Merge pull request #45 from dqops/1.10.1

1.10.1

.run/dqo run.run.xml

@@ -5,7 +5,7 @@
       <option name="region" />
       <option name="useCurrentConnection" value="false" />
     </extension>
-    <option name="JAR_PATH" value="$PROJECT_DIR$/dqops/target/dqo-dqops-1.10.0.jar" />
+    <option name="JAR_PATH" value="$PROJECT_DIR$/dqops/target/dqo-dqops-1.10.1.jar" />
     <option name="VM_PARAMETERS" value="-XX:MaxRAMPercentage=60.0 --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED" />
     <option name="PROGRAM_PARAMETERS" value="--server.port=8888 --dqo.python.debug-mode=silent" />
     <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />

CHANGELOG.md

@@ -1,11 +1,5 @@
-# 1.9.1
+# 1.10.1
 
-* Data lineage editor suggests similar source and target tables
-* Fixes in the management of tabs
-* ClickHouse connector
-* Teradata connector
-* Small UI fixes
-* Performance improvements in the anomaly detection code
-* Other performance optimizations
-* Run Python rules in parallel to speed up anomaly detection when tables have a different number of enabled checks
+* Small UI fixes to open pages directly from an URL.
+* Fix problems when installing on Windows using pip, when Python was installed from Windows Store and uses a deeply nested folder structure
 

VERSION

@@ -1 +1 @@
-1.10.0
+1.10.1

distribution/pom.xml

@@ -11,7 +11,7 @@
 
 	<groupId>com.dqops</groupId>
 	<artifactId>dqo-distribution</artifactId>
-    <version>1.10.0</version>  <!-- DQOps Version, do not touch (changed automatically) -->
+    <version>1.10.1</version>  <!-- DQOps Version, do not touch (changed automatically) -->
 	<name>dqo-distribution</name>
 	<description>DQOps Data Quality Operations Center final assembly</description>
 	<packaging>pom</packaging>

distribution/python/dqops/install.py

@@ -62,7 +62,9 @@ def install_dqo(dest: str, dqo_tag: str, dqo_version: str):
     download_to_file(dqo_tag, dqo_version, distribution_local_name)
 
     with zipfile.ZipFile(distribution_local_name, "r") as zip_ref:
-        zip_ref.extractall(dest)
+        os_platform = sys.platform.lower()[0:3]
+        dest_path = ("\\\\?\\" if os_platform == "win" else "") + os.path.abspath(dest)
+        zip_ref.extractall(dest_path)
 
     if os.path.exists(distribution_local_name):
         os.remove(distribution_local_name)

distribution/python/dqops/version.py

@@ -15,8 +15,8 @@
 # limit
 
 # WARNING: the next two lines with the version numbers (VERSION =, PIP_VERSION =) should not be modified manually. They are changed by a maven profile at compile time.
-VERSION = "1.10.0"
-PIP_VERSION = "1.10.0"
+VERSION = "1.10.1"
+PIP_VERSION = "1.10.1"
 GITHUB_RELEASE = "v" + VERSION + ""
 JAVA_VERSION = "17"
 

docs/data-sources/athena.md

@@ -99,7 +99,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
 
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 

docs/data-sources/aws.md

@@ -222,7 +222,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
 
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 

docs/data-sources/azure.md

@@ -251,7 +251,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
 
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 

docs/data-sources/bigquery.md

@@ -79,7 +79,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
 
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 

docs/data-sources/clickhouse.md

@@ -89,7 +89,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
 
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 

docs/data-sources/csv.md

@@ -187,7 +187,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
 
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 

docs/data-sources/databricks.md

@@ -99,7 +99,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
 
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 

docs/data-sources/db2.md

@@ -91,7 +91,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
 
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 

docs/data-sources/deltalake.md

@@ -136,7 +136,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
 
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 

docs/data-sources/gcp.md

@@ -127,7 +127,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
 
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 

docs/data-sources/hana.md

@@ -90,7 +90,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
 
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 

docs/data-sources/iceberg.md

@@ -151,7 +151,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
 
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 

docs/data-sources/json.md

@@ -184,7 +184,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
 
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 

docs/data-sources/mariadb.md

@@ -84,7 +84,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
 
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 

docs/data-sources/mysql.md

@@ -95,7 +95,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
 
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with