Derrick Harris of GigaOM reports, ” It doesn’t have a cool name like Cassandra, Voldemort or MongoDB, but Google is offering up a non-relational database called Google Cloud Datastore. Like almost everything the company has done since announcing its Compute Engine service at last year’s IO conference — including the rest of the features it announced on Wednesday — Cloud Datastore looks like a direct shot at current cloud champion Amazon Web Services. AWS has a managed NoSQL database service called DynamoDB that’s replicated across three availability zones to ensure its stays up. Google’s Cloud Datastore sounds eerily similar, according to the product’s website (although Google calls its product “NoSQL-like). It’s fully managed, built for speed and scale and is replicated across data centers. For some queries, Google even promises that Cloud Datastore will support ACID transactions.

Harris continues, “Although the services advertise similar features in terms of availability and scalability, they’re quite different technically. Cloud Datastore is based on Google’s BigTable database (and a library called Megastore on top of it) while DynamoDB is based on Amazon’s Dynamo database. You can get details on Datastore  and how it works here. Pricing information is available here. If its goal is to compete with AWS, though, Google’s cloud platform still has a long way to go. Yes, it has most of the key services in place and even some seeming advantages in certain areas, but it’s lacking the incredible breadth of services AWS offers — everything from virtual server instances to a devops service to a hosted data warehouse. It’s also lacking a seven-year reputation for being an all-around reliable platform and an ever-growing list of large-enterprise users.”

Read more here.

photo credit: Google

Rachel King of ZDnet reports, “SAP and Hewlett-Packard have shed light on a secret product in the works, which could reduce the amount of hardware needed to process big data while being more efficient at the same time. Unveiled at the close of SAP Sapphire in Orlando this week, Project Kraken is the combination of HP’s enterprise server technology with SAP’s flagship HANA in-memory database. The key point to know is that this server prototype effectively triples the amount of memory on a single but scalable server designed for processing big data.”

King continues, “Running on Intel’s Xeon E7 processor family (also known as Ivy Bridge-EX), Project Kraken supports up to 12 terabytes of memory on a single server unit designed for processing complex big data workloads. The current industry standard is considered to be four terabytes. Like most new enterprise technology products — both hardware and cloud-related — the goals are to improve business processes by simplifying the setup and reducing processing times. Targeted towards a myriad of verticals ranging from government and healthcare to finance and retail, potential workloads include CRM, enterprise resource planning, and supply chain management.”

Read more here.

photo credit: SAP

A new article out of the company reports that IBM has announced “a major three-year agreement with L’Oréal USA for expert procurement services using an advanced cloud analytics solution that will transform the way L’Oréal USA buys from its network of North American suppliers. The new services will enable the company to bring beauty products to consumers more quickly and efficiently.  With this agreement, L’Oréal USA will partner with IBM to undertake a complete transformation of the buying, compliance and management of its vendor and supply process, ultimately leading to lower costs and increased supplier value. The company will tap a powerful portfolio of IBM procurement services, consulting and analytical expertise including a new analytics tool developed by IBM Research, as well as IBM SmartCloud commerce solutions. Together, these will help the marketing and procurement team at L’Oreal gain immediate and broad access to global data to make faster and more insightful decisions, while ensuring purchasing compliance.”

Rich Ullrich, Vice President Indirect Procurement, L’Oréal USA stated, “With our rapid growth and expansion, we needed deep category knowledge across all spend areas as well as the assurance of compliance with all buying processes in order to realize true savings. IBM was unparalleled in its ability to deliver cutting-edge data analytics, the best cloud commerce solution and the strategic sourcing and technology services needed to make it happen.”

Read more here.

photo credit: L’Oreal

CVS Caremark is looking for a Data Scientist – Healthcare in Scottsdale, AZ. According to the post, “The Data Scientist, Healthcare will be a key role in the new Data & Analytics Center of Excellence at CVS Caremark. We are looking for a creative individual contributor who is skilled at honing in on how we can manage and use our huge data sets to create value for the enterprise. This is a highly visible role and the scientist will be working with analytics and IT data groups across the enterprise. The ideal candidate would have a deep knowledge of healthcare data sets and a proven ability to identify meaningful connections and relationships in data points from diverse sources. This role will be an enterprise resource and may be responsible for defining and acting as the Analytics subject matter expert (SME) on analytics projects in the range of $50K – $1M+ in budget.”

