This is an automated email from the ASF dual-hosted git repository.
altay pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/beam.git
The following commit(s) were added to refs/heads/master by this push:
new 8837c93d642 Re-word line in Octo case study
new 83f09e70682 Merge pull request #27992 from jrmccluskey/caseStudyCleanup
8837c93d642 is described below
commit 8837c93d6421040e6cdd87d9db90cef64493a14e
Author: Jack McCluskey <[email protected]>
AuthorDate: Mon Aug 14 14:09:36 2023 -0400
Re-word line in Octo case study
---
website/www/site/content/en/case-studies/octo.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/website/www/site/content/en/case-studies/octo.md
b/website/www/site/content/en/case-studies/octo.md
index b7fcf824ee6..9ab6fd4b00c 100644
--- a/website/www/site/content/en/case-studies/octo.md
+++ b/website/www/site/content/en/case-studies/octo.md
@@ -64,7 +64,7 @@ In this spotlight, OCTO’s Data Architect, Godefroy Clair, and
Data Engineers,
OCTO’s Client, a prominent grocery and convenience store retailer with tens of
thousands of stores across several countries, relies on an internal web app to
empower store managers with informed purchasing decisions and effective store
management. The web app provides access to crucial product details, stock
quantities, pricing, promotions, and more, sourced from various internal data
stores, platforms, and systems.
-Before 2022, the Client utilized [Cloud
Composer](https://cloud.google.com/composer) for orchestrating batch pipelines
that consolidated and processed data from Cloud Storage files and Pub/Sub
messages and wrote the output to BigQuery. However, with most source data
uploaded at night, batch processing posed challenges in meeting SLAs and
providing the most recent information to store managers before store opening.
Moreover, incorrect or missing data uploads required cumbersome database s [...]
+Before 2022, the Client utilized an orchestration engine for orchestrating
batch pipelines that consolidated and processed data from Cloud Storage files
and Pub/Sub messages and wrote the output to BigQuery. However, with most
source data uploaded at night, batch processing posed challenges in meeting
SLAs and providing the most recent information to store managers before store
opening. Moreover, incorrect or missing data uploads required cumbersome
database state reverts, involving a su [...]
To address these issues, the Client sought OCTO's expertise to transform their
data ecosystem and migrate their core use case from batch to streaming. The
objectives included faster data processing, ensuring the freshest data in the
web app, simplifying pipeline and database maintenance, ensuring scalability
and resilience, and efficiently handling spikes in data volumes.
