Confluent offers a cloud-native service for Apache Kafka, Kafka focuses on the movement and storage of large data streams, while Cribl Stream focuses on processing and enrichment of those streams for further analysis. Cribl Stream can be used to shape, filter, and enrich data before it is ingested into a Kafka stream managed by Confluent. This allows for optimized and more relevant data to be streamed in real-time.
Cribl Stream is a data pipeline solution focused on enabling observability, security, and data management. It allows to route, shape, and enrich data from various sources before sending it to multiple destinations.
Confluent offers a managed service for Apache Kafka, which is a distributed event streaming platform used for building real-time data pipelines and streaming applications. Confluent provides additional features and managed services on top of Apache Kafka.
Both Cribl Stream and Confluent can handle data routing and transformation, though Cribl Stream is often more specialized in these areas with a focus on observability data. Both can integrate with a wide range of data sources, but the use cases might differ slightly based on their core strengths.
The community, including the engineering and sales teams, is available on Slack and is very supportive.
Perhaps more flexibility in terms of metrics would be helpful.
The community on Slack is excellent for solving questions and getting ideas.
Confluent is an enterprise-ready, full-scale streaming platform that enhances Apache Kafka.
Confluent has integrated cutting-edge features that are designed to enhance these tasks:
Confluent is a more complete distribution of Kafka in that it enhances the integration possibilities of Kafka by introducing tools for managing and optimizing Kafka clusters while providing methods for making sure the streams are secure. Confluent supports publish-and-subscribe as well as the storing and processing of data within the streams. Kafka is easier to operate and build thanks to Confluent.
Confluent's software is available in three different varieties:
Confluent Advantage Features
Confluent has many valuable key features. Some of the most useful ones include:
Reviews from Real Users
Confluent stands out among its competitors for a number of reasons. Two major ones are its robust enterprise support and its open source option. PeerSpot users take note of the advantages of these features in their reviews:
Ravi B., a solutions architect at a tech services company, writes of the solution, “KSQL is a valuable feature, as is the Kafka Connect framework for connecting to the various source systems where you need not write the code. We get great support from Confluent because we're using the enterprise version and whenever there's a problem, they support us with fine-tuning and finding the root cause.”
Amit S., an IT consultant, notes, “The biggest benefit is that it is open source. You have the flexibility of opting or not opting for enterprise support, even though the tool itself is open source.” He adds, “The second benefit is it's very modern and built on Java and Scala. You can extend the features very well, and it doesn't take a lot of effort to do so.”
Cribl optimizes log collection, data processing, and migration to Splunk Cloud, ensuring efficient data ingestion and management for improved operational efficiency.
Cribl offers seamless log collection directly from cloud sources, allowing users to visually extract necessary data and replay specific events for in-depth analysis. It provides robust management of events, parsing, and enrichment of data, along with effective log size reduction. Cribl is particularly beneficial for migrating enterprise logs, optimizing usage, and reducing costs while streamlining the transition between different log management tools.
What are Cribl's most important features?Cribl is widely implemented in industries requiring extensive data management, such as technology and finance. Users leverage Cribl to handle log collection, processing, and migration efficiently, ensuring smooth operation and effective data analysis. It aids in managing temporary data storage during downtimes and better handling historical data, preventing data loss and allowing extended periods for viewing statistics and monitoring trends.
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