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For nearly 2 decades I have been working on integration of data and systems across many different customers both Federal and Commercial alike.  In 2009, I founded ArganoMS3 to address enterprise integration and API enablement needs as interconnectivity was sorely lacking across many customers and systems.  During this time we were enamored by Service Oriented Architecture (SOA) and the promises that the strategists stated it would bring.  Quickly, we realized that philosophy and reality tend to clash on a regular basis and what was said would make things easy and manageable instead made systems fragile and prone to failure because the interconnectivity portion of SOA had not matured enough to ensure service reliability and system uptime.

As the tools started to catch up with the philosophy users began using message brokers like ActiveMQ and WMQ to establish reliability patterns and Staged Event Driven Architecture (SEDA).  These systems were generally using SOAP messaging which allowed for contract-first development strategies which was a catapult into the right direction for providing true interoperability between systems.  This also gave way to the rise of the Enterprise Service Bus (ESB) and products like Oracle Service Bus and MuleSoft who appeared to provide a layer of process ahead of the SOAP services.  However, SOAP soon was believed to be too big and bulky and was not friendly enough for developers, so they started to look for the next generation of integration technologies.

Image result for ESB pattern

Twenty years after the Y2K scare and engineers are scrambling to bring APIs online for customers and many product companies are scrambling to transform their ESB’s into an API Platform to meet the demand.  So what is an API? In the early 2000s and before an Application Programming Interface (API) was a way to have native languages leverage or invoke a library using methods that were built into the product to make it perform a function and return an expected response.  In 2021, when you hear API you know that it has become synonymous with Representational State Transfer (REST) based APIs.  REST-based APIs bring a lot to the table in regards to connectivity.  They have well-defined methods, response codes to address many different scenarios, support for multiple types of data and the only thing that is required is the utilization of the HTTP/1.1 protocol for communication.

Image result for api gateway architecture

So we have a great approach and great strategy so what’s the problem?  The problem comes with the evolution of enterprises and their data.  As of 2017, over 3 years ago, IBM stated that 90% of the world’s data was created in the previous 2 years.  With more and more connected devices we are creating more and more data each and every second.  And that is where the problem lies with the current API strategy.  HTTP/1.1 is not designed to manage and send huge data sizes between systems. The amount of hardware that is needed to support the increasing volumes of data has single-handedly created Cloud providers such as AWS, GCP, and Azure. 

So where do we go and how do we get there?

There are so many topics to dive into on how to handle this question but in this blog, we will focus on what I call a service connectivity platform and one of the leading companies in this space is Kong!
As much as it may surprise you, Kong is leading the way in the world of service connectivity platforms.  They are the first product company to address multiple layers of connectivity within a single product and with increasing agility, are taking on even more needs.  Kong, however, is frequently incorrectly classified as just an API gateway by many organizations and evaluators such as Forrester because they have not established a service connectivity platform category, and frankly, there just aren’t enough products out there to even compete against Kong.  So what is a service connectivity platform?

Image result for kong distributed api architecture

A service connectivity platform is a collection of tools that address multiple needs in a cloud native world such as ingress and egress control, API management, API gateway, security, and service mesh.  The service connectivity platform leverages these components to accelerate the communications between the different services and provides a number of different approaches and patterns for implementation that historically was not possible.  For example, Kong provides the ability to do a standard legacy API gateway approach similar to other products like APIGEE and 3-Scale.  The legacy API gateway approach is like a beefy reverse proxy that applies some rules before sending the request to the service to be fulfilled.  There is a head controller, and all traffic has to pass through this location.  This can become a choke point in a large enterprise and doesn’t support modern distributed systems. This approach would generally require additional load balancing and networking to manage a modern architecture.  What Kong can do that these other API platforms cannot is implement a point of service enforcement approach to architecture.  This is where Kong management and policy enforcement occur inside the POD next to the service and distributes the workload across a cluster.  In addition, the service is portable and doesn’t require any new service or security strategies in the host platform where it may be relocated to.  Lastly, Kong supports modern communication using protobuf, gRPC, and graphQL to give flexibility to the API implementers, unlike other platforms such as Mulesoft and Tibco, which only support HTTP/1.1 protocols.

