Data Pipeline Data Validation Data Warehouse Metadata 
Christophe Blefari
MARCH 15, 2024
Attributing Snowflake cost to whom it belongs — Fernando gives ideas about metadata management to attribute better Snowflake cost. Understand how BigQuery inserts, deletes and updates — Once again Vu took time to deep dive into BigQuery internal, this time to explain how data management is done. This is Croissant.
Monte Carlo
JANUARY 25, 2023
In this article, Chad Sanderson , Head of Product, Data Platform , at Convoy and creator of Data Quality Camp , introduces a new application of data contracts: in your data warehouse. In the last couple of posts , I’ve focused on implementing data contracts in production services.
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Data Engineering Weekly
MAY 16, 2023
I won’t bore you with the importance of data quality in the blog. Instead, Let’s examine the current data pipeline architecture and ask why data quality is expensive. Instead of looking at the implementation of the data quality frameworks, Let's examine the architectural patterns of the data pipeline.
Ascend.io
AUGUST 31, 2023
Still, these methods have been overshadowed by EtLT — the predominant approach reshaping today’s data landscape. In this article, we assess: The role of the data warehouse on one hand, and the data lake on the other; The features of ETL and ELT in these two architectures; The evolution to EtLT; The emerging role of data pipelines.
Databand.ai
AUGUST 30, 2023
Each type of tool plays a specific role in the DataOps process, helping organizations manage and optimize their data pipelines more effectively. Poor data quality can lead to incorrect or misleading insights, which can have significant consequences for an organization. In this article: Why Are DataOps Tools Important?
Airbnb Tech
NOVEMBER 28, 2023
However, for all of our uncertified data, which remained the majority of our offline data, we lacked visibility into its quality and didn’t have clear mechanisms for up-leveling it. How could we scale the hard-fought wins and best practices of Midas across our entire data warehouse?
Data Engineering Weekly
MARCH 10, 2024
Pradheep Arjunan - Shared insights on AZ's journey from on-prem to the cloud data warehouses. Google: Croissant- a metadata format for ML-ready datasets Google Research introduced Croissant, a new metadata format designed to make datasets ML-ready by standardizing the format, facilitating easier use in machine learning projects.
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