Repo maintenance - Outdated links and minor things

MartijnVisser · MartijnVisser · commit 30765cec84d3 · 2021-07-19T17:35:04.000+02:00
diff --git a/.gitignore b/.gitignore
@@ -0,0 +1 @@
+.idea
diff --git a/README.md b/README.md
@@ -53,6 +53,6 @@ Learn more about Flink at https://flink.apache.org/.
 
 ## License 
 
-Copyright © 2020 Ververica GmbH
+Copyright © 2020-2021 Ververica GmbH
 
 Distributed under Apache License, Version 2.0.
diff --git a/aggregations-and-analytics/01/01_group_by_window.md b/aggregations-and-analytics/01/01_group_by_window.md
@@ -2,7 +2,7 @@
 
 > :bulb: This example will show how to aggregate time-series data in real-time using a `TUMBLE` window.
 
-The source table (`server_logs`) is backed by the [`faker` connector](https://github.com/knaufk/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
+The source table (`server_logs`) is backed by the [`faker` connector](https://flink-packages.org/packages/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
 
 Many streaming applications work with time-series data.
 To count the number of `DISTINCT` IP addresses seen each minute, rows need to be grouped based on a [time attribute](https://docs.ververica.com/user_guide/sql_development/table_view.html#time-attributes).
diff --git a/aggregations-and-analytics/02/02_watermarks.md b/aggregations-and-analytics/02/02_watermarks.md
@@ -4,7 +4,7 @@
 
 > :bulb: This example will show how to use `WATERMARK`s to work with timestamps in records. 
 
-The source table (`doctor_sightings`) is backed by the [`faker` connector](https://github.com/knaufk/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
+The source table (`doctor_sightings`) is backed by the [`faker` connector](https://flink-packages.org/packages/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
 
 The [previous recipe](../01/01_group_by_window.md) showed how a `TUMBLE` group window makes it simple to aggregate time-series data.	 
 
@@ -13,7 +13,7 @@ As different versions of the Doctor travel through time, various people log thei
 We want to track how many times each version of the Doctor is seen each minute. 	 
 Unlike the previous recipe, these records have an embedded timestamp we need to use to perform our calculation. 	 
 
-More often than not, most data will come with embedded timestamps that we want to use for our time series calculations.	We call this timestamp an [event-time attribute](https://ci.apache.org/projects/flink/flink-docs-stable/learn-flink/streaming_analytics.html#event-time-and-watermarks).	 
+More often than not, most data will come with embedded timestamps that we want to use for our time series calculations.	We call this timestamp an [event-time attribute](https://ci.apache.org/projects/flink/flink-docs-stable/docs/learn-flink/streaming_analytics/#event-time-and-watermarks).	 
   
 Event time represents when something actually happened in the real world.
 And it is unique because it is quasi-monotonically increasing; we generally see things that happened earlier before seeing things that happen later. Of course, data will never be perfectly ordered (systems go down, networks are laggy, doctor sighting take time to postmark and mail), and there will be some out-of-orderness in our data. 	 
diff --git a/aggregations-and-analytics/03/03_group_by_session_window.md b/aggregations-and-analytics/03/03_group_by_session_window.md
@@ -2,7 +2,7 @@
 
 > :bulb: This example will show how to aggregate time-series data in real-time using a `SESSION` window.
 
-The source table (`server_logs`) is backed by the [`faker` connector](https://github.com/knaufk/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
+The source table (`server_logs`) is backed by the [`faker` connector](https://flink-packages.org/packages/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
 
 #### What are Session Windows?
 
diff --git a/aggregations-and-analytics/04/04_over.md b/aggregations-and-analytics/04/04_over.md
@@ -2,7 +2,7 @@
 
 > :bulb: This example will show how to calculate an aggregate or cumulative value based on a group of rows using an `OVER` window. A typical use case are rolling aggregations.
 
-The source table (`temperature_measurements`) is backed by the [`faker` connector](https://github.com/knaufk/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
+The source table (`temperature_measurements`) is backed by the [`faker` connector](https://flink-packages.org/packages/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
 
 OVER window aggregates compute an aggregated value for every input row over a range of ordered rows. 
 In contrast to GROUP BY aggregates, OVER aggregates do not reduce the number of result rows to a single row for every group. 
diff --git a/aggregations-and-analytics/05/05_top_n.md b/aggregations-and-analytics/05/05_top_n.md
@@ -4,7 +4,7 @@
 
 > :bulb: This example will show how to continuously calculate the "Top-N" rows based on a given attribute, using an `OVER` window and the `ROW_NUMBER()` function.
 
