The CDC has announced a significant reduction in the childhood vaccine schedule, now recommending vaccines for only 11 diseases instead of 18, aligning more closely with Denmark's approach. Health experts express concern that this change may undermine public trust and lead to lower vaccination rates, especially amid ongoing debates about vaccine safety and efficacy.

The article discusses the transition from Timescale to ClickHouse using ClickPipe for Change Data Capture (CDC). It highlights the advantages of ClickHouse in terms of performance and scalability for time-series data, making it a strong alternative for users seeking more efficient data processing solutions.

Apache Flink CDC 3.5.0 has been released, introducing new pipeline connectors for Apache Fluss and PostgreSQL, alongside enhancements in schema evolution and transform support. The update also addresses various issues in multi-table synchronization and improves the overall usability of the framework. Feedback from the community is encouraged through mailing lists and JIRA.

Managing replication slots in Postgres is crucial to prevent WAL bloat and ensure efficient Change Data Capture (CDC) processes. Best practices include using the pgoutput plug-in, defining maximum replication slot sizes, enabling heartbeats for idle databases, and utilizing table-level publications and filters to optimize resource usage. These strategies help maintain database performance and avoid operational issues.

This tutorial guides users through setting up a complete Change Data Capture (CDC) pipeline using Debezium and Kafka Connect to stream changes from a PostgreSQL database. It covers the prerequisites, infrastructure setup with Docker, PostgreSQL configuration, connector registration, and observing change events in Kafka topics.

Mitzi Morris discusses the transition from Stan to JAX in statistical modeling, highlighting the benefits of JAX's flexibility and efficiency in coding. The post also touches on the integration of JAX in projects like wastewater-informed forecasting at the CDC and contrasts the features of JAX with those of Stan.

Best practices for implementing Flink CDC via YAML in Realtime Compute for Apache Flink are discussed, highlighting its capabilities, use cases, and enterprise-grade features. The article details how users can easily build data pipelines for real-time data synchronization with minimal coding, covering aspects like schema evolution, data transformations, and monitoring metrics.

Iceberg format v3 introduces deletion vectors that enhance the efficiency of Change Data Capture (CDC) workflows by allowing row-level deletions without rewriting entire files. The article benchmarks the performance improvements of Iceberg v3 over v2 during MERGE operations, demonstrating significant gains in speed and cost-effectiveness for large-scale data updates and deletes. Key innovations include reduced I/O and improved query acceleration through the use of compact binary representations stored in Puffin files.

Apache Flink CDC 3.4.0 has been released, featuring a new pipeline connector for Apache Iceberg and support for batch execution mode. The update also includes optimizations in schema evolution, transform enhancements, and various improvements across MySQL and MongoDB CDC connectors.