Confluent Announced Their S1 and “Data In Motion” Mission
The world of business runs strictly on numbers and values, and as companies slowly shift to the digital world, they require intricate solutions to accommodate their new requirements. As a result, data processing is practically a necessity in the modern age, and the naturalization of the practice is well underway in most sectors.
However, adapting rapid data processing, queuing, and distributed messaging is different across all industries, and those that deal with the most data have the most challenging time implementing adequate software solutions. One company may have found a unique solution to this issue, though.
Confluent, a stream data platform provider, has just announced their S1, which reimagines how the business will work in the digital age of tomorrow, today.
Let’s take a brief look at their strategy, the core requirements for enterprise-native data in motion, and how some SaaS alternative solutions may actually be more compelling.
Confluent’s “Data in Motion” Strategy
The Data in Motion strategy applies to companies looking to create a digital-first approach to doing business. Unlike most other solutions that promise to integrate seamlessly within an existing business infrastructure, Confluent sees the business world of tomorrow differently.
The Digital First approach dictates that companies won’t integrate solutions that automate their processes – they’ll change their operations from the ground up to accommodate new-age digital solutions.
There is a lot of focus on supplementing the existing, archaic business structure, yet Confluent strives to reimagine how things work.
The digital-first approach is highly beneficial for various industries as it can improve almost all internal and external processes, thus streamlining efficiency, improving operations, and cutting costs simultaneously.
We live in a data-driven world, and as time goes by, more and more things will rely on data than anything else. That is why understanding how data moves and improving the process is key to doing business in the future, and Confluent’s data-first approach promises to do just that.
New companies will have a slightly easier time accommodating these changes, as they are starting from scratch. However, those that have been around for a while won’t have too much trouble making the switch either. These established companies won’t need to implement anything to their existing framework – they’ll simply change the framework in its entirety.
The Core Requirements for Enterprise Data in Motion
Now, data in small businesses and data in megacorporations are two very, very different things. While one needs to process a couple of payments a day, index a small amount of data, and analyze things on a bi-weekly or monthly basis, the other has to do these things in a sub-second time frame.
That being said, making the transition towards a software-driven real-time full-stack operation will take some adjustment – and some know-how.
Enterprise-level data needs immense processing power to move and flow properly, which is why a solution will have to come with a range of features to accommodate these large quantities of data, the most important of which we will outline below.
Geo-replication is a feature found in data systems that handles distribution across data networks located in different places across the world. The bigger a corporation gets, the more locations and data centers it will have, and keeping every one of them up to date is easy through geo-replication.
Geo-replication is used to improve response times, making both internal and external operations more streamlined. This feature is mostly present across SQL systems and Distributed messaging systems.
Multi-tenancy isn’t a combination of hardware and software such as geo-replication but a kind of software architecture. With multi-tenancy, a single software instance runs simultaneously on a sole server, actively serving more than one tenant at a time.
Through this particular software structure, systems can actively run the same thing more than once in one centralized area, allowing for process automation, data collection, querying, and analysis.
As data flows and moves, practically none of it is wasted. In today’s world, the company that has the most relevant and useful data is the one that reigns supreme. Data will need to be stored somewhere, and with huge quantities of it coming in every day, tiered storage is the way to go.
Tiered storage involves segmenting the data and storing it according to data integrity, quality, and relevancy. Not all data is created equal, nor do all pieces of data get assessed or used as often as others. It is a surefire way to augment speeds, improve analytics, and optimize results.
Pulsar and its Promise for Data in Motion
Data in motion as a concept and as a practice announced in Confluent’s S1 is an appealing and promising prospect. However, the technology required for such a thing is quite intricate and doesn’t necessarily even exist yet. Sure, we have data processing systems, SQL engines, and a range of other software that might be prepared to take this data-driven approach, but one thing is certain – innovation is needed now more than ever.
Apache Hive used to be the world premiere SQL engine, yet it wasn’t designed to handle such large amounts of data, which is why Presto became a thing. Following this rhetoric, the world of data in motion will need a specialized set of tools that can work with such quantities of data and features – and the answer will most likely be Pulsar.
Data in motion isn’t merely a prospect or a promise – it’s a reality that is right on our doorstep, just like Big Data, AI, and ML. All these technologies are going to coexist in a sort of symbiosis, where one forwards the sophistication and development of the other until we’ve reached a technology-induced business equilibrium.
Apache Pulsar was built from the ground up to handle large quantities of data through advanced solutions. One thing that makes it a promising prospect for data in motion is its virtually unmatched scalability.
Apache Pulsar is a cloud-native distributed messaging platform which is used for server-to-server messaging and communication. It comes with all the things that Confluent’s new S1 might need, such as multi-tenant software architecture, cloud-native geo-replication, and tiered storage to improve how the data itself is handled.
How Pandio Can Help
While Pulsar is promising, managed services such as Pandio provide the additional security, expertise, and experience to make a large enterprise comfortable deploying open source in the wild. Pandio is focused on delivering AI Orchestration through its full product suite that addresses the abstraction of data (managed Trino), the movement of data (managed Pulsar), and the rapid build, training, and deployment of machine learning models (PandioML).
Pandio’s managed service utilizes Apache Pulsar to the fullest extent to streamline any internal and external querying and to message, making S2S communication a walk in the park. Furthermore, Pandio typically saves large enterprises 40%+ versus Kafka, which is a game-changing factor because such services handle enormous quantities of data.Lastly, Pandio is compliant with AI/Ml solutions, as it was built around artificial intelligence. As the world of data continues to evolve, so will the sophistication of Pandio’s services.