Followers may be interested in this white paper from The Bloor Group which summarizes the differences between database technologies It's meaty.
Here are a few additional points that Bloor has written about MarkLogic's Enterprise NoSQL approach:
MarkLogic is also a true transactional database. Most NoSQL databases have compromised the ACID (Atomicity, Consistency, Isolation and Durability) properties that are important for transaction processing, MarkLogic is fully equipped to be a transactional database, and if you simply wanted to use it for order processing, there would be no problem in doing so.
The database has been built to enable rapid search of its content in a similar manner to the way that Google’s search capabilities have been built to enable rapid search of the Internet.
As some of MarkLogic’s implementations have grown to above the single petabyte level, fast search of massive amounts of data is one of its most important features. To enable its search capability MarkLogic indexes everything on ingest; not just the data, but also the XML metadata. This provides it with the ability to search both text and structure. For example, you might want to quickly find someone’s phone number from a collection of emails.
With MarkLogic you could pose a query such as: “Find all emails sent by Jonathan Jones, sort in reverse order by time and locate the latest email that contains a phone number in its signature block.”
You may be able to deduce from this that Mark Logic knows what an email is, knows how to determine who the sender is, knows what a signature block is and knows how to identify a phone number from within the signature block. If you were looking for a mobile phone number then you would simply add the word “mobile” in front of phone number. It should be clear from this that very few databases could handle such a query, because most databases are straight-jacketed by whatever version of SQL they implement and, even if it were possible to bend SQL in such a way as to formulate this kind of query, most databases cannot dig into data structures they hold in the way that MarkLogic.
With the release of MarkLogic 6 last fall, MarkLogic also provided SQL support through integration with Tableau and Cognos, in-database analytic functions, JSON support, JAVA and REST APIs and more. For more information on this release, you can go here:
As capital markets organizations focus on ways to reduce risk, data management is receiving a top-to-bottom makeover.
Tony Agresta's insight:
"In the current environment, smaller is sometimes better. For instance, firms are no longer spending money on gigantic warehouses and agreeing on a single data model that would fit all the data in one place," says Amir Halfon, CTO of financial services at MarkLogic, a provider of database technology. "It's that aspect that introduces the most cost. The traditional approach of a big data warehouse doesn't cut it anymore."
"Consulting Services team members are Big Data experts with vast experience solving challenges with data volume, variety, velocity, and complexity. They are working with the world’s most innovative organizations on projects with lives at stake and trillions of dollars on the line. Consulting Services is constantly evolving its methodology to capture best practices and lessons learned. This dedication to perfection enables MarkLogic customers to bring Big Data Applications to market faster and cheaper than their competitors."
The purpose of Big Data is to supply companies with actionable information on any variety of aspects. But this is proving to be far more difficult than it looks with over half of Big Data projects left uncompleted.
Two of the most often reported reasons for project failures are a lack of expertise in data analysis. Reports show that data processing, management and analysis are all difficult in any phase of the project, with IT teams citing each of those reasons more than 40% of the time.
However, failures in Big Data projects may not solely lie on faulty project management. In a recent survey, a staggering 80% of Big Data’s biggest challenges are from a lack of appropriate talent. The field’s relative infancy is making it hard to find the necessary staff to see projects through, resulting in underutilized data and missed project goals.
IT teams are quickly recognizing a chasm between executives and frontline staffers whose job it is to apply findings from Big Data. In the end,it may not be the anticipated cure-all for 21st century business management. It is only as good as good as the system that runs it.
Very interesting infographic. Why do they fail? For all of the reasons above and then some... Over 80% of the data being collected today is unstructured and not readily stored in relational database technology burdened by complex extract, transform and load. There's also pre-existing data, sometimes referred to as "dark data" that includes documents which need to be included and made discoverable for a host of reasons - compliance and regulatory issues are one. Log activity and e-mail traffic used to detect cyber threats and mitigate risk through analysis of file transfers is yet another set of data that requires immediate attention.
Social and mobile are clearly channels that need to be addressed as organizations continue to mine data from the open web in support of CRM, product alerts, real time advertising options and more.
To accomplish all of this, organizations need a platform with enterprise hardened technology that can ingest all of these forms of data in real time, without having to write complex schemas. Getting back to the point - What do most projects fail? If companies attempt to do this with technology that is not reliable, not durable and does not leverage the skills of their existing development organization, the project will fail.
We have seen this time and time again. MarkLogic to the rescue. With over 350 customers and 500 big data applications, our Enterprise NoSQL approach mitigates the risk. Why? Our technology stack includes connectors to Hadoop, integration with leading analytics tools using SQL, Java and Rest APIs, JSON support, real time data ingestion, the ability to handle any form of data, alerting, in database analytics functions, high availability, replication, security and a lot more.
When you match this technology with a world-class services organization with proven implementation skills, we can guarantee your next Big Data project will work. We have done it hundreds of times with the largest companies in the world and very, very big data.
Streamline data and data gathered for you by market researchers
Help you manage, tabulate, analyze, visualize and deliver data in interactive, web-based and mobile formats
Provide insight into your target market
Offer solutions to pressing business problems.
