Data Analytics Data Lake Data Quality Testing 
AWS Big Data
MARCH 12, 2024
In recent years, data lakes have become a mainstream architecture, and data quality validation is a critical factor to improve the reusability and consistency of the data. In this post, we provide benchmark results of running increasingly complex data quality rulesets over a predefined test dataset.
AWS Big Data
JUNE 6, 2023
AWS Glue Data Quality allows you to measure and monitor the quality of data in your data repositories. It’s important for business users to be able to see quality scores and metrics to make confident business decisions and debug data quality issues. An AWS Glue crawler crawls the results.
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DataKitchen
JANUARY 25, 2022
Ensuring that data is available, secure, correct, and fit for purpose is neither simple nor cheap. Companies end up paying outside consultants enormous fees while still having to suffer the effects of poor data quality and lengthy cycle time. . When a job is automated, there is little advantage to outsourcing. .
AWS Big Data
DECEMBER 13, 2023
For the past 5 years, BMS has used a custom framework called Enterprise Data Lake Services (EDLS) to create ETL jobs for business users. Manually upgrading, testing, and deploying over 5,000 jobs every few quarters was time consuming, error prone, costly, and not sustainable.
DataKitchen
AUGUST 3, 2021
First-generation – expensive, proprietary enterprise data warehouse and business intelligence platforms maintained by a specialized team drowning in technical debt. Second-generation – gigantic, complex data lake maintained by a specialized team drowning in technical debt. See the pattern?
DataKitchen
OCTOBER 11, 2021
The biggest challenge is broken data pipelines due to highly manual processes. Figure 1 shows a manually executed data analytics pipeline. First, a business analyst consolidates data from some public websites, an SFTP server and some downloaded email attachments, all into Excel.
AWS Big Data
NOVEMBER 9, 2023
A modern data platform entails maintaining data across multiple layers, targeting diverse platform capabilities like high performance, ease of development, cost-effectiveness, and DataOps features such as CI/CD, lineage, and unit testing. AWS Glue – AWS Glue is used to load files into Amazon Redshift through the S3 data lake.
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