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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 <thejackmcclus...@gmail.com> 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.