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new 9160abff new blogpost wayangVStrino (#38)
9160abff is described below
commit 9160abff839e3175612f4712df42d51e116b075a
Author: Zoi Kaoudi <[email protected]>
AuthorDate: Fri Mar 8 12:23:29 2024 +0100
new blogpost wayangVStrino (#38)
* new blogpost wayangVStrino
* added as author
* minor addition
---
blog/2024-03-08-wayang-vs-presto.md | 54 ++++++++++++++++++++++++++++++++
blog/authors.yml | 5 +++
static/img/blog/trino-architecture.pdf | Bin 0 -> 307755 bytes
static/img/blog/wayang-architecture.pdf | Bin 0 -> 1065940 bytes
static/img/blog/wayang-with-trino.pdf | Bin 0 -> 854550 bytes
5 files changed, 59 insertions(+)
diff --git a/blog/2024-03-08-wayang-vs-presto.md
b/blog/2024-03-08-wayang-vs-presto.md
new file mode 100644
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--- /dev/null
+++ b/blog/2024-03-08-wayang-vs-presto.md
@@ -0,0 +1,54 @@
+---
+slug: website_update
+title: Website updated
+authors: [zkaoudi]
+tags: [wayang, presto, trino]
+---
+
+# Apache Wayang vs. Presto/Trino
+
+We have been asked several times about the difference between Apache Wayang
and Presto/Trino. In this blog post, we will clarify the main differences and
how they impact various applications and use cases.
+
+<!--truncate-->
+## Key Distinctions
+
+Trino/Presto is a **query engine** for **distributed SQL query processing**.
It is composed of a coordinator and multiple workers. The coordinator consists
of a query optimizer and a scheduler, while the workers are responsible for
performing the necessary query processing. Data is fetched from external
systems via a Connector API, i.e., Trino/Presto supports [multiple data
sources](https://trino.io/ecosystem/data-source). Notably,query processing is
is conducted exclusively by Trino/Pre [...]
+
+
+In contrast, Wayang is a **middleware** for **integrating diverse data
platforms**, including but not limited to query engines. This means that Wayang
leverages the processing capabilities of the underlying data platforms to
complete a given job, with no actual query processing taking place within
Wayang itself.
+
+Below you can graphically see the difference between the two systems. Note
that not all available data sources or data platforms are illustrated for
simplicity reasons.
+
+<img width="75%" alt="Wayang" src="/img/blog/wayang-architecture.pdf" />
+<br/>
+
+<br/>
+<img width="75%" alt="Trino" src="/img/blog/trino-architecture.pdf" />
+<br/>
+
+
+I hope this makes it clear now. <br/>
+In fact, Trino can be easily plugged to Wayang as a platform and be seamlessly
integrated with other data platforms, as shown below.
+
+<img width="75%" alt="Trino" src="/img/blog/wayang-with-trino.pdf" />
+
+## What are the advantages of using Wayang?
+
+Wayang brings several benefits thanks to its integration layer:
+
+* Seamless integration of SQL query engines with ML and other data analysis
systems within a single job, eliminating the need to materialize intermediate
results.
+
+
+* Users are freed from the task of specifying the query engines for an
application if they desire. By submitting their Wayang job, the cross-platform
optimizer can automatically determine the best data platform to use for
improved performance or cost savings.
+
+
+* Wayang facilitates cross-platform data processing by utilizing multiple data
platforms to execute a query for a single job, optimizing performance and cost
efficiency.
+
+* Data does not have to be transferred outside their original location.
+
+## Conclusion
+
+Trino is a distributed SQL query engine which performs all the query
processing of an input SQL query in a distributed manner. Wayang, on the other
hand, is a data platform integrator which can automatically determine which
data platform(s) is best suited for an application.
+
+
+Author: [zkaoudi](https://github.com/zkaoudi)
diff --git a/blog/authors.yml b/blog/authors.yml
index 78f3a5ad..9b97898b 100644
--- a/blog/authors.yml
+++ b/blog/authors.yml
@@ -8,3 +8,8 @@ kamir:
title: Apache Committer
url: https://github.com/kamir
image_url: https://avatars.githubusercontent.com/u/1241122?v=4
+zkaoudi:
+ name: Zoi Kaoudi
+ title: PPMC Apache Wayang
+ url: https://github.com/zkaoudi
+ image_url: https://avatars.githubusercontent.com/zkaoudi
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