Save Big Money with Big Data

Data in data warehouses doubles every 2.5 years. For users, this means more data to analyze, leading to better business outcomes. That’s the good news. The bad news is that this extra storage capacity and computing power comes at a cost. A high cost it turns out.

So what is an enterprise to do?

Keep writing bigger and bigger checks to the data warehouse vendor? At least the business can take advantage of the extra data?

Or should they move some of the lesser-used data to tape? That will save money. But it will also limit business access to this now “off-line” data which may mean missed business opportunities.

What if there was a third option that would preserve the on-line access for the business analysts and control these escalating costs for IT?

Cisco’s new Big Data Warehouse Expansion solution announced this week at Cisco Live provides this third option.

Log in here to access the presentations at Cisco Live on Cisco’s new Big Data Warehouse Expansion.

Cisco Big Data Warehouse Expansion is a new offering that combines hardware, software and services to help customers control the costs of their ever-expanding data warehouses by offloading infrequently used data to low-cost big data stores. Analytics are enriched as more data is retained and all data remains accessible.

Components in the solution include:

  • Cisco UCS optimized for big data stores.
  • Cisco Data Virtualization for federating multiple data sources.
  • Appfluent VisibilityTM to deliver analytics on business activity and data usage across Teradata, Oracle / Exadata, IBM DB2, IBM Netezza, IBM® PureData™ for Analytics and Hadoop.
  • Cisco Services methodology for assessing, migrating, virtualizing and operating a logically expanded warehouse.

If you are looking for a solution to your rising enterprise data warehouse costs, look no further than Cisco.

Follow us @CiscoDataVirt to stay up to date on the latest news!

Data Virtualization: Live at Cisco Live! San Francisco

It has been a great year for Data Virtualization at Cisco Live!   Milan, Melbourne, and Toronto were fantastic opportunities to introduce Data Virtualization to Cisco customer and partner audiences.  And we have saved the best for last with multiple activities at Cisco Live! San Francisco.

We kick things off on Monday May 18 with a by-invitation program for Cisco Data Virtualization customers and prospects.  We start the day at 3:00 with a special pass to John Chambers’ keynote address.  This is followed by a reception, data virtualization demo and tour in the World of Solutions hall.  And we close the evening with a dinner at one of San Francisco’s finest restaurants. Participants in this program return on Wednesday night for a special performance by Lenny Kravitz.   If you would like to join us, please contact Paul Torrento at ptorrent@cisco.com.

For those of you attending the full event, Data Virtualization is also featured in two sessions both entitled, Driving Business Outcomes for Big Data Environment.  I will lead a quick summary session on Thursday at 11:15am, with Jim Green providing a deeper-dive technical session from 11:30-12:30 that day.   In these sessions we will address one of the major issues organizations are facing as a consequence of exponential data growth – that is the huge expenses required to upgrade capacity in their enterprise data warehouses. To avoid this spend, customers are looking for lower cost alternatives such as offloading infrequently used data to Hadoop.  In these sessions you will find out about Cisco’s complete solution with Unified Computing System hardware and Data Virtualization software and Services methodology.

Please also stop by the Data Virtualization booth in the Cisco Services pavilion where we can chat about your business outcome objectives and how data virtualization can help.

And if you can’t make it to Cisco Live! San Francisco, then no worries.  Just check out the recording of my colleague Peter Tran’s session, Utilizing Data Virtualization to Create More Business Agility and Better Decision-Making, from Cisco Live! Milan.  It’s a great crash course intro to data virtualization.

How Data Virtualization Helps Data Scientists

By now it is clear that big data analytics opens the door to unprecedented analytic opportunities for business innovation, customer retention and profit growth. However, a shortage of data scientists is creating a bottleneck as organizations move from early big data experiments into larger scale adoption. This constraint limits big data analytics and the positive business outcomes that could be achieved.

Jason Hull

Click on the photo to hear from Comcast’s Jason Hull, Data Integration Specialist about how his team uses data virtualization to get what they need done, faster

It’s All About the Data

As every data scientist will tell you, the key to analytics is data. The more data the better, including big data as well as the myriad other data sources both in the enterprise and across the cloud. But accessing and massaging this data, in advance of data modeling and statistical analysis, typically consumes 50% or more of any new analytic development effort.

• What would happen if we could simplify the data aspect of the work?
• Would that free up data scientists to spend more time on analysis?
• Would it open the door for non-data scientists to contribute to analytic projects?

SQL is the key. Because of its ease and power, it has been the predominant method for accessing and massaging data for the past 30 years. Nearly all non-data scientists in IT can use SQL to access and massage data, but very few know MapReduce, the traditional language used to access data from Hadoop sources.

How Data Virtualization Helps

“We have a multitude of users…from BI to operational reporting, they are constantly coming to us requesting access to one server or another…we now have that one central place to say ‘you already have access to it’ and they immediately have access rather than having to grant access outside of the tool” -Jason Hull, Comcast

Data virtualization offerings, like Cisco’s, can help organizations bridge this gap and accelerate their big data analytics efforts. Cisco was the first data virtualization vendor to support Hadoop integration with its June 2011 release. This standardized SQL approach augments specialized MapReduce coding of Hadoop queries. By simplifying access to Hadoop data, organizations could for the first time use SQL to include big data sources, as well as enterprise, cloud and other data sources, in their analytics.