Next is the service mesh capabilities that Kong brings to the table.  With a native communication channel between the ingress controller, the API manager, and the service mesh, the speed at which data can get onto the mesh’s data plane is accelerated.  In addition, with a simple plugin, the ability to enable tracing from the point of entry across the entire platform is possible giving a modern observability feature that other integration platforms don’t have.  OpenTracing / OpenTelemetry is an industry-standard that is built into the Kong platform so it allows you to use different visualization tools for a single pane of glass.  The service mesh not only gives you better insight, but it provides more service resiliency and a higher level of security with mTLS built right in.  The standard certificate rotation capabilities ensure that your platform never has an expired certificate and that communication is never interrupted.  Kong Mesh doesn’t just provide value inside of the cluster like other service mesh technology.  Kong has taken this another step to aid in future-proofing enterprises because they know that not all applications, especially in a large enterprise, have container management for legacy systems.  For that reason, Kong Mesh supports VMs as well and provides all of the same capabilities outside of clusters to applications that are located within a VM, bringing the connectivity of the enterprise to a new level.

Image result for Kuma api architecture

So, let’s get back to the big data point.  In the past three (3) years the data has increased and it was estimated that 1.7MB of data per second is created for every person on Earth in 2020.  If we factor in the pandemic it’s probably nearly 40% higher than that.  That is 8 billion people producing 1.7MB+ of data per second.  This information is collected and stored and with an interconnected enterprise many systems cannot keep up. They have periods of downtime where data gets out of sync and all the consumers of that information need to be brought back up to date when things come back online.  HTTP/1.1 and REST APIs are not able to manage this much data and keep up with its day to day message-driven delivery.  Because of this many organizations are actually going backward in technology capabilities and architectural approaches by setting up FTP and File-based data synchronization processes to support the message-driven API data transfers. 

While the regression in architectural patterns is true for most enterprises and ABSOLUTELY true for Government agencies, some companies that are leading the digital edge are taking a different approach.  As mentioned above, Kong supports multiple protocols, including Kafka and gRPC, which runs on HTTP/2 standard.  The HTTP/2 protocol is the next generation of HTTP that is a very fast streaming protocol.  Organizations such as Chick-fil-A, Netflix, Square, and our favorite company chat engine SLACK are using gRPC to keep up with huge volumes of data that need to traverse their enterprises.  In addition, these companies are leveraging service mesh and cloud native applications running in containerization platforms as mentioned above to round out their service connectivity platforms.  These technologies are what keeps their tools up and running 24x7x365 with nearly no downtime.  They are not built on legacy ESB systems using Legacy API gateways and HTTP/1.1.  They have advanced because they needed to.  The rest of the industry has started to realize these new technologies and the needs they have within their enterprise and starting to adopt them as well.

So is this the end of REST APIs?  Absolutely not! Just look we still have some systems out there using COBOL and Fortran.  However, the next generation of enterprise systems is here and it’s built around implementing and using a service connectivity platform, and with that, it looks like Kong is ahead of the rest and leading the way


About the Author: Aaron Weikle

FOUNDER & CEO OF MS3

Aaron has been a though leader in the API enablement and Enterprise Integration space for nearly 2 decades. His innovation solutions while working in the Public sector has saved millions of US tax payer dollars while enabling the government to function at a more efficient pace.

Not only has the Public sector benefit from Aaron’s innovative solutions but he has played major roles in major Digital transformation efforts in the Private sector as well. His participation with solutions such as IoT at Chik-fil-a, API enablement at McDonalds and eCommerce implementation at Ralph Lauren has brought his solutions to global markets.

Filed Under: APIs Tagged With: API, Kong, Tavros

Our vision for Tavros, “a cost-effective, cloud-native, integration platform composed of best-of-breed and seamlessly integrated open-source components”, started to form long before the 2020 global pandemic mandated such a platform. The first signs of Tavros started sometime around 2018 with OpenTracing (now OpenTelemetry).

With many decades of combined integration experience, we wanted to address the areas of an integrated platform where organizations chronically fell short at providing enterprise-ready software and alleviate the pain points.  These included things such as observability of integrations for easier tracing and troubleshooting, integrated DevOps to aid in faster delivery, integrated configuration management to ensure deployability across all environments, Cloud Native Software to support ANY cloud provider, and a way to connect and transform data with ease and efficiency.  These areas became the focus areas in which Tavros would be built. 

