1/14/2024 0 Comments Conduktor kafka 20m series![]() ![]() It may happen when you work with Python or nodejs (both are using librdkafka behind the scene). If you are sure you have configured your connection properly in Conduktor and it works in other tools, make sure you're using the official configuration Java-style, and not the C-style (librdkafka). įAQ Ensure you are using the Java-style configuration See Naming Conventions for Realm Names and Hostnames. Note: It is recommended to have all uppercase realm names. Solution 4: Make the Kerberos realm name all uppercase.Cause 4: The Kerberos realm name is not all uppercase.Solution 3: Synchronize the clocks (or have a system administrator do so).Cause 3: Clock skew - If the time on the KDC and on the client differ significantly (typically 5 minutes), this error can be returned.Solution 2: Consult your Kerberos documentation to generate a new keytab and use that keytab.Cause 2: If you are using the keytab to get the key (e.g., by setting the useKeyTab option to true in the Krb5LoginModule entry in the JAAS login configuration file), then the key might have changed since you updated the keytab.Cause 1: The password entered is incorrect. ![]() This makes it easy to identify and resolve any issues that may arise.Troubleshooting : " KrbException: Pre-authentication information was invalid " ERROR\ It allows users to quickly connect to their clusters with appropriate security, and can control access to ensure your Kafka pipeline remains secure. Conduktor is designed to work seamlessly with Amazon MSK clusters. In addition, Conduktor has features that enable testing of your Kafka applications, monitoring for performance and health, and analysis to optimize performance. The primary tool of the platform is Console, a graphical UI built for Kafka, making it easy to explore your MSK clusters and perform necessary operations. This cloud-native platform provides users with a comprehensive set of tools that help them gain visibility into their clusters and make informed decisions. Fortunately, Amazon MSK now offers users the ability to gain visibility into their clusters with the help of Conduktor. ![]() However, working directly with Kafka is still a pain due to a lack of visibility and the complexity of standard operations. Kafka provides real-time data streaming technology, with Amazon offering the infrastructure you need to harness its power. In the world of big data, visibility into the performance of your Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters is essential for successful and efficient data management. These Engineering teams need tooling to ensure that they’re using Kafka appropriately to deliver the business goals they set out to accomplish. With consumer expectations continuing to rise, engineering teams will continue to adopt Kafka. The Data Masking feature ensures that any sensitive data flowing through Conduktor is only visible to those that have access through role-based access control (RBAC). Conduktor’s Monitoring & Alerting ensures that dev and ops teams are alerted when there is an error in their Clusters and are speaking the same language when troubleshooting. Conduktor also helps Operations teams concerned about the security and reliability of Kafka. Its Testing features enable users to create visual tests and see how data flows through their Kafka pipelines before pushing to production. Conduktor’s most popular feature, Console, gives users an easy-to-use UI to explore critical MSK clusters, topics, schema registries, and more. Conduktor recently launched a partnership with Amazon MSK that helps users gain visibility into their Kafka clusters. Developers working with either open-source Kafka or managed solutions like Amazon MSK still suffer from a lack of visibility into what’s happening inside of their Kafka clusters. Solutions like Amazon MSK, Confluent, and Aiven have removed some of the operational burdens and helped cut down the time it takes to leverage Apache Kafka to deliver real-time applications. Setting up and maintaining Kafka drains significant cycles for both development and operations teams. One challenge with Apache Kafka is the operational overhead it introduces to engineering teams. Apache Kafka is massively scalable with over 80% of the Fortune 100 using it to meet customer needs. To meet consumer expectations, engineering teams have adopted open-source streaming technologies such as Apache Kafka. Protect Kafka: Visualize, Test, and Secure Kafka ClustersĬonsumers today expect applications to have updates in real time. ![]()
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