked, is high fidelity, and consists of multiple data types, many of which are unstructured. Applying typical algorithms for search, storage, and categorization is becoming much more complex and inefficient.
Speed. How fast is the data coming in? High-definition video, streaming media over the Internet to player devices, slow-motion video for surveillance, social media streaming feeds - all of these have very high ingestion rates. Businesses have to keep up with the data flow to make the information useful. They also have to keep up with ingestion rates to drive faster business outcomes - or in the military, to save lives.
Volume. All collected data must be stored in a location that is secure and always available. With such high volumes of data, IT teams must make decisions about what is “too much data.” For example, they might flush all data each week and start all over the following week. But for many business units and their applications, this is not an option, so more data must be stored longer - without increasing the operational complexity. This can cause the infrastructure to quickly break along the axis of volume.
Thinking Big and Different
There are a lot of new and different facets to Big Data. What makes Big Data different is that companies are realizing that all the data they have collected as part of their business operations and all the data that is constantly being collected by video surveillance, web trends, mobile phones, consumer behavior, social media and so on can be combined in interesting and useful ways to gain competitive
June 2012 I
www.dcseurope.info 15
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56