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Posted to commits@devlake.apache.org by ly...@apache.org on 2024/03/22 07:22:10 UTC

(incubator-devlake-website) 03/07: Small fix

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

lynwee pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/incubator-devlake-website.git

commit 33f8ff9eca4a99007a3893fb13775759b6cdfddf
Author: Jochum <jo...@xebia.com>
AuthorDate: Fri Mar 15 10:32:57 2024 +0100

    Small fix
---
 blog/2024-03-15-DevLake-Playground-How-to-explore-your-data/index.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/blog/2024-03-15-DevLake-Playground-How-to-explore-your-data/index.md b/blog/2024-03-15-DevLake-Playground-How-to-explore-your-data/index.md
index 8fb3af205b..0a6e6f204a 100644
--- a/blog/2024-03-15-DevLake-Playground-How-to-explore-your-data/index.md
+++ b/blog/2024-03-15-DevLake-Playground-How-to-explore-your-data/index.md
@@ -16,7 +16,7 @@ A Jupyter notebook combines Python code and documentation, which you can easily
 The benefits of these Jupyter notebooks as opposed to Grafana are:
 - Grafana is limited to SQL queries for gathering and transforming data, and visualizations for data tables.
 - Python (code) offers more flexibility in transforming the data and can easily provide feedback on intermediate steps.
-- The playground also supports more visualization types; for example (see the first use case below,) the data is structured as a graph, which we visualized with Graphviz.
+- The playground also supports more visualization types; for example (see the first use case below,) when the data is structured as a graph, we can visualize it with Graphviz.
 
 
 ## Use cases