Qualifications for the position include: “Deep understanding of analytical methods, statistics, predictive modeling, and application of machine learning to address analytical needs. Strong knowledge of current-state of databases, data structures, BI tools, and other analytics technology with a focus on how these can be applied to address business challenges. Understanding of health care data, analytics, and current uses of analytics to address buinsess challenges in health care. Knowledge of common data structures and ability to write efficient code in at least one language. Proficiency with relational (SQL) and non-relational databases (Hadoop). Ability to work with multiple business and data groups to define and establish data marts and databases to meet aggressive customer timelines. Ability to identify unexplored data opportunities for the business to unlock and maximize potential.”

Learn more and apply here.

photo credit: CVS Caremark

To view just the slides from this presentation, click HERE.

About the Presentation

Yes, we face a data deluge and big data seems to be largely about how to deal with it.  But 99% of what has been written about big data is focused on selling hardware and services.  The truth is that until the concept of big data can be objectively defined, any measurements, claims of success, quantifications, etc. must be viewed skeptically and with suspicion.  While both the need for and approaches to these new requirements are faced by virtually every organization, jumping into the fray ill-prepared has (to date) reproduced the same dismal IT project results.

Learning Objectives include:

• The very real, very rapid, very great increases in data of all forms (charts showing data types and volume increases)
• Challenges faced by virtually all data management programs
• Means by which big data techniques can compliment existing data management practices
• Necessary but insufficient pre-requisites to exploiting big data techniques
• Prototyping nature of practicing big data techniques

About the Speaker

Peter Aiken is an award-winning, internationally recognized thought leader in the area of organizational data management, architecture, and engineering. As a practicing data manager, consultant, author and researcher, he has been actively performing and studying these areas for more than 30 years. His sixth book is titled XML in Data Management. He has held leadership positions with the US Department of Defense and consulted with more than 50 organizations in 20 different counties. Dr. Aiken’s achievements have resulted in recognition in Outstanding Intellectuals of the 21st Century and bibliographic entries in Who’s Who of Emerging Leaders in America, Who’s Who in Science and Engineering, and other recognitions. His entertaining but clear and concise insights make him a sought after speaker, lecturer and consultant. He is an Associate Professor in Virginia Commonwealth University’s Information Systems Department and the Founding Director of datablueprint.com.

This presentation is brought to you in collaboration with:

To view the recording of this presentation, click HERE.

About the Presentation

Yes, we face a data deluge and big data seems to be largely about how to deal with it.  But 99% of what has been written about big data is focused on selling hardware and services.  The truth is that until the concept of big data can be objectively defined, any measurements, claims of success, quantifications, etc. must be viewed skeptically and with suspicion.  While both the need for and approaches to these new requirements are faced by virtually every organization, jumping into the fray ill-prepared has (to date) reproduced the same dismal IT project results.

Learning Objectives include:

• The very real, very rapid, very great increases in data of all forms (charts showing data types and volume increases)
• Challenges faced by virtually all data management programs
• Means by which big data techniques can compliment existing data management practices
• Necessary but insufficient pre-requisites to exploiting big data techniques
• Prototyping nature of practicing big data techniques

About the Speaker

Peter Aiken is an award-winning, internationally recognized thought leader in the area of organizational data management, architecture, and engineering. As a practicing data manager, consultant, author and researcher, he has been actively performing and studying these areas for more than 30 years. His sixth book is titled XML in Data Management. He has held leadership positions with the US Department of Defense and consulted with more than 50 organizations in 20 different counties. Dr. Aiken’s achievements have resulted in recognition in Outstanding Intellectuals of the 21st Century and bibliographic entries in Who’s Who of Emerging Leaders in America, Who’s Who in Science and Engineering, and other recognitions. His entertaining but clear and concise insights make him a sought after speaker, lecturer and consultant. He is an Associate Professor in Virginia Commonwealth University’s Information Systems Department and the Founding Director of datablueprint.com.

This presentation is brought to you in collaboration with:

Any quick search indicates that there’s no shortage of analytics technologies to extract meaning from Big Data. Yet, a review of the recent activity of Lavastorm Analytics reveals that its Lavastorm Analytics Engine may be one of the more viable.

At the end of April 2013, the company partnered with Datawatch Corporation to include its engine in the latter’s Information Optimization Platform, enabling customers to create analytics applications for a variety of unstructured and structured data significantly faster than before. At the beginning of April, Lavastorm collaborated with Cyfeon Solutions to include its analytics engine in Cyfeon’s Answer Factory solution, which also utilizes MongoDB, Apache Solr, and Hadoop, to increase the speed of analytics and capacity for optimization, while processing massive quantities of data.

In February, Lavastorm unveiled its Lavastorm Analytics Engine 4.6 with updates that increase support to QlikTech QlikView, VMWare 5 virtual machines, and data visualization tools for Excel. More importantly, the company is currently giving out 14 day trials of the Professional Plus desktop version of its analytics engine, as well as a desktop public edition for free. A third version, the Professional Edition, is available for purchase only.

