The Cloud Bridge Between On-Premise and Tomorrow’s Data-In-Motion
A very common misconception is that migrating on-premise solutions to the cloud is just a case of replicating them somewhere remotely, with the specifics of that “somewhere” coming down to a choice between price points, geographic distance, and specialist capabilities.
However, simple replication ignores several issues like on-premise bespoke applications that don’t move easily, legacy infrastructure, and historic tooling that produced very specific application instances. Furthermore, replicating legacy systems in the cloud (even were it easily possible) misses out on many of the advantages of cloud-native technologies, agility, CD/CI models of DevOps, elasticity and the ability to scale seamlessly without reference to infrastructure constraints.
Migration is therefore a gradual process, but when ensconced in the journey, a hybrid model involves fragile bridges between premise and cloud comprising of many individually created requests and replies between services. A better solution by far is the positioning of a Kafka Connect framework as the arbiter between multiple clouds and on-premise.
It should be noted that creating a complex Connect framework involves significant resources and may well still be as constrained as any non-cloud service — just what the organization is trying to move away from. Confluent Cloud provides a fully cloud-native solution to businesses that are either migrating towards the cloud or are invested in a hybrid topology. Confluent Cloud offers all the benefits of cloud-native technologies, with data stream processing capable of rapid, real-time data-in-motion computation at any scale.
Confluent Cloud lets companies develop applications on top of consistent data sets gathered and available from every source (legacy, on-premise, cloud). Those data sets also form the basis for near real-time insights into activity as it happens, meaning immediate decision-making and all the possibilities of multiple event triggers.
Those capabilities give companies the type of agility in their operations that allow them to adjust and control down to granular detail issues like customer experience, security posture, operational decisions, and much more.
The technology working on data in motion enabled by Confluent Cloud can be best-in-breed, wherever that stems from: BigQuery or Elasticsearch, Athena on AWS, dedicated machine learning arrays — every use case is different. The available connectors remove much of the hard work when building manual iterations of Kafka, plus a query language for teams are already conversant in.
The bridging facilities between data sources and services where operational constraints exist mean that every service or application can use all of cloud’s advantages, like (pretty-much) infinite storage and processing. The ability to grow and scale in cloud terms is bidirectional, so seasonal peaks in demand , and even sudden transient business upticks are handled without any service slowing or causing backlogs of processing.
Similarly, unlike home-brewed Kafka implementations, updates to core systems that power stream processing of data in motion are uninterrupted — there is a 99.95% up-time guarantee from the Confluent Cloud.
The reliability means mission-critical systems like fraud checking or real-time event triggers are rarely, if ever impacted by security patches or system updates, and with enterprise-grade monitoring, there is full oversight of all data in motion across the homogeneous landscape.
Your teams and decision-makers already know about Confluent and Kafka and the ways that real-time data can create industry-changing concepts and systems. With Confluent Cloud, the enterprise in transition can leverage data in motion. Find out more here.
27 January 2023
25 January 2023