Given the proliferation of the microservices architectural style, accelerated by the maturity and value of Kubernetes as a container orchestration platform, we were actively trying to find better ways to address the need for observability in our customers. We eventually found the Distributed Tracing (Dapper) whitepaper by Google, and the amazing open-source work done by Uber, Lightstep, and Red Hat, on the OpenTracing framework, all backed by the emerging Cloud Native Computing Foundation. We saw tremendous value and sound engineering that we wanted to bring to our customers. So we went to work and made our own open-source contributions in the form instrumentation for Mule and Apache Camel.

From this point we started tracking the expansion of valuable, independently developed open-sourced components addressing pain points we’ve identified time and time again in our customers, all growing organically with the support of the CNCF.

We started to have sporadic conversations like: 

  • X platform is really great at some functions, but not so much at others. It would be great if we could just plug-in component A for that.
  • Could we somehow pick and choose the best piece for each concern? 
  • What’s the potential comparative return on investment vs established offerings? 
  • Can you imagine if we all had to stay at home indefinitely in a couple of years because of a crazy virus?

A couple of years later we were introduced to DataSonnet which was the “missing link” that was needed before we felt we could really commit to this effort. Data transformation is at the heart of enterprise integration (identify your bounded contexts!) We always keep an eye out for solid open-source alternatives to support our client base. With the introduction of DataSonnet, a data transformation DSL initiated by our partner ModusBox, in early 2020 we immediately saw the value in an expanded version of the tool’s capabilities. Based on the incredible work in Google’s JSonnet and DataBricks’ super fast Scala re-implementation we knew the engineering was solid and that ModusBox had already done a lot of the work in facilitating the extensibility needed. After a few months of engineering efforts, we co-released DataSonnet v2. In addition to the DataSonnet release, we constructed a native component to intertwine the language within the Apache Camel DSL.  With a few tweaks, we were able to get inclusion as an official expression language within Apache Camel’s latest Long Term Support version 3.7.

With these two pieces figured out, observability and data transformation, the train started rolling. After 10+ years of enterprise integration work at hundreds of different customers and projects utilizing different technologies and components, we have a solid understanding of what supplemental tooling always proves valuable, which components are usually punted down the road that could otherwise accelerate time-to-value for a project; and the experience configuring those components! So we defined and committed a dedicated team to our vision.

Today, Tavros is an integration platform that not only compares to existing enterprise offerings but that we believe is better in many respects.

Here are some features to highlight:

  • Provisioned and configured on-demand by Kubernetes Operations (kOps) and Ansible
  • Infrastructure and Platform As Code (IaC), backed by GitOps and Continuous Delivery after provisioning
  • Out Of the Box application Continuous Delivery pipelines
  • Out Of the Box best-practices configuration for cross-cutting concerns like Logging, Tracing, and Mutual TLS
  • Single Pane of Glass observability
  • Many time-to-value accelerators for integration applications
  • Single Sign On (SSO) experience across the supported components
  • A Bring-Your-Own-Component design
  • Out Of the Box API catalog and developer portals tied into the Continuous Delivery pipelines
  • Commercial support available if needed
  • Minimal to zero licensing cost
  • No risk of vendor lock-in

The components that currently power the Tavros platform include: kOps, Ansible, Kubernetes, Flux v2, Helm, Kubeseal, Kong, Kuma, Keycloak, Jaeger, Elastic Cloud, Gitea, Jenkins, Nexus, Apache Camel, Spring Boot, DataSonnet, and OpenTracing. With plans for many others as part of our Bring-Your-Own-Component design goals.

With quick uptake already occurring by early adopters, we’re excited to bring this unbelievable amount of value to our customers, partners, and the open-source community in general. We hope to start engaging with the community to make Tavros even better.

For more information, check out our GitHub repository and our website.


About the Author: Jose Montoya

Tavros Principal Product Manager, Sr. Cloud Architect

As a Solutions Architect at ArganoMS3, Jose Montoya is in charge of delivering tailor-made software to our clients. Throughout his 5 year career with ArganoMS3 he has worked for multiple companies of every size and of various industries, always delivering business value by working alongside stakeholders at different organizational levels.

In his time off Jose likes working on community-driven open-sourced projects, staying up to date with the latest software engineering practices, watching sports, playing videogames, or tending to his garden in South Texas.

Education: Bachelor of Science in Computer Engineering from the University of Texas.

Filed Under: Integration, Tavros Tagged With: Integration, Open-Source, Tavros

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