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new b2f9c2f67 feat: blog hands on lakehouse (#1403)
b2f9c2f67 is described below
commit b2f9c2f6769a28fe0b0a626770208cc877d3b743
Author: Yang Guo <[email protected]>
AuthorDate: Thu Jul 24 19:36:04 2025 +0800
feat: blog hands on lakehouse (#1403)
---
website/blog/2025-07-23-hands-on-fluss-lakehouse.md | 12 ++++++------
1 file changed, 6 insertions(+), 6 deletions(-)
diff --git a/website/blog/2025-07-23-hands-on-fluss-lakehouse.md
b/website/blog/2025-07-23-hands-on-fluss-lakehouse.md
index e35b8acf9..5374fa903 100644
--- a/website/blog/2025-07-23-hands-on-fluss-lakehouse.md
+++ b/website/blog/2025-07-23-hands-on-fluss-lakehouse.md
@@ -1,6 +1,6 @@
---
slug: hands-on-fluss-lakehouse
-title: "Hands-on Fluss Lakehouse with Paimon S3"
+title: "From Stream to Lake: Hands-On with Fluss Tiering into Paimon on Minio"
authors: [gyang94]
toc_max_heading_level: 5
---
@@ -23,7 +23,7 @@ toc_max_heading_level: 5
limitations under the License.
-->
-# Hands-on Fluss Lakehouse with Paimon S3
+# From Stream to Lake: Hands-On with Fluss Tiering into Paimon on Minio
Fluss stores historical data in a lakehouse storage layer while keeping
real-time data in the Fluss server. Its built-in tiering service continuously
moves fresh events into the lakehouse, allowing various query engines to
analyze both hot and cold data. The real magic happens with Fluss's union-read
capability, which lets Flink jobs seamlessly query both the Fluss cluster and
the lakehouse for truly integrated real-time processing.
@@ -31,9 +31,9 @@ Fluss stores historical data in a lakehouse storage layer
while keeping real-tim
In this hands-on tutorial, we'll walk you through setting up a local Fluss
lakehouse environment, running some practical data operations, and getting
first-hand experience with the complete Fluss lakehouse architecture. By the
end, you'll have a working environment for experimenting with Fluss's powerful
data processing capabilities.
-## Integrate Paimon S3 Lakehouse
+## Integrate with Paimon Minio Lakehouse
-For this tutorial, we'll use **Fluss 0.7** and **Flink 1.20** to run the
tiering service on a local cluster. We'll configure **Paimon** as our lake
format and **S3** as the storage backend. Let's get started:
+For this tutorial, we'll use **Fluss 0.7** and **Flink 1.20** to run the
tiering service on a local cluster. We'll configure **Paimon** as our lake
format on **Minio** as the storage backend. Let's get started:
### Minio Setup
@@ -97,7 +97,7 @@ For this tutorial, we'll use **Fluss 0.7** and **Flink 1.20**
to run the tiering
datalake.paimon.s3.path.style.access: true
```
- This configures Paimon as the datalake format with S3 as the warehouse.
+ This configures Paimon as the datalake format on Minio as the warehouse.
4. Start Fluss
@@ -366,4 +366,4 @@ Now let's dive into some actual data processing. We'll use
the Flink SQL Client
## Summary
-In this guide, we've explored the Fluss lakehouse architecture and set up a
complete local environment with Fluss, Flink, Paimon, and S3. We've walked
through practical examples of data processing that showcase how Fluss
seamlessly integrates real-time and historical data. With this setup, you now
have a solid foundation for experimenting with Fluss's powerful lakehouse
capabilities on your own machine.
\ No newline at end of file
+In this guide, we've explored the Fluss lakehouse architecture and set up a
complete local environment with Fluss, Flink, Paimon, and Minio. We've walked
through practical examples of data processing that showcase how Fluss
seamlessly integrates real-time and historical data. With this setup, you now
have a solid foundation for experimenting with Fluss's powerful lakehouse
capabilities on your own machine.
\ No newline at end of file
