Hands-on implementation of Confluent Cloud features, including
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Hands-on implementation of Confluent Platform features, including
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Infrastructure-as-Code (IaC) for automated provisioning and resource lifecycle management.
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Building real-time stream processing pipelines using Apache Flink, Kafka Streams and ksqlDB.
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Operationalizing Data Science by implementing real-time predictive models and playing with common ML frameworks, such as TensorFlow or XGBoost. |
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| Project | Description |
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| Confluent Cloud Tableflow & Azure Databricks integration |
| Project | Description | Stars | Forks |
|---|---|---|---|
| csfle | Client-Side Field Level Encryption for Confluent Cloud | ||
| ccloud-clients-oauth | OAuth/OIDC authentication with Azure AD for CC clients | ||
| cfk-clients-oauth | OAuth/OIDC authentication with Azure AD for CP clients using CFK | ||
| Kafka-R-Prediction | Real-time ML model on streaming data |
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Alerting Pipeline with Confluent Cloud Audit Logs Streamlining security and operational alerts using Confluent Cloud audit logs. Read Article β |
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Predictive Analytics at BAADER Case study on building real-time machine learning systems with Kafka and RStudio. Read Article β |
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Data Analytics Pipeline: Kafka & RStudio Technical deep dive into bridging the gap between event streams and statistical computing. Read Article β |

