Thursday, April 6, 2017

5 Reasons you should leverage EE BigDataNOW™ for your Big Data

Big Data has been swooping the BI and Analytics world for a while now. It’s touted as the better way of Data Warehousing for Business Intelligence (BI) and Analytics (BA) projects. It has removed hardware limitations on storage and data processing. Not to mention, it has broken the barriers of schema and query definitions. All of these advancements have sprung the industry in a forward direction.

Literally, you can dump any data in any format and start building analytics on the records. We mean any data whether it’s a file, table, object, or in any schema into Hadoop.

1. EE BigDataNOW™ will organize your Big Data repositories no matter the source

Ok, so everything is good until you realize all your data is sitting in your Hadoop clusters or Data Lakes with no way out; how are you supposed to understand or access your data? Can you even trust the data that is in there? How can you ensure everyone who needs access has a secure way of retrieving the data? How do you know if the data is easy to explore and understand for the average user?
Most importantly, how do you start exposing your Big Data store with API’s that are easy to use and create? These are some of the questions you are faced with when you want to make sense of you Big Data repositories.

Stone Bond’s EE BigDataNOW™ helps you achieve the “last-mile” of your Big Data journey. It helps you organize your Big Data repositories, whether in a Lake, in the cloud or on-premise, EE helps make sense of all the data for your end users to access. Users will be able to browse the data with ease and expose it through APIs. EE BigDataNOW™ lets you organize the chaos and madness that the data loading individuals uploaded.

2. Everyone is viewing and referencing the same data

For easy access to the data, Stone Bond provides a Data Virtualization Layer for your Big Data repository that organizes the data into logic models and APIs. It lets you provide a mechanism for administrators to build logical views with secure access to sensitive data. Now everyone is seeing the same data and not different versions of it. This reduces the confusion by providing a clear set of Master Data Models and trusted data sets that are sanctioned to have the accurate data for their needs. It auto-generates APIs for the models on the fly so users can access the data through SOAP/REST or OData and be able to build dashboards and run analytics on the data. It also provides a clean queryable SQL interface, so users are not learning new languages or writing many lines of code. It finally brings a sense of calmness and sureness that is needed for true Agile BI development.

3. It’s swift … did we mention you access & federate your data in real-time?

EE BigDataNOW™ can be a valuable component on the ingestion side of the Big Data store too; it will federate, apply transformations and organize the data to be loaded into the Data Lake using its unique Agile ETL capabilities, making your overall Big Data experience responsive from end to end. EE BigDataNOW™ has a fully UI driven, data workflow engine that loads data into Hadoop whether its source is streaming data or stored data. It can federate real-time data with historical data on demand for better analysis.

4. Take the load off your developers

One of the major complexities that Big-Data developers run into is building and executing the Map-Reduce jobs as part of the data workflow. EE BigDataNOW™ can create and execute Map-Reduce jobs through its Agile ETL Data Workflow Nodes; this will help run Map-Reduce jobs and store results in a meaningful, easy way for end users to be able to access the Map-Reduce jobs.

5. EE BigDataNOW™ talks to your other non-Hadoop Big Data sources

EE BigDataNOW™ includes non-Hadoop sources such as Google Big Query, Amazon Redshift, SAP HANA, etc. EE BigDataNOW™ can also connect to these nontraditional Big Data sources, and populate or federate data from these sources for all your Big Data needs.

To read more about Big Data, don’t forget to check out Stone Bond’s Big Data page. What are you waiting for? Break through your Big Data barriers today!

This is a guest blog post written by,

Monday, February 13, 2017

Did You See the Gartner Market Guide for Data Virtualization?

Gartner’s Market Guide to Data Virtualization (DV) that was published a few months ago was really a “coming of age” milestone for that relatively unknown data integration pattern. With the data explosion on all fronts, the traditional tools and patterns such as ETL, EAI, ESB, or Data Warehouse are mostly obsolete. To download and read full Gartner Market Guide for Data Virtualization click here

Unfortunately, it looks like we’re entering another déjà vu scene, where the next "best way" to handle integration problems is hyped as one more stand-alone category of integration. Remember how we had to decide, before initiating a new project, whether the problem required ETL, ESB, or SOA? Bear in mind that it was never that cut-and-dry; every project needed a little of each, so you just picked one. Then you realized you had to have three different tools and vendors, not to mention plenty of custom coding and timelines, counted in years, to get to the desired end, if at all.  In my experience, no architecture can rely solely on a single integration pattern. Most DV tools focus exclusively on Data Virtualization. There may be a vendor that offers tools in each category, but those are typically separate tools that don’t share objects and functionality.

Stone Bond Technologies has always considered integration as a continuum. There is a huge body of capabilities that are necessary for every single pattern.  You always have to access all manner of disparate data sources; you always have to align them to make sense of them together; you always need to apply business rules and validations. You need to make sure the formats and units of measure are aligned … and on and on. Then you need data workflow, notifications, and events. You need security at every turn. That’s where Enterprise Enabler started – as a technological foundation that handles these requirements without staging the data anywhere, and that virtually eliminates programming. With that, delivering as DV, ETL, EAI, ESB, or SOAP is not so difficult. Most integration software, on the other hand, starts with a particular pattern and ends up adding tools or custom coding to figure out "The Hard Part."