In February 2012, Cisco became the first data virtualization vendor to enable MapReduce programs to easily query virtualized data sources, on-demand with high performance. This allowed enterprises to extend MapReduce analyses beyond Hadoop stores to include diverse enterprise data previously integrated by the Cisco Information Server.

In 2013, Cisco maintained its big data integration leadership with updates of its support for Hive access to the leading Hadoop distributions including Apache Hadoop, Cloudera Distribution (CDH) and Hortonworks (HDP). In addition, Cisco now also supports access to Hadoop through HiveServer2 and Cloudera CDH through Impala.

Others, beyond Cisco, recognize this beneficial trend. In fact, Rick van der Lans, noted Data Virtualization expert and author, recently blogged on future developments in this area in Convergence of Data Virtualization and SQL-on-Hadoop Engines.

So if your organization’s big data efforts are slowed by a shortage of data scientists, consider data virtualization as a way to break the bottleneck.

Data Virtualization: Achieve Better Business Outcomes, Faster

Bob Eve, Director, Product Management
View Bob Eve’s original post on Cisco Data Center’s Blog

Data, Data Everywhere!

The challenge of making business decisions in a networked world isn’t a lack of data. It’s having data residing in multiple systems, global locations, locked away in spreadsheets, and in people’s heads.

Almost every enterprise faces this data silos challenge to a greater or lesser degree. But how businesses address it makes the difference between becoming a market leader or an “also-ran.” The fact is, better information leads to better decisions and better business outcomes. The Harvard Business Review (Big Data’s Management Revolution, October 2012) stated that data-driven companies are 5 percent more productive and 6 percent more profitable than their competitors.

Being able to easily access and use vast data stores has always been difficult. But in just the past few years, the problem has become 10 times worse. If it was just more data, then more compute and database horsepower could fix it. The bigger issues for businesses are proliferating data silos and ever-expanding distribution.

Data Virtualization to the Rescue

Industry-leading businesses are addressing the challenge with data virtualization. Data virtualization is an agile data integration approach that organizations use to:

  • Gain more insight from their data
  • Respond faster to accelerating analytics and business intelligence requirements
  • Reduce costs by 50 to 75 percent compared to data replication and consolidation approaches
  • Data virtualization abstracts data from multiple sources and transparently brings it together to give users a unified, friendly view of the data that they need.

Data Virtualization Presents a Unified View

Armed with quick and easy access to critical data, users can analyze it with their favorite business intelligence and analytic tools to drive a wide range of business outcomes. For example, they can increase customer profitability. Bring products to market faster. Reduce costs. And lower risk.

To read more about what Data Virtualization might mean to your enterprise, check out our new white paper Data Virtualization: Achieve Better Business Outcomes, Faster.

Big Data Analytics – Better with Data Virtualization

Big Data Analytics Make Business Sense

Big data analytic opportunities are abundant, with business value the driver. According to the Professors Andrew McAfee and Erik Brynjolfsson of MIT:

“Companies that inject big data and analytics into their operations show productivity rates and profitability that are 5% to 6% higher than those of their peers.”

Read More

Archiving with Big Data = Better Business Results

Business Value from Mixing Current and Historical Data

Historical data is now an essential tool for businesses as they struggle to meet increasingly stringent regulatory requirements, manage risk and perform predictive analytics that help improve business decisions.And while recent data may be available in from operational systems and some summarized historical data available in the data warehouse, the traditional practice of archiving older, detail-level data offsite on tape makes business analytics challenging, if not impossible, because the historical information needed is simply unavailable.

Read More

What Are The Top Data Virtualization Use Cases?

A recently published BI Leadership Benchmark Report lists eleven use cases for data virtualization. The report, Data Virtualization: Perceptions and Market Trends, which includes survey results from 192 BI professionals, was authored by Wayne Eckerson, Director, BI Leadership, a TechTarget research service.
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How Data Virtualization Addresses the Big Data Integration Skills Shortage

Big data opens the door to unprecedented analytic opportunities for business innovation, customer retention and profit growth. However, the big data skills shortage is creating a bottleneck at every organization today as they move from early big data experiments into enterprise scale adoption. This constraint limits big data analytics success.

Read More

Integrating Analytic Data Using Data Virtualization

More Data = Better Analysis

The analytic data domain includes all kinds of data, including:

  • traditional enterprise data from sources like the Enterprise Data Warehouse;
  • big data from sources like Hadoop;
  • cloud data from SaaS applications and public providers;
  • social media data from sites like Facebook and Twitter; and
  • personal/desktop data from spreadsheets and flat files.

Data virtualization is an optimal solution to integrate all these sources, thereby improving analysis and business insight.

Read More

Data Challenges in the Analytics Pipeline

Data is the lifeblood of analytics — the more diverse the better.

In their best-selling book, Big Data: A Revolution That Will Transform How We Live, Work, and Think, Mayer-Schonberger and Cukier describe the synergy that occurs when previously unrelated and disparate data is brought together to uncover hidden insights. But these advanced analytics data requirements are a double-edged sword as these more diverse sources complicate data integration and constrain progress.

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