The only one I would add would emphasize the need to use ALL of your data including new forms of unstructured data created through social and mobile. Streaming this data in real time allows retailers to react to advertising, sentiment, changes in buying behavior and more.
SAN CARLOS, CA, Feb 12 (Marketwire) -- MarkLogic Corporation, provider of the only enterprise NoSQL database,today announced the availability of a free Developer License forMarkLogic(R) Enterprise Edition.
Tony Agresta's insight:
From Gary Bloom: "By providing a free Developer License we enable developers to quicklydeliver reliable, scalable and secure information and analyticapplications that are production-ready," said Gary Bloom, CEO andPresident of MarkLogic. "Many of our customers first experimented withother free NoSQL products, but turned to MarkLogic when they recognizedthe need for search, security, support for ACID transactions and otherfeatures necessary for enterprise environments. Our goal is to eliminatethe cost barrier for developers and give them access to the bestenterprise NoSQL platform from the start."
Visit MarkLogic.com to read Playtime is Over. Upgrade to Enterprise NoSQL..
Tony Agresta's insight:
Playtime is over. It's time to upgrade to MarkLogic Enterprise NoSQL.
CIOs, heads of development and the developers themselves are waking up to the realization that when it is time to put these NoSQL applications into the production, there is a tremendous amount of work still needed to get them ready. If they go into production without security, ACID transactions, high availability, backup and recovery—it would be the equivalent of heading down a long and winding road in a toy car. Eventually, the breaks stop working and the wheels fall off.
But while the open-source enthusiasts have been enjoying their pizza and beer and playing with flavor-of-the-month technology, a new class of Enterprise NoSQL developer has emerged. They recognized the need for integrated search, certified security, clustering, replication, failover, alerting, full-text and geospatial indexing, and a suite of application development tools. And, they turned to the only Enterprise NoSQL database that provided those features: MarkLogic.
This new class of Enterprise NoSQL developer starts building out applications confident that they have all of the capabilities they will need in order to deploy their applications into production as soon as they’re done. They don’t have the drudgery of creating workarounds to ensure ACID transactions, building in security, integrating and maintaining 3rd party search engines, or building out frameworks or administration tools. All that work was already done by MarkLogic: who is trusted by enterprises like Citibank, Dow Jones, BBC and every three letter US agency.
"Organizations have fascinating ideas, but they are disappointed with a difficulty in figuring out reliable solutions,” writes Sicular from The Gartner Group.
"Their disappointment applies to more advanced cases of sentiment analysis, which go beyond traditional vendor offerings. Difficulties are also abundant when organizations work on new ideas, which depend on factors that have been traditionally outside of their industry competence, e.g. linking a variety of unstructured data sources.”
Today, organizations are coming to the realization that free or low cost open source technology to handle big data requires intense development cycles that burn costs and time. Solving demanding challenges in these four areas has proven difficult:
Search & Discovery
Analytics and Information Products
Organizations need to work with proven technology that's reliable and durable. They need to work with technology that handles ACID transactions, enterprise security, high availability, replication, real time indexing and alerting - without having to right 10,000+ lines of code.
Major financial institutions, healthcare payors, government agencies, media giants, energy companies, and state & local organizations have standardized on big data technology proven to increase developer productivity, create new revenue streams and address mission critical operations in a post 9-11 era.
There are still myths about exactly what a NoSQL database can and can't do.
Tony Agresta's insight:
If you are interested in big data, please listen to the podcast with Adam Fowler. He gets to the truth about big data technology. The fact is, enterprise NoSQL databses are available and proven to work.
An introduction to the HDFS Storage feature available as a technology preview from MarkLogic.
Tony Agresta's insight:
Seamlessly combine the power of MapReduce with MarkLogic’s real-time, interactive analysis and indexing on a single, unified platform.
Get more power out of Hadoop. Hadoop and MarkLogic together can allow you to tackle problems that would be difficult or impossible to address by either technology alone.
Save money by leveraging common infrastructure. Using MarkLogic and Hadoop Distributed File System (HDFS) enables common batch-processing infrastructure to be used across many different projects and applications.
Enterprise-class support for Hadoop. Our partnership with Hortonworks provides a strong, supported platform for building enterprise-class Big Data Applications with Apache Hadoop.
The well-known book and movie documenting the success of Billy Beane and the Oakland A's is probably the best example of using data to provide a completive advantage in sports.
Analyzing player tendencies like pitch sequences, at-bats and defensive moves are interesting. When you connect them to other players and teams, they become even more interesting and can lead to sets of rules that make up how you coach every detail. Expectations and patterns taught to players provide them with guidelines on how to react and increase the odds of winning.
Could this be applied to football? It seems like that's what watching film of past games is all about. If a team could tag the plays with meaningful content about the outcome, the situation, the players on the field, the time, location, weather and then make it discoverable, coaches could identify patterns and tendencies that were previously undetected.
There are probably some other applications of big data in football. Analyzing new recruits by looking at unstructured data from the open web is one. Real time twitter streams during game time linked in advertising is likely another.
So who wins? 49ers, 27-24. Sorry Ray Lewis. Just one ring for you.