It turns out that Data Virtualization demands that multiple disparate data sources be logically aligned in such a way that together they comprise a virtual data model that can be queried directly back to the sources.

I like the diagram that Gartner included in the Guide (To view Gartner's diagram and read the full Market guide, click here). Below is a similar image depicting Stone Bond’s Enterprise Enabler® (EE) integration platform in particular. Note, the single agile Integrated Development Environment (IDE) covers all integration patterns, and is 100% metadata driven. The only time data is stored is when it is cached temporarily for performance or for time-slice persistence.

Enterprise Enabler®

Refer to the above diagram for a few additional things you should know about Enterprise Enabler:
  • As you can see, all arrows depicting data flow are bi-directional in this diagram. EE federates across any disparate sources, and also can write back to those sources with end-user awareness and security.
  • IoT is also included as part of the source list. Anything that emits a discernible signal can be a source or destination
  • AppComms™  are Stone Bond’s proprietary connectivity layer. An AppComm knows how to intimately communicate with a particular class of sources (e.g., SAP,   Salesforce, DB2, XML, and hundreds of others) including leveraging application-specific features. It also knows how to take instructions from the Transformation Engine as it orchestrates the federation of data lives from the sources.
  • The Transformation engine manages the resolution of relationships across sources and the validation and business rules.
  •  EE auto-generates and hosts the DV services
  • Data Virtualizations and associated logic can be re-used as Agile ETL with a couple of clicks. Agile ETL leverages the federation capabilities of DV without staging any data.
  • EE includes a full data workflow engine for use with Agile ETL or seamlessly inserted as part of the overall DV requirements.
  • EE has a Self-Serve Portal which allows BI users to find and query appropriate virtual data models
  • EE monitors endpoints for schema changes at touch-points where data is used in any of the DV services or Agile ETL. You’ll be immediately notified with detailed import analysis. (patented Integration Integrity Manager) 

Thursday, January 5, 2017

Even Beyond the Logical Data Warehouse

What is a Logical Data Warehouse? There is still much uncertainty and ambiguity around this subject. Or, perhaps I should say, there should be.

Instead of trying to lock down a definition, let’s take advantage of the opportunity to think about what it CAN be. It is the role, if not the obligation, of Experts to describe the essence of any new discipline. However, in the case of LDW, a premature assessment is likely to sell short the potential reach and extensibility of the contribution of Data Virtualization (DV) and Federation to the entire universe of data and application integration and management.

Certainly, the players with the biggest marketing budgets are likely to spread a limited, but compelling, definition and set of case studies, which could become the de facto discipline of Logical Data Warehouse. While these definitions may represent a significant step forward for data management, they would be limiting the full potential of what these new models could bring to the marketplace.

I fear, however, a repeat of the biggest historical impediment to realizing a universal data management framework. Each new “wave” of innovation has been blindly adopted and touted as the single best approach ever. ETL only went so far, then EAI came along as a separate technology (to save the world), Data Warehouse (to store the world) then SOA (to serve the world), and now Data Virtualization and Logical DataWarehouses (to access data faster and with more agility). In this case of Data Virtualization and Logical Data Warehouse, we owe it to our fellow technology implementers to leverage every aspect possible, to advance the cause of the ultimate data integration and management platform.

If we look at all of the data integration patterns, don’t we see that there is a tremendous amount of functionality that overlaps all of these patterns? Why do we even have these distinctions?

What if we seize this DV/LDW revolution as the opportunity to reinvent how we think about data integration and management altogether? Consider the possibility of a platform where:

LDW is a collection of managed virtual models 

  • These can be queried as needed by authorized users.
  • The same logic of each virtual model is reusable for physical data movement
  • Virtual data models incorporate data validation and business logic
  • Staging of data is eliminated except caching for performance
  • Virtual data models federate data live for ETL
  • Virtual data models and accompanying logic can be designated, or “sanctioned” as Master Data definitions
  • Master Data Management eliminates the need for maintaining copies of the data
  • Golden Records are auto-updated, and in many cases, become unnecessary
  • With the “write-back” capabilities, data can be updated or corrected in either end user applications/dashboards or by executing embedded logic
  • Write-back capabilities mean that anytime a source is updated, all of the relevant sources can be synchronized immediately also. (Imagine that eventually, the sync  process as we know it today simply disappears.)
  • Complex data workflows allow the use of virtual models and in-process logic to be incorporated into the LDW definitions.
  • These logic workflows handle preventive and predictive analytics as well as application and process logic
  • Data Lineage is easily traced based on traversing the metadata that describes each virtual model. 
  • Every possible source: applications, databases, instruments, IoT, Big Data, live streaming data, all play seamlessly together.
         Oh, and LDW is pretty cool for preparing data for BI/BA also!

We at Stone Bond Technologies have been leaders in Data Federation and Virtualization for more than ten years. We believe it is our responsibility to remove all obstacles, allow data to flow freely, but securely wherever and whenever it is needed. Our vision has always been a single, intimately connected, organic platform with pervasive knowledge of all of the data flowing throughout the organization, whether cloud, on premise, or cross-business; applications, databases, data lakes.. any information anywhere.

Being too quick, individually or collectively, to take a stand on the definition of Logical Data Warehouse is likely to abort the thought process that is still ripe with the opportunity to take it way beyond the benefits that are commonly extolled today.