Professional Plus Versus Public

Although the desktop version of this visually-based, incrementally agile analytics tool is designed for single users, there are limited reasons to download the Public Edition. Users can get a feel for the tool and how it works, but will be severely restricted in its capabilities. Whereas the Professional Plus processes up to 10 million rows via databases and Excel, delimited files, and supports a range of XML, HTML, image files, and interfaces for Java, SQL, Python, and SQL; the Public Edition is merely good for 100,000 rows of Excel, delimited files – and supports none of the other aforementioned functions.

The Professional Plus enables users to access a variety of advanced features such as Lavastorm’s vaunted InFlow reporting and visualization for analytics, collaborative library nodes for custom controls, access to the Lavastorm Analytics Engine server, and fee-based, on-site training. The Public Edition only comes with basic collaboration and analytics capabilities, although it hints at the speed and full capacity of the engine. The Professional Edition only lacks the advanced collaboration library controls and the programming interface potential of the Professional Plus; it has a maximum of a million rows.

Engine Diagnostic

The value of the Lavastorm Analytics Engine lies in its ability to allow users to perform data discovery, automated controls, and ad-hoc analytics within the same environment. Its highly scalable architecture (especially with the Professional Plus Edition) also integrates a multitude of data types from disparate sources, so that organizations can preserve legacy silos and still gain a unified picture of their data – without an explicit warehouse. Users can run continuous analytic models by automating process and freely sift through different types of data with discovery tools due to the visual nature of Lavastorm’s analytics. Schema is not required, allowing business users to combine data sources without using code.

The engine works by granting users access to over 100 different analytics nodes in the Lavastorm Analytics Library. Each node is pre-packaged to serve a different purpose, such as to gather information about metadata, data acquisition, qualitative or distributive patterns of data, correlations and more. Since each node already comes ready to perform a specific task, users spend less time programming and can simply deploy analytics on the fly or in automated processes, enabling them to spend more time actually analyzing data.

Professionals can supplement their libraries by downloading additional nodes from Lavastorm that are designed for certain types of functions and frequently-used business systems. Recent packs of nodes include those for R Analytics and for Advanced Analytics. The contents of the May 7^th Enhanced Analytics Node Pack include nodes for statistics such as “Quick Stats”, which provides averages, maximums, minimums, and null counts, as well as nodes for encryption, decryption, and for interfacing URL requests to HTTP servers.  Nodes can also be modified by IT for more specific deployments.

Another distinct advantage of using Lavastorm’s analytic engine is its InFlow Reporting, which offers graphical outputs for specific points within a node and helps users to gain insight via visual representation. This feature is not only essential for ascertaining quick information through discovery tools, but it also assists with the reliability of information gleaned from data. The result is improved data transparency and a clear auditing trail from decisions to the information that substantiated them. This sort of self-documentation by visual representation is easily repeated and helps to optimize data use. Users get a clearer understanding of where information from data comes from, enabling them to rely on data more frequently and accurately.

The relative ease in which discovery tools and ad-hoc analytics are performed is ideal for an Agile work environment. Lavastorm Analytics Engine’s schema-less approach permits users to create iterations before developing formal tables, which increases the celerity in which they can be performed. Additionally, the visual representations of analytics indicate the precise point in which future iterations should occur by displaying anomalies. Users can streamline processes via automation, enabling greater efficiency and continuous analysis of data types. Results can be published in a variety of application environments utilizing conventional BI tools, ERP, data warehouses, and other data management systems.

Lavastorm Analytics Platform

In addition to working in conjunction with other solutions or on its own, Lavastorm also offers a more comprehensive analytics platform in which its analytics engine functions as the central component. When used in conjunction with the other components in the Lavastorm Analytics Platform, the analytics engine serves as a means of federating data from five different components and applying user-tailored analytics capabilities to all of them.

Other than the analytics engine, the most integral component of the Lavastorm Analytics Platform is a transaction warehouse that provides automated analyses of data from transactions. The warehouse is extremely scalable, can process billions of records in almost real time, and can aggregate a variety of data sources such as CRM, crash data retrieval systems, billing events, and IP networks. Its processing expedience enables users to discern patterns and trends almost as soon as they take place, which is valuable for detecting fraud and other threats.

