#124 bump version

README.md

@@ -13,15 +13,15 @@ DDQ is a small library for checking constraints on Spark data structures. It can
 DDQ is available as a [spark package](http://spark-packages.org/package/FRosner/drunken-data-quality). You can add it to your spark-shell, spark-submit or pyspark using the `--packages` command line option:
 
 ```sh
-spark-shell --packages FRosner:drunken-data-quality:4.0.0-s_2.10
+spark-shell --packages FRosner:drunken-data-quality:4.1.0-s_2.11
 ```
 
 ### Python API
 
 DDQ also comes with a Python API. It is available via the Python Package Index, so you have to install it once using `pip`:
 
 ```
-pip install pyddq==4.0.0
+pip install pyddq==4.1.0
 ```
 
 ### Project Dependency [![Latest Release](https://img.shields.io/github/tag/FRosner/drunken-data-quality.svg?label=JitPack)](https://jitpack.io/#FRosner/drunken-data-quality)
@@ -150,7 +150,7 @@ assert(constraintResults(uniqueKeyConstraint).isInstanceOf[ConstraintSuccess])
 In order to use the Python API, you have to start PySpark with the DDQ jar added. Unfortunately, using the `--packages` way is [not working in Spark < 2.0](https://issues.apache.org/jira/browse/SPARK-5185).
 
 ```
-pyspark --driver-class-path drunken-data-quality_2.10-x.y.z.jar
+pyspark --driver-class-path drunken-data-quality_2.11-x.y.z.jar
 ```
 
 Then you can create a dummy dataframe and run a few checks.

build.sbt

@@ -1,6 +1,6 @@
 organization  := "de.frosner"
 
-version       := "4.1.0-SNAPSHOT"
+version       := "4.1.0"
 
 name          := "drunken-data-quality"