[incubator-streampipes] 08/18: [STREAMPIPES-622] Modify introduction text

[bo...@apache.org]

This is an automated email from the ASF dual-hosted git repository.

bossenti pushed a commit to branch feature/STREAMPIPES-607
in repository https://gitbox.apache.org/repos/asf/incubator-streampipes.git

commit a418be586e64abba2d47a59a294e83c4c3aadc09
Author: Dominik Riemer <do...@gmail.com>
AuthorDate: Wed Nov 9 22:21:54 2022 +0100

    [STREAMPIPES-622] Modify introduction text
---
 README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 8ffe09149..06e681529 100644
--- a/README.md
+++ b/README.md
@@ -63,14 +63,14 @@ StreamPipes is an end-to-end toolbox for the industrial IoT.
 It comes with a rich graphical user interface targeted at non-technical users and provides the following features:
 
 
-* Quickly connect >20 industrial data sources and protocols such as OPC-UA, PLCs, MQTT, REST, Pulsar, Kafka and others
-* Create data harmonization and analytics pipelines using > 100 algorithms and many data sinks to forward data to third-party systems
+* Quickly connect >20 industrial protocols such as OPC-UA, PLCs, MQTT, REST, Pulsar, Kafka and others.
+* Create data harmonization and analytics pipelines using > 100 algorithms and data sinks to forward data to third-party systems.
 * Use the data explorer to visually explore historical data with many widgets tailored for time-series data.
 * A live dashboard to display real-time data from data sources and pipelines, e.g., for shopfloor monitoring.
 
 
-StreamPipes is highly extensible and includes a Java and Python (currently in development phase) SDK to create new 
-pipeline elements and adapters.  
+StreamPipes is highly extensible and includes a Java SDK to create new 
+pipeline elements and adapters. Python support is available in an early development stage - stay tuned!  
 Pipeline elements are standalone microservices that can run anywhere -
 centrally on your server or close at the edge.
 You want to employ your own machine learning model on live data?