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.

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

Databand.ai

AUGUST 30, 2023

This includes defining roles and responsibilities related to managing datasets and setting guidelines for metadata management. Data profiling: Regularly analyze dataset content to identify inconsistencies or errors. Additionally, high-quality data reduces costly errors stemming from inaccurate information.

Building 52

Building Data Cleanse Data Governance Datasets 52

Data Engineering Weekly

OCTOBER 30, 2022

I found the blog helpful in understanding the generative model’s historical development and the path forward. link] Sponsored- [New eBook] The Ultimate Data Observability Platform Evaluation Guide Considering investing in a data quality solution? The author explains how to dump the history of blockchains into S3.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

RandomTrees

SEPTEMBER 6, 2020

The architecture is three layered: Database Storage: Snowflake has a mechanism to reorganize the data into its internal optimized, compressed and columnar format and stores this optimized data in cloud storage. This stage handles all the aspects of data storage like organization, file size, structure, compression, metadata, statistics.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Databand.ai

NOVEMBER 4, 2022

If the data includes an old record or an incorrect value, then it’s not accurate and can lead to faulty decision-making. Data content: Are there significant changes in the data profile? Data validation: Does the data conform to how it’s being used? But when the data comes through, we see six columns.

Data 52

Data Data Pipeline Retail Metadata 52

Databand.ai

AUGUST 30, 2023

Validity: Adherence to predefined formats, rules, or standards for each attribute within a dataset. Uniqueness: Ensuring that no duplicate records exist within a dataset. Integrity: Maintaining referential relationships between datasets without any broken links.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 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