Green Intelligence Quotient: Smart Analytics Software Can Rescue Your Data Center
Reducing energy consumption and improving business performance is a win-win for businesses by any standard.
- By Lisa Hopkins
- February 25, 2009
By Lisa Hopkins
Businesses today face a unique challenge: they must store and manage an ever-increasing amount of data while keeping data center power, cooling, and real estate costs down. The combination of these opposing needs has plagued IT managers and served as a wake-up call for CIOs. Many realize that hardware alone can no longer bear the burden of today’s data-hungry climate and have called for smarter solutions.
Fortunately, a growing number of businesses are finding success by addressing the problem at its source -- their data management software. By implementing more power-efficient analytics software, IT executives can manage explosive data growth efficiently without adding more servers to their data center. This unlikely solution for addressing eco-efficiency has proven to help companies with saving both kinds of green -- the environment and money.
The explosion in the amount of data companies are collecting and analyzing is alarming. Web sites and operational systems already produce volumes. Add to that the need to store multiple years’ worth of data. New business regulations plus several civil court actions (with huge damage settlements) have focused attention on the need to keep and search unstructured data, starting with e-mail, documents, and voice recordings. Massive volumes of messages need to be archived and analyzed for everything from signs of potential legal and regulatory issues to information for upcoming sales or service efforts. Using current methods, this analysis will require huge amounts of extra IT power.
Simultaneously, IT will be asked (if it hasn't been already) to control the growth of its energy and carbon footprints. Data center electrical consumption was reported at 61 billion kWh in 2006, representing 1.5% of all U.S. electricity consumption, according to the U.S. Environmental Protection Agency (EPA). This means that data centers doubled the amount of energy consumed in just a six-year period. The EPA predicts that based on current trends, energy consumption will continue to grow 12 percent per year. The cost of electricity over a three-year period now exceeds the acquisition cost of most servers. It is the second-largest operating cost in data centers after labor.
This data center power escalation causes five problems:
- Cost: Electricity can account for 15-20 percent of the data center cost. Kenneth Brill of the Uptime Institute reports that the average wattage required to cool hardware has grown from eight watts per hour for $1,000 of hardware in 2000 to 109 watts today. Price per kilowatt hour varies by region and by season, a more than 12-fold increase.
- Cooling: Equipment densities cause hot spots that can surpass 30 kilowatts per rack.
- Limited Energy Envelope: Every data center has a power ceiling that cannot be exceeded without expensive building improvements. Kilowatts have become the critical data center currency.
- Pollution: The manufacturing, use and disposal of most electronic equipment today worldwide depends on fossil fuels and the use of hazardous substances, releasing large amounts of pollutants, including carbon dioxide, a major contributor to global warming.
- System Availability: Sunguard reports that 26 percent of the IT business continuity disasters it responded to in 2006 were power related.
To address these problems businesses must make a substantial change in the way they manage the hardware and software in their data centers.
A Smarter Approach Not Limited to Hardware
Fortunately, there are new technologies that promise to help ease the data center energy crisis. In addition to more efficient hardware and virtualization technologies (which can increase hardware utilization), there are software solutions that can address the root cause of energy concerns.
Using sophisticated analytics to outsmart the competition is emerging as a “must do” business practice in many industries. Vast amounts of current and historical data must be run against intricate analytic models to accurately predict future outcomes. However, these analytics systems are where the data explosion has had the most impact. Implementing more efficient analytics software, therefore, can solve not only the data explosion and its byproduct (rampant energy use) but also dramatically increase the speed, scalability, and flexibility of business intelligence.
Column-based analytics software has been a particularly successful approach in reducing inefficiencies. The column-based approach is an alternative to general-purpose, row-based database management systems. Row-based systems were not designed for analytics, and as a result, the inefficiencies of these systems have added to IT’s power management problems in two ways:
First, general-purpose databases double or triple raw data with indices and management overhead, requiring extra storage. Second, because row-based databases are not built for analytics, they are inherently slow when computing analytic requests. This often leads to IT managers acquiring more hardware to fix the performance problem, only to compound their energy problem.
As an alternative to the inefficient row-based systems, a column-based database can provide cost and energy advantages similar to virtualization while dramatically accelerating performance. Up to 100-fold performance improvements with up to 90 percent compression of the raw data in large analytic applications have been recorded which result in a dramatic reduction in hardware and energy requirements and a decrease in CO2 emissions by as much as 90 percent.
Column-Based Analytics Servers Deliver the Green
Once implemented, column-based databases can serve many purposes in the data center. The most typical use is in applications requiring instant answers on large data volumes and that must serve many complex, ad hoc queries from hundreds of concurrent users. These include applications such as reporting and data warehouse accelerators, Web-based analytics services, and advanced analytics (such as risk analysis, click-stream analysis, and fraud detection). On-demand analytics -- where applications such as stock trading software or manufacturing software call the analytics system for instant answers -- are also an excellent match.
A column-based database may be architecturally different from a row-based DBMS, but from a management perspective, it is just business as usual. Column-based analytics software typically uses mainstream front-end access tools, standard ETL, and standard servers and storage. Additionally, a column-based database is often much easier to maintain and requires less tuning than traditional DBMSes.
Reducing energy consumption and improving business performance is a win-win for businesses by any standard. As more companies battle data growth and energy efficiency, the need for smart solutions, such as column-based analytics, will continue to grow.
Lisa Hopkins is the director of product marketing for intelligent enterprise solutions at Sybase, Inc. You can reach the author at firstname.lastname@example.org