Confluent’s ksqlDB Is a Strong Performer in The Forrester Wave™: Streaming Analytics, Q2 2021

We are pleased to announce that Confluent has been named a Strong Performer in The Forrester Wave: Streaming Analytics, Q2 2021 report (the report is available to Forrester subscribers or for purchase). Since its evolution from KSQL in 2019, our Apache Kafka^® native stream processing engine ksqlDB has become the go-to analytic technology for many of the hundreds of thousands of organizations already using Apache Kafka for their event streaming needs. By closely coupling event streaming and stream processing, Confluent delivers a complete data-in-motion platform that can be deployed anywhere, for any use case.

“Enterprises that have data-in-motion, and all do, are turning to Kafka (and Confluent) in droves to deliver real-time data,” according to the Forrester Wave. “To provide customers with streaming analytics, Confluent offers ksqlDB that is both a persistent database for streaming data and includes a SQL interface that supports streaming analytics. Confluent’s ksqlDB is built on the same underlying distributed architecture used by the Confluent [Platform or Confluent Cloud] for scaling, performance, security, and other enterprise features.”

Around the world and across industries, customers such as Ticketmaster, Optimove, BAADER, and AO.com are using ksqlDB to transform their businesses with a wide variety of use cases. While these use cases are varied, one thing is consistent among our customers: They know that event streaming and stream analytics need to be designed to work together in a single platform. In adopting our integrated approach, customers benefit from the reduced complexity that comes from managing fewer tools and technologies—a benefit that compounds through our ability to deploy in any customer environment. Additionally, our single platform helps customers improve time to value as they extend their Kafka environments rather than building new capabilities from the ground up.

A unique approach to data in motion and streaming analytics

“Confluent starts with Kafka to bring enterprise streams analytics”
–Forrester

At Confluent, we’re committed to helping our customers build a platform for all of their data-in-motion needs, whether that’s rich, frontend customer experiences or efficient backend operations.

However, too often, vendors force their customers to abandon their current investments, retrain the long-awaited announcement from their employees, and migrate their data environments. Confluent takes the opposite approach. We deliver streaming analytics integrated with Apache Kafka, the world’s most prevalent event streaming technology, creating a complete data-in-motion platform. With a complete (and familiar) platform, we’re able to help our customers deploy Confluent in any environment with the scalability that’s expected from a cloud-native product.

The core value propositions of Confluent’s data-in-motion platform—complete, cloud-native, and everywhere—deliver a unique set of values in the marketplace for streaming analytics customers.

Complete

To make use of streaming analytics, organizations need to successfully tie together event streaming, stream analytics, and applications that activate real-time insights. Confluent’s ksqlDB integrates directly with Kafka to easily add stream analytics to event streaming pipelines. From there, customers can then choose to build applications directly within ksqlDB using simple SQL statements to activate the real-time insights they’ve generated. Alternatively, we have a broad portfolio of fully managed connectors that can drive insights into OSS and partners tools, which, in our opinion, helped yield a score of 5/5 in the partners criterion within the strategy category in the Forrester report.

This complete and integrated approach to data in motion means that our customers can get the most from their existing investments to get up and running with streaming analytics quickly.

Everywhere

Some streaming analytics solutions work in the cloud, and some work on premises. Confluent works wherever our customers’ environments are located. With global availability on AWS, Azure, and Google Cloud, together with a strong offering on premises, Confluent gives you cross-environment access to all of your data, no matter where it resides or originates. With enterprises trending towards a permanent state of multi-cloud and hybrid environments, Confluent offers unification and flexibility for streaming analytics across the new silos public clouds have created.

Cloud Native

Streaming analytics, and data in motion in general, demand cloud-native solutions that are fully managed, serverless, infinitely scalable, and elastic. Unlike many on-premises technologies masquerading as cloud-native services, Confluent has architected its offering to take full advantage of the capabilities offered by public clouds. That means our customers get the familiarity of Kafka and the ease of integration of ksqlDB, all within a true cloud-native service. For on-prem and hybrid customers, our self-managed service inherits all of the work that we’ve done for the cloud, including integrations and a Kubernetes-based operator.

“Confluent offers strengths in data connectors, aggregate analytics, throughput, and deployment options.”
–Forrester

Taken together, we believe Confluent’s approach of delivering a complete, cloud-native streaming analytics offering that allows customers to deploy anywhere and everywhere represents unique value in the market. We’re happy Forrester has recognized us as a strong performer in the Streaming Analytics Wave, and we look forward to building new and exciting features in this space. If you’d like to get started with ksqlDB and Confluent today, you can follow this guide to get started.

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A thank you to our customers, partners, and community

We’re excited to share this recognition with both our customers, our broad partner ecosystem, and the open source community of developers. Without their feedback, input, and contributions, we wouldn’t be able to deliver the value we do today. Thank you!