-The source table (`spells_cast`) is backed by the [`faker` connector](https://github.com/knaufk/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
+The source table (`spells_cast`) is backed by the [`faker` connector](https://flink-packages.org/packages/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
 
 The Ministry of Magic tracks every spell a wizard casts throughout Great Britain and wants to know every wizard's Top 2 all-time favorite spells. 
 
diff --git a/aggregations-and-analytics/07/07_chained_windows.md b/aggregations-and-analytics/07/07_chained_windows.md
@@ -2,7 +2,7 @@
 
 > :bulb: This example will show how to efficiently aggregate time series data on two different levels of granularity.
 
-The source table (`server_logs`) is backed by the [`faker` connector](https://github.com/knaufk/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
+The source table (`server_logs`) is backed by the [`faker` connector](https://flink-packages.org/packages/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
 
 Based on our `server_logs` table we would like to compute the average request size over one minute **as well as five minute (event) windows.** 
 For this, you could run two queries, similar to the one in [Aggregating Time Series Data](../01/01_group_by_window.md). 
@@ -11,7 +11,7 @@ At the end of the page is the script and resulting JobGraph from this approach.
 In the main part, we will follow a slightly more efficient approach that chains the two aggregations: the one-minute aggregation output serves as the five-minute aggregation input.
 
 We then use a [Statements Set](../../foundations/08/08_statement_sets.md) to write out the two result tables. 
-To keep this example self-contained, we use two tables of type `blackhole` instead of `kafka`, `filesystem`, or any other [connectors](https://ci.apache.org/projects/flink/flink-docs-release-1.12/dev/table/connectors/). 
+To keep this example self-contained, we use two tables of type `blackhole` instead of `kafka`, `filesystem`, or any other [connectors](https://ci.apache.org/projects/flink/flink-docs-stable/docs/connectors/table/overview/). 
 
 ## Script
 
diff --git a/aggregations-and-analytics/08/08_match_recognize.md b/aggregations-and-analytics/08/08_match_recognize.md
@@ -41,7 +41,7 @@ BASIC AS BASIC.type = 'basic');
 
 ## Script
 
-The source table (`subscriptions`) is backed by the [`faker` connector](https://github.com/knaufk/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
+The source table (`subscriptions`) is backed by the [`faker` connector](https://flink-packages.org/packages/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
 
 ```sql
 CREATE TABLE subscriptions ( 
diff --git a/foundations/04/04_where.md b/foundations/04/04_where.md
@@ -2,7 +2,7 @@
 
 > :bulb: This example will show how to filter server logs in real-time using a standard `WHERE` clause.
 
-The table it uses, `server_logs`,  is backed by the [`faker` connector](https://github.com/knaufk/flink-faker) which continuously generates rows in memory based on Java Faker expressions and is convenient for testing queries. 
+The table it uses, `server_logs`,  is backed by the [`faker` connector](https://flink-packages.org/packages/flink-faker) which continuously generates rows in memory based on Java Faker expressions and is convenient for testing queries. 
 As such, it is an alternative to the built-in `datagen` connector used for example in [the first recipe](../01/01_create_table.md).
 
 You can continuously filter these logs for those requests that experience authx issues with a simple `SELECT` statement with a `WHERE` clause filtering on the auth related HTTP status codes. 
diff --git a/foundations/05/05_group_by.md b/foundations/05/05_group_by.md
@@ -2,9 +2,9 @@
 
 > :bulb: This example will show how to aggregate server logs in real-time using the standard `GROUP BY` clause.
 
-The source table (`server_logs`) is backed by the [`faker` connector](https://github.com/knaufk/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
+The source table (`server_logs`) is backed by the [`faker` connector](https://flink-packages.org/packages/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
 
-To count the number of logs received per browser for each status code _over time_, you can combine the `COUNT` aggregate function with a `GROUP BY` clause. Because the `user_agent` field contains a lot of information, you can extract the browser using the built-in [string function](https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/table/functions/systemFunctions.html#string-functions) `REGEXP_EXTRACT`.
+To count the number of logs received per browser for each status code _over time_, you can combine the `COUNT` aggregate function with a `GROUP BY` clause. Because the `user_agent` field contains a lot of information, you can extract the browser using the built-in [string function](https://ci.apache.org/projects/flink/flink-docs-stable/docs/dev/table/functions/systemfunctions/#string-functions) `REGEXP_EXTRACT`.
 
 A `GROUP BY` on a streaming table produces an updating result, so you will see the aggregated count for each browser continuously changing as new rows flow in.
 
diff --git a/joins/03/03_kafka_join.md b/joins/03/03_kafka_join.md
@@ -67,7 +67,7 @@ ON t.currency_code = c.currency_code;
     <summary>Data Generators</summary>
 
 The two topics are populated using a Flink SQL job, too. 
-We use the  [`faker` connector](https://github.com/knaufk/flink-faker) to generate rows in memory based on Java Faker expressions and write those to the respective Kafka topics.  
+We use the  [`faker` connector](https://flink-packages.org/packages/flink-faker) to generate rows in memory based on Java Faker expressions and write those to the respective Kafka topics.  
 
 ### ``currency_rates`` Topic
 
diff --git a/joins/04/04_lookup_joins.md b/joins/04/04_lookup_joins.md
@@ -14,7 +14,7 @@ In this example, you will look up reference user data stored in MySQL to flag su
 
 ## Script
 
-The source table (`subscriptions`) is backed by the [`faker` connector](https://github.com/knaufk/flink-faker), which continuously generates rows in memory based on Java Faker expressions. The `users` table is backed by an existing MySQL reference table using the [JDBC connector](https://ci.apache.org/projects/flink/flink-docs-stable/dev/table/connectors/jdbc.html).
+The source table (`subscriptions`) is backed by the [`faker` connector](https://flink-packages.org/packages/flink-faker), which continuously generates rows in memory based on Java Faker expressions. The `users` table is backed by an existing MySQL reference table using the [JDBC connector](https://ci.apache.org/projects/flink/flink-docs-stable/dev/table/connectors/jdbc.html).
 