The platform also comes with a resolution center that provides a central dashboard with reporting and visualization tools for case management, which serves as an environment to process concerns found in the transaction warehouse and analytics engine. Lavastorm Bill Analyzer functions as a practical extension of the analytics engine and displays combined views of services and accounts to customers through various billing systems. The Lavastorm Data Acquisition module is optional and utilizes metadata to process structured data without using typical ETL systems or code. Lavastorm has a desktop version of its platform for single users which enables them to deploy rules-based analytics for ad-hoc projects.

Final Thought

As its recent partnerships with Datawatch Corporation and Cyfeon Solutions indicate, Lavastorm’s analytics engine is capable of the type of analytics to transform Big Data into valued information. Its key assets are its extreme scalability and data integration utility. The ease of use of its node library allows for virtually anyone to create analytics to their liking while minimizing the involvement of IT departments – which can still enhance analytics options as needed.

Users have the capacity to integrate, analyze, and optimize Big Data, increasing its viability and importance in business and organizational processes. The highly visual nature of the analytics engine facilitates a degree of traceability which enhances data lineage and agility, allowing users to generate continuous automated analytics as readily as those for ad-hoc purposes.

A new article out of the company reports that, “Oversight Systems will demonstrate its operational analysis software platform which helps businesses and government agencies evolve from exploring big data to quickly generating and delivering operational insights for greater business success, at the DATA (Digital Accountability and Transparency Act) Demonstration Day Hosted by the House Committee on Oversight and Government Reform on May 16, 2013 in Washington D.C. This marks the second annual DATA Demonstration Day, designed to provide Members of Congress, Congressional staff and the public an opportunity to learn how data technologies could help the U.S. federal government cut waste, streamline reporting processes, and improve public accountability if federal spending data were fully standardized and published, as would be required by the proposed DATA Act. The DATA Act, originally introduced in 2011, is expected to be re-introduced in both houses of Congress.”

The article adds, “Across industry and government, Oversight analyzes over $1.75 trillion of transactions to deliver insights that recover revenue and deliver stronger compliance for customers worldwide. The Defense Finance and Accounting Service (DFAS), the U.S. Navy, the U.S. Department of Education and the U.S. Bureau of the Census all rely on Oversight’s platform to prevent over $2 billion in annual improper payments and ensure the accurate reconciliation of expenditures.”

Read more here.

photo credit: Oversight Systems

The team at DATAVERSITY™ is proud to report that, “Enterprise Data World (EDW), the business world’s most comprehensive vendor-neutral educational event about data and information management, took place April 28- May 2 in San Diego, CA. Organized by DATAVERSITY™ and DAMA International, EDW hosted 800 attendees and 200 hours of in-depth tutorials, hands-on workshops, practical sessions, and insightful keynotes that covered a variety of hot topics including data governance, big data, data architecture, enterprise information management, data integration, NoSQL, cloud-based data, and data virtualization. In addition, EDW hosted an exhibit hall showcasing 36 leading enterprise data vendors.”

Tony Shaw, president of DATAVERSITY commented, “EDW is where data management gets really practical. This is where managers and practitioners actually learn from each other about how to deploy the architectures and solutions necessary to succeed in the new world of enterprise data management.” The article adds, ‘This year’s conference highlights included the opening keynote presentation from GitHub’s Tim Berglund, who presented, Then Our Buildings Shape Us. Bergland’s talk shared insights and recommendations for dealing with the torrid pace of technological change, including the challenge of selecting from so many new database options.”

Read more here.

photo credit: EDW

Prachi Patel of IEEE Spectrum reports, “Farmers today produce three times as much food as they did 50 years ago using just 12 percent more land, thanks to new technologies and better farming practices. But the global playing field isn’t level. In Africa, farmers produce a fraction of what they could, according to the Forum for Agricultural Research in Africa, and most barely get by, struggling against infertile soil, drought, and diseases. Helping farmers—in Africa and elsewhere—produce more will be key to lifting millions out of poverty and sustainably feeding a world population of 9 billion in 2050.”

Food-policy experts believe that a crucial step toward that goal is to give farmers, scientists, and entrepreneurs unhindered access to agricultural data which is generated at research centers worldwide. At a two-day international conference on open data in agriculture last week, leaders of the G8—the world’s eight wealthiest countries—brainstormed the best ways to make data available without restrictions, in formats easy for humans and machines to parse. ‘Agricultural data is interesting because it comes in several flavors,’ says James Hendler, computer science professor at Rensselaer Polytechnic Institute, in Troy, N.Y., who guides the U.S. government’s Data.gov website. There are deeply detailed data sets on things like plant genomics and local weather conditions. Then there are broad data sets on such topics as the best crops for certain soils, changes in rainfall levels, signs of pests and diseases, and anticipated prices at local markets.”

Read more here.