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When organizations need to optimize their business processes and improve operations and decisions, the often speak of having the right information at the right time, but don’t always make that a priority. This information optimization is often thought to be expensive and time-consuming, especially with advent of big data and disparate data sources across cloud and on-premises environments, as I have articulated. Datawatch can help business get to information of any variety or volume at any time through its access and integration tools. When I published my last analysis of Datawatch, it had made significant advancements in its platform, with enterprise-class reliability and support for business analytics through its data discovery and virtualization processes. Over the last year Datawatch continued to grow its business worldwide, and through investments into its marketing, sales and product efforts is finding more potential from existing and new customers. The company’s energized product efforts earned it our 2012 Technology Innovation Award for Information Applications for its Information Optimization Suite.
Datawatch has simplified its product portfolio over the last year, focusing on how organizations transform, distribute and optimize information. Its Monarch Professional, Data Pump and Enterprise Server products respectively support these common functions. It has expanded its support for big data to ensure that no matter where information exists, it can be optimized for use across business and IT. In its 11.6 release Datawatch added support for Hadoop and Hive through its Data Pump product. It also works with commercialized Hadoop providers such as MapR, which provides enterprise-scale deployments. Datawatch supports other types of big data technologies, including RDBMS, appliances and systems, which a third of organizations in our big data research are planning to adopt.
Datawatch brings information into business processes through support for a range of environments. For instance, in cloud computing, it partners with Amazon Web Services, a rapidly growing Infrastructure as a Service (IaaS) provider, for a range of applications and tools. Our research finds that business has led the way to cloud computing. Datawatch can operate safely and securely across these environments with little impact to IT.
Datawatch continues to advance in many small but critical capabilities, such as document approval and state management. It optimizes information processing through prefetching data needed by the operating environment. It has added visual presentation methods to its product, including traffic lights and thermometer gauges, and lets users drill down to any level of detail. It now provides Section 508 compliance for supporting the disabled, for which it has created a template that can be adapted to an organization’s specific needs. Datawatch products are used for a wide range of governance and compliance needs. Our research finds that the cost of compliance is rising faster in the last three years, according to 53 percent of organizations in heavily regulated industries.
Datawatch now provides more power to analysts and individuals who need to facilitate information optimization through the Monarch Power Client, a visual environment that was part of the Monarch Professional 11.5 release. This product helps address assembling information into a view, a process that almost half (47%) of organizations in our information applications research found challenging. Monarch Context for Excel helps address the issues in using personal productivity tools with support for secured embedding of information inside spreadsheets and for data lineage. Data is always more valuable when it is a click away, rather than accessible only upon request from a separate analyst.
To support specific needs of IT, Datawatch supports machine data that is generated by applications and systems, which, if shaped in the right format, can help optimize not only IT systems and resources but also business processes. Datawatch can take data in log files and database and combine it with information in reports, documents and HTML pages. Datawatch recently announced it can utilize machine data from Microsoft Windows.
Datawatch has grown through partnerships with software providers such as Qlikview, helping them get access to semistructured data, and solution providers such as Asta Systems. Resellers use Datawatch as a new business enabler to empower the optimized use of information across an enterprise. For global deployments, Datawatch supports languages like Japanese and Chinese and unique character sets. Support for and focus on partners is a critical investment for Datawatch as it seeks to grow globally. I would like to see Datawatch provide a version of its product for free trial on its website, operating either in the cloud or on the desktop.
Datawatch makes information optimization more readily available at an affordable price. Its software’s ability to access content and semistructured information and blend it with structured data is what organizations require to optimize business processes and make more informed actions and decisions. Our research into business technology innovation finds the needs to improve and drive better quality in processes are important to more than half of organizations. We are busy researching information optimization to see how the best practices and efforts of organizations are changing how technologies are used for business.
Datawatch finds itself at the intersection of information needs for an enterprise. I would like to see more support from the company for mobile technology, and simpler methods to flip through information assets and even collaborate on them, but with its current focus on its foundation and enterprise-class requirements, those features represent potential for providing more value by harvesting its investments in big data and cloud computing. Organizations should examine Datawatch to see how it can help them leverage investments to access and integrate information and meet business needs while meeting IT requirements for security and policy compliance. Its progressive software earned Datawatch our 2012 Ventana Research Leadership Award for Information Applications for its deployment at Piedmont Henry Hospital. If you are looking to get information from any source to any form for any business need, see how Datawatch meets the requirements of the next generation of information optimization.
CEO & Chief Research Officer
ParAccel is a well-funded big data startup, with $64 million invested in the firm so far. Only a few companies can top this level of startup funding, and most of them are service-based rather than product-based companies. Amazon has a 20 percent stake in the company and is making a big bet on the company’s technology to run its Redshift data warehouse in the cloud initiative. Microstrategy also uses ParAccel for it’s cloud offering, but holds no equity in the company.
ParAccel provides a software-based analytical platform that competes in the database appliance market, and as many in the space are increasingly trying to do, it is building analytic processes on top of the platform. On the base level, ParAccel is a massively parallel processing (MPP) database with columnar compression support, which allows for very fast query and analysis times. It is offered either as software or in an appliance configuration which, as we’ll discuss in a moment, is a different approach than many others in the space are taking. It connects with Teradata, Hadoop, Oracle and Microsoft SQL Server databases as well as financial market data such as semi-structured trading data and NYSE data through what the company calls On Demand Integration (ODI). This allows joint analysis through SQL of relational and non-relational data sources. In-database analytics offer more than 600 functions (though places on the company’s website and datasheets still say just over 500).
