anchor.diff:  Since the https://github.com/debezium/debezium/commit/0a99ed67cd8f74d6f451b0a2d3809e23127e4698[initial commit] in November 2015,
   the Debezium community has worked tirelessly to realize the vision of building a comprehensive open-source low-latency platform for change data capture for a variety of databases.
   
  -Within those four years, Debezium's feature set has tremendously grown: stable, highly configurable CDC connectors for MySQL, Postgres, MongoDB and SQL Server, incubating connectors for Apache Cassandra and Oracle, facilities for transforming and routing messages, support for design patterns such as the outbox pattern and much more.
  +Within those four years, Debezium's feature set has grown tremendously: stable, highly configurable CDC connectors for MySQL, Postgres, MongoDB and SQL Server, incubating connectors for Apache Cassandra and Oracle, facilities for transforming and routing messages, support for design patterns such as the outbox pattern and much more.
   A very active, open and welcoming community of users, contributors and committers has formed around the project.
   Debezium is deployed to production at lots of organizations from all kinds of industries,
   partly with huge installations, using hundreds of connectors to stream data changes out of thousands of databases.
  
positive.code: setting up such integrations can be done without any coding,
  just by means of configuration.
  
  But there's many other use cases of CDC which go beyond just moving data from A to B.
  When adding stream processing into the picture, e.g. via Kafka Streams or Apache Flink,
  CDC enables you to run https://github.com/debezium/debezium-examples/tree/master/kstreams-live-update[time-windowed streaming queries], continuously updated as your data changes
  ("what's the aggregated order revenue per category within the last hour").
  You can use CDC to link:/blog/2019/10/01/audit-logs-with-change-data-capture-and-stream-processing/[build audit logs] of your data,
  telling who did change which data items at which time.
  Or update denormalized views of your data, for the sake of efficient data retrieval, adhering to the CQRS pattern.
  
  Finally, CDC also can play a vital role in microservices architectures;
  exchanging data between services and keeping local views of data owned by other services achieves a higher independence, without having to rely on synchronous API calls.
  One particularly interesting approach in this context is the link:/blog/2019/02/19/reliable-microservices-data-exchange-with-the-outbox-pattern/[outbox pattern],
  which is link:/documentation/reference/1.0/configuration/outbox-event-router.html[supported well] by Debezium.
  In case you don't start on the green field (who ever does?),
  CDC can be used to implement the strangler pattern for moving from a monolithic design to microservices.
  
  
negative.code: 
  Now, let's talk a little bit about the contents of the 1.0 release.
  
  This version continues the effort we began in link:/blog/2019/10/02/debezium-0-10-0-final-released/[0.10] to make sure the emitted event structures and configuration options of the connectors are correct and consistent.
  While we've always been very careful to ensure a smooth upgrading experience, you can expect even more stability in this regard going forward after the 1.0 release.
  
  We've expanded the test coverage of databases (Postgres 12, SQL Server 2019),
  