 ```sql
 CREATE TABLE subscriptions ( 
diff --git a/joins/05/05_star_schema.md b/joins/05/05_star_schema.md
@@ -112,7 +112,7 @@ ON t.destination_station_key = ds.station_key
     <summary>Data Generators</summary>
 
 The four topics are populated with Flink SQL jobs, too.
-We use the  [`faker` connector](https://github.com/knaufk/flink-faker) to generate rows in memory based on Java Faker expressions and write those to the respective Kafka topics.  
+We use the  [`faker` connector](https://flink-packages.org/packages/flink-faker) to generate rows in memory based on Java Faker expressions and write those to the respective Kafka topics.  
 
 ### ``train_activities`` Topic
 
diff --git a/other-builtin-functions/01/01_date_time.md b/other-builtin-functions/01/01_date_time.md
@@ -2,7 +2,7 @@
 
 > :bulb: This example will show how to use [built-in date and time functions](https://ci.apache.org/projects/flink/flink-docs-stable/dev/table/functions/systemFunctions.html#temporal-functions) to manipulate temporal fields.
 
-The source table (`subscriptions`) is backed by the [`faker` connector](https://github.com/knaufk/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
+The source table (`subscriptions`) is backed by the [`faker` connector](https://flink-packages.org/packages/flink-faker), which continuously generates rows in memory based on Java Faker expressions.
 
 #### Date and Time Functions
 
@@ -22,7 +22,7 @@ Assume you have a table with service subscriptions and that you want to continuo
 
 * `CURRENT_TIMESTAMP`: returns the current SQL timestamp (UTC)
 
-For a complete list of built-in date and time functions, check the Flink [documentation](https://ci.apache.org/projects/flink/flink-docs-release-1.12/dev/table/functions/systemFunctions.html#temporal-functions).
+For a complete list of built-in date and time functions, check the Flink [documentation](https://ci.apache.org/projects/flink/flink-docs-stable/docs/dev/table/functions/systemfunctions/#temporal-functions).
 
 > As an exercise, you can try to reproduce the same filtering condition using `TIMESTAMPADD` instead.
 
diff --git a/other-builtin-functions/02/02_union-all.md b/other-builtin-functions/02/02_union-all.md
@@ -2,7 +2,7 @@
 
 > :bulb: This example will show how you can use the set operation `UNION ALL` to combine several streams of data.
 
-See [our documentation](https://ci.apache.org/projects/flink/flink-docs-master/docs/dev/table/sql/queries/#set-operations)
+See [our documentation](https://ci.apache.org/projects/flink/flink-docs-stable/docs/dev/table/sql/queries/set-ops/)
 for a full list of fantastic set operations Apache Flink supports.
 
 
@@ -12,7 +12,7 @@ The examples assumes you are building an application that is tracking visits :fo
 There are three sources of visits. The universe of Rick and Morty, the very real world of NASA and such, 
 and the not so real world of Hitchhikers Guide To The Galaxy.
 
-All three tables are `unbounded` and backed by the [`faker` connector](https://github.com/knaufk/flink-faker).
+All three tables are `unbounded` and backed by the [`faker` connector](https://flink-packages.org/packages/flink-faker).
 
 All sources of tracked visits have the `location` and `visit_time` in common. Some have `visitors`, some have
 `spacecrafts` and one has both.
diff --git a/udfs/01/01_python_udfs.md b/udfs/01/01_python_udfs.md
@@ -37,7 +37,7 @@ For detailed instructions on how to then make the Python file available as a UDF
 
 #### SQL
 
-The source table (`temperature_measurements`) is backed by the [`faker` connector](https://github.com/knaufk/flink-faker), which continuously generates rows in memory based on Java Faker expressions. 
+The source table (`temperature_measurements`) is backed by the [`faker` connector](https://flink-packages.org/packages/flink-faker), which continuously generates rows in memory based on Java Faker expressions. 
 
 ```sql
 --Register the Python UDF using the fully qualified 
