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 * The **Flink Operations Playground** (in the `operations-playground` folder) lets you explore and play with Flink's features to manage and operate stream processing jobs. You can witness how Flink recovers a job from a failure, upgrade and rescale a job, and query job metrics. The playground consists of a Flink cluster, a Kafka cluster and an example 
 Flink job. The playground is presented in detail in
-["Flink Operations Playground"](https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/try-flink/flink-operations-playground.html), which is part of the _Try Flink_ section of the Flink documentation.
+["Flink Operations Playground"](https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/try-flink/flink-operations-playground), which is part of the _Try Flink_ section of the Flink documentation.
 
-* The **Table Walkthrough** (in the `table-walkthrough` folder) shows to use the Table API to build an analytics pipeline that reads streaming data from Kafka and writes results to MySQL, along with a real-time dashboard in Grafana. The walkthrough is presented in detail in ["Real Time Reporting with the Table API"](https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/try-flink/table_api.html), which is part of the _Try Flink_ section of the Flink documentation.
+* The **Table Walkthrough** (in the `table-walkthrough` folder) shows to use the Table API to build an analytics pipeline that reads streaming data from Kafka and writes results to MySQL, along with a real-time dashboard in Grafana. The walkthrough is presented in detail in ["Real Time Reporting with the Table API"](https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/try-flink/table_api), which is part of the _Try Flink_ section of the Flink documentation.
 
 * The **PyFlink Walkthrough** (in the `pyflink-walkthrough` folder) provides a complete example that uses the Python API, and guides you through the steps needed to run and manage Pyflink Jobs. The pipeline used in this walkthrough reads data from Kafka, performs aggregations, and writes results to Elasticsearch that are visualized with Kibana. This walkthrough is presented in detail in the [pyflink-walkthrough README](pyflink-walkthrough).