The Event Stream Processing Software solutions below are the most common alternatives that users and reviewers compare with Kpow for Apache Kafka®. Other important factors to consider when researching alternatives to Kpow for Apache Kafka® include ease of use and reliability. The best overall Kpow for Apache Kafka® alternative is SAS Viya. Other similar apps like Kpow for Apache Kafka® are Aiven for Apache Kafka, Tray.ai, Apache Kafka, and Confluent. Kpow for Apache Kafka® alternatives can be found in Event Stream Processing Software but may also be in Analytics Platforms or iPaaS Software.
As a cloud-native AI, analytics and data management platform, SAS Viya enables you to scale cost-effectively, increase productivity and innovate faster, backed by trust and transparency. SAS Viya makes it possible to integrate teams and technology enabling all users to work together successfully to turn critical questions into accurate decisions.
Aiven for Apache Kafka is a fully managed streaming platform, deployable in the cloud of your choice. Snap it into your existing workflows with the click of a button, automate away the mundane tasks, and focus on building your core apps.
Apache Kafka is an open-source stream processing platform developed by the Apache Software Foundation written in Scala and Java.
StreamSets DataOps Platform is an end-to-end data engineering platform to design, deploy, operate and optimize data pipelines to deliver continuous data. StreamSets offers a single pane of glass for batch, streaming, CDC, ETL and ML pipelines with built-in data drift protection for full transparency and control across hybrid, on-premise and multi-cloud environments.
Amazon Kinesis Data Streams is a serverless streaming data service that makes it easy to capture, process, and store data streams at any scale.
Ably is a realtime data delivery platform providing developers everything they need to create, deliver and manage complex projects. Ably solves the hardest parts so they don’t have to.
Cloud Dataflow is a fully-managed service for transforming and enriching data in stream (real time) and batch (historical) modes with equal reliability and expressiveness.