The English poet John Donne tells us that “no man is an island.”  In the same way, nothing lives in isolation. So then how do we identify boundaries for microservice implementation and deployment?  Domain Driven Design offers patterns to implement a “bounded context,” itself a pattern, but relies on the business to define the bounded context.  This is as it should be.  The observation to be made here is that the underlying technology with which the bounded context is implemented will determine the rigidity or flexibility of the implementation.

 

Linked Data is conceived to allow dynamic linking between any two resources (URIs).  As a result, irrespective of where the boundary is set to describe the resource, our own Temporal Linked Data® (TLD) makes it easy to reference TLD aggregate as an OWL Object Property link, whether deployed together or apart, the container will resolve the link.  

 

Where relational, columnar, and object data management platforms require these relationships to be accounted for ahead of time, TLD allows these connections to be made along the way, according to well bounded business semantics, referenced as opaque RDF resources.  

 

By decoupling design time concerns from runtime concerns, the iterative and incremental deployment of TLD microservices represents a strategy for iterative and incremental growth of business functionality.

 

The Earth as Well Bounded Microservice in the Universe (NASA Image of the Day)

For my part, I like to know something relevant about the person whose work I am reading.  To this end I thought it would be of interest to relate how our auto-generated Hybrid Transactional / Analytic Processing (HTAP) by way of Temporal Linked Data® (TLD) came about.

 

For approaching 30 years, BRSG Consultants have been business school trained IT Management Consultants and ISV employees, and entrepreneurs.  Our transition from Smalltalk to Java began in late 1996.  We have long preferred technologies that allow us to model the business the way it is, such that business personnel would recognize the abstraction.  Lean and agile business process on the management side, and enterprise distributed object systems on the technology side, have been our calling card.  From a Java perspective, we have focused on operational in-memory IMDG, and Hadoop / Spark analytic platforms for our HTAP efforts.

 

Along the way we have found Semantic Web, Linked Data, and Actor Model concepts to be of interest in combination for their commercial potential.  While this proved to be a heavy lift to implement in a commercially viable way in the Java ecosystem, our efforts to do so became a reality when we turned our attention toward Elixir and the BEAM ecosystem.

 

We have no stones to throw at the great technology that we have used to date.  Nothing has changed there and we are happy to provide services in these areas.  But for our own product development efforts and, if given the option, we have simply found the BEAM ecosystem to be more economically and technically compelling.  

 

We hope that you will find our reasons to be of interest as we share them along our HTAP TLD journey.

 

Modern optics and the Hubble Telescope allow us to see a galaxy 270 million light years away and a Milky Way star only 2500 light years away — what we are able to see this century is not new, just new to us (NASA Image of the Day)

Low-friction data structures are those that: 1) are self-describing, 2) directly represent that which they model, and 3) do not require transformation between operations and analytics.  Our graph-throughout approach to Hybrid Transactional / Analytic Processing system development encourages a direct connection between business and IT where data model creation can be collaborative.  

 

Once the discrete, well-bounded RDF data model is designed, the backend system is generated, ready for: 1) behavioral augmentation where necessary, 2) test, and 3) deploy.  Monolith and command-and-control are out, well-bounded microservice, distributed, and peer-to-peer are in.

 

This low-code approach helps to address the: 1) human resource impact of adopting new technology, 2) communication overhead of translating business concerns to production enterprise software, 3) adaptability of systems to business need, and 4) explicit connection between operational and analytic systems.

 

In contrast to relational and columnar data management which rely on implicit relationships embedded in SQL, Temporal Linked Data® (TLD) makes an explicit connection between temporal data aggregates.  

 

Likewise, where Event Stores keep all data domain events together, TLD keeps all temporal data alongside the aggregate to which it belongs.

 

Explicit connections are both hard and soft, technical and social.  Hybrid Transactional / Analytic Processing by way of TLD explicitly connects:

 

  • Temporal data aggregates,
  • Operational data with analytic data for realtime analysis,
  • Business and IT personnel, and
  • Represents the shortest path between business concept and production deployment.

 

Amundsen Scott South Pole Station, a low-friction location to view the night sky (NSF Public Domain Image)

 

Our generation has the unique privilege of being able to see back to within a few million years of the beginning of time.  How far back one is able to look depends on the technology that is used, but our Universe’s past is there to be seen.  Temporal Linked Data® (TLD) naturally keeps a record of enterprise data changes to enable another kind of time travel, the story of enterprise data, for both operational and analytic purposes.

 

This series of weblogs introduces TLD as a transactional, low-code, enterprise class compute and temporal data cluster that naturally projects all writes to a world class big data graph analytics platform such as: 1) third-generation graph database for analysis, machine learning, and explainable artificial intelligence by way of TigerGraph, and / or 2) enterprise knowledge graph, ML, and AI by way of ReactiveCore.

 

For a high-level understanding we will briefly explore these subjects.

 

For a more concrete understanding we will use a gamification example, described as follows.

 

The technological innovation represented by the BEAM ecosystem and third-generation graph database allow for the possibility of building enterprise systems that simultaneously account for operational and analytic concerns.  We look forward to taking this fast-data, big-data, HTAP, Temporal Linked Data® journey with you.

 

Our Temporal Universe