Data center infrastructure is key to big data strategy

"Tenkine Server channel May 21 News" With the advent of the big data age, the infrastructure that hosts the big data faces changes to cope with the big data revolution. Large data is unstructured information of unprecedented scale and form, with more and more sensor-based mobile devices and output data, the amount of data available will continue to grow exponentially. The major data strategy requires changes in the data center infrastructure to include the following:

Large data causes storage requirements to grow by 60% to 80% a year, and given this rapid growth and current cost constraints, it buyers should choose the most cost-effective hardware for scalability and storage speed. Hardware devices such as IBM Netezza and Oracle Exadata have proven to be effective in both scalability and performance. Consider using hardware devices to support critical data Services, but you should also be sure that the architecture of the device will provide rapid performance upgrades in the future.

With successful large data strategies, companies can integrate high-quality data from within and Hadoop from multiple cloud vendors. This also improves the quality of business-related data, allowing disparate data to be organized into large data resources that are consistent and timely.

Large data is changing the decision-making base for central data warehousing and loosely coupled data marts, which have a much smaller repository that can replace either a central data warehouse or a data source in a central data warehouse. With the increase of local offices or international subsidiaries, central management will need high quality data to maintain control and avoid decentralization of power while expanding the line of business.

The demand for enterprise web outside the chain deepens the reliance on the public and the mixed cloud. Many large enterprises find that they need large data from multiple cloud vendors, but they cannot expect the cloud to be responsible for consolidating the data. Enterprises can only seek tools from data virtualization vendors to consolidate large data across multiple clouds.

Storage tiering Strategy for SSD

Large data requires storage to provide both large capacity and high performance. Storage Tiering provides a wide range of cost and performance options in a pool of storage resources, from expensive high-performance solid-state storage to traditional serial scsi (SAS) disk storage, which lowers total cost of ownership. Adding a solid layer between the main memory and the disk will help keep the performance of large data tasks high and not cause storage costs to get out of control. The best combination of the cost and speed of SSDs is the use of approximately 10% SSD and 90% of the mechanical hard drive. This strategy allows IT companies to achieve more than 90% performance gains at the cost of just 10% more. The same rules apply to the main memory and SSD capacity ratios.

As the performance-price ratio of SSD increases faster than traditional disks (capacity increases and prices are lower), the proportion of traditional disk and SSD configurations is expected to follow the rules of 80 and 20 in the near future. The latest vertical and memory database facilities of IBM BLU acceleration can leverage SSD to achieve far more traditional disk performance, and their design can effectively play the advantage of flat disks such as SSDs.

Large data analysis and reporting capabilities

While embedded analysis tools have been able to improve business processes with reporting and automated optimization, large data has changed the analysis rules again. For example, a large data strategy can create an iterative and insight analysis thread for each customer, compared to the traditional primary behavioral analysis insights of a single customer, allowing the company to track customers and better maintain long-term relationships with all customers.

This means that third-party considerations need to be manually added beyond automated reporting and embedded analysis: ad hoc and loosely coupled analysis. Support for dedicated query analysis and statistical tools is a necessary software prerequisite. Many traditional it vendors, as well as cloud vendors, such as IBM, Cognos and birst--, are expanding these capabilities.

Hadoop in the Enterprise

Many IT companies solve the problem of acquiring Hadoop data sources from the Web by creating their own versions of Hadoop within the enterprise. However, lack of expertise is a challenge. Organizations that develop their own data management tools should be aware that these major vendors, such as IBM, Oracle, and EMC, often provide proprietary products for accessing Hadoop data or custom development, so that it companies do not need specialized data-inductive measures to access the data they need. If you decide to build your own data platform, vendors also provide integration services that make Hadoop more efficient and more aligned with existing IT resources.