The company’s latest release, ParAccel 4.0, introduced product enhancements around performance as well as reliability and scalability. Performance enhancements include advanced query optimization that is said to improve aggregation performance 20X by doing “sort-aware” aggregations which tracks data properties up and down the processing pipeline. ParAccel’s own High Speed Interconnect protocol has been further optimized reducing data distribution overhead and speeding query processing. The new version 4.0 introduces new algorithms that exploit I/O patterns to pre-fetch data and store in memory, which again speeds query processing and reduced I/O overhead. The need for scalability is addressed in enhancements to enable the system to scale to 5,000 concurrent connections supporting up to 38,000 users on a single system. Its Hash Join algorithms allow for complex analytics by allowing the number of joins to fit the complexity of the analytic. Finally, interactive workload management introduces a class of persistent queries that allows short running queries and long running queries to be run side by side without impacting performance. This is particularly important as the integration of on-demand data sources through the company’s ODI approach could otherwise interfere with more interactive user requirements.
The company separates out its semi-annual database release cycle from the more iterative analytics release cycle. The new analytic functions just released just last month include a number of interesting developments for the company. Text analytics for various feeds allows for analytics across a variety of use cases, such as social media, customer comment analysis, insurance and warranty claims. In addition, functions such as sessionization and JSON parsing allow a new dimension of analytics for ParAccel as web data can now be analyzed. The new analytic capabilities allow the company to address a broad class of use cases such as “golden path analysis”, fraud detection, attribution modeling, segmentation and profiling. Interestingly, some of these use case are of the same character as those seen in the Hadoop world.
So where does ParAccel fit in the broader appliance landscape? According to our benchmark research on big data more than 35 percent of businesses plan to use appliance technology, but the market is still fragmented. The appliance landscape can be broken down into categories that include hardware and software that run together, software that can be deployed across commodity hardware, and non-relational parallel processing paradigms such as Hadoop. This landscape gets especially interesting when we look at Amazon’s Redshift and the idea of elastic scalability on a relational data warehouse. The lack of elastic scalability in the data warehouse has been a big limitation for business; it has traditionally taken significant money, time and energy to implement.
With its “Right to Deploy” pricing strategy, ParAccel promises the same elasticity as with its on-premises deployments. The new pricing policy removes the traditional per-node pricing obstacles by offering prices based on “unlimited data” and takes into consideration the types of analytics that a company wants to deploy. This strategy may play well against companies that only sell their appliances bundled with hardware. Such vendors will have a difficult time matching ParAccel’s pricing because of their hardware-driven business model. While the offer is likely to get ParAccel invited into more consideration sets, it remains to be seen whether they win more deals based on it.
Partnerships with Amazon and MicroStrategy to provide cloud infrastructure produce a halo effect for ParAccel, but the cloud approaches compete against ParAccel’s internal sales efforts. One of the key differentiators for ParAccel as the company competes against the cloud version of itself will be the analytics that are stacked on top of the platform. Since neither Redshift nor MicroStrategy cloud offers currently license the upper parts of this value stack, customers and prospects will likely hear quite a bit about the library of 600-plus functions and the ability to address advanced analytics for clients. The extensible approach and the fact that the company has built analytics as a first class object in its database allow the architecture to address speed, scalability and analytic complexity. The one potential drawback, depending on how you look at it, is that the statistical libraries are based on user-defined-functions (UDFs) written in a procedural language. While the library integration is seamless to end users and scales well, if a company needs to customize the algorithms, data scientists must go into the underlying procedural programming language to make the changes. The upside is that the broad library of analytics can be used based on the SQL paradigm.
While ParAccel aligns closely with the Hadoop ecosystem in order to source data, the company also seems to be welcoming opportunities to compete with Hadoop. Some of the use cases mentioned above such as so called “golden-path analysis, and others have been provided as key Hadoop analytic use cases. Furthermore, many Hadoop vendors are bringing the SQL access paradigm and traditional BI tools together with Hadoop to mitigate the skills gap in organizations. But if an MPP database like ParAccel that is built natively for relational data is also able to do big data analytics, and is able to deliver a more mature product with similar horizontal scalability and cost structure, the argument for standard SQL analytics on Hadoop becomes less compelling. If ParAccel is right, and SQL is the Lingua Franca for analytics, then they may be in a good position to fill the so called skills gap. Our benchmark research on business technology innovations shows that the biggest challenge for organizations deploying big data today revolves around staffing and training, with more than 77 percent of companies claiming that they are challenged in both categories.
ParAccel offers a unique approach in a crowded market. The new pricing policy is a brilliant stroke, as it not only will get the company invited into more bid opportunities, but it moves client conversations away from the technology-oriented three Vs and more to analytics and the business-oriented three Ws. If the company puts pricing pressure on the integrated appliance vendors, it will be interesting to see if any of those vendors begin to separate out their own software and allow it to run on commodity hardware. That would be a hard decision for them, since their underlying business models often rely on an integrated hardware/software strategy. With companies such as MicroStrategy and Amazon choosing it for their underlying analytical platforms, the company is one to watch. Depending on the use case and the organization, ParAccel’s in-database analytics should be readily considered and contrasted with other approaches.
VP and Research Director