Data Engineering Weekly

FEBRUARY 2, 2025

Shifting left involves moving data processing upstream, closer to the source, enabling broader access to high-quality data through well-defined data products and contracts, thus reducing duplication, enhancing data integrity, and bridging the gap between operational and analytical data domains.

Data Engineer 57

Data Engineer Data Engineering Engineering Data Lake 57

Monte Carlo

AUGUST 8, 2023

The Definitive Guide to Data Validation Testing Data validation testing ensures your data maintains its quality and integrity as it is transformed and moved from its source to its target destination. It’s also important to understand the limitations of data validation testing.

Data Validation 52

Data Validation Data Pipeline SQL Data 52

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.

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52

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 Warehouse 98

Data Warehouse Metadata Data Certification 98

Wayne Yaddow

FEBRUARY 18, 2025

By understanding the differences between transformation and conversion testing and the unique strengths of each tool, organizations can design more reliable, efficient, and scalable data validation frameworks to support their data pipelines.

Data Pipeline 52

Data Pipeline SQL Raw Data Python 52

Airbnb Tech

NOVEMBER 24, 2020

During this transformation, Airbnb experienced the typical growth challenges that most companies do, including those that affect the data warehouse. In the first post of this series, we shared an overview of how we evolved our organization and technology standards to address the data quality challenges faced during hyper growth.

Data Warehouse 52

Data Warehouse Certification Data Pipeline Data 52

DataKitchen

SEPTEMBER 21, 2023

Data in Place refers to the organized structuring and storage of data within a specific storage medium, be it a database, bucket store, files, or other storage platforms. In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers.

Raw Data 69

Raw Data Data Business Intelligence Data Engineering 69