datapine

SEPTEMBER 29, 2022

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.

Data Quality 241

Data Quality Metrics Data-driven Management 241

AWS Big Data

NOVEMBER 9, 2023

In this post, we delve into a case study for a retail use case, exploring how the Data Build Tool (dbt) was used effectively within an AWS environment to build a high-performing, efficient, and modern data platform. It does this by helping teams handle the T in ETL (extract, transform, and load) processes.

Data Warehouse 103

Data Warehouse Testing Data Quality Reporting 103

DataRobot Blog

MARCH 10, 2022

The first step in building a model that can predict machine failure and even recommend the next best course of action is to aggregate, clean, and prepare data to train against. This task may require complex joins, aggregations, filtering, window functions, and many other data transformations against extremely large-scale data sets.

Manufacturing 52

Manufacturing IoT Machine Learning Forecasting 52

AWS Big Data

MARCH 3, 2023

It has been well published since the State of DevOps 2019 DORA Metrics were published that with DevOps, companies can deploy software 208 times more often and 106 times faster, recover from incidents 2,604 times faster, and release 7 times fewer defects. Finally, data integrity is of paramount importance.

Software 52

Software Data Lake Testing Cost-Benefit 52

Alation

OCTOBER 18, 2022

Alation is pleased to be named a dbt Metrics Partner and to announce the start of a partnership with dbt, which will bring dbt data into the Alation data catalog. In the modern data stack, dbt is a key tool to make data ready for analysis. Data Transformation in the Modern Data Stack.

Metadata 52

Metadata Metrics Recreation/Entertainment Data Quality 52

DataKitchen

JANUARY 23, 2023

DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements.

Testing 130

Testing Data Governance Data Quality Data-driven 130

Alation

NOVEMBER 22, 2022

What Is Data Governance In The Public Sector? Effective data governance for the public sector enables entities to ensure data quality, enhance security, protect privacy, and meet compliance requirements. With so much focus on compliance, democratizing data for self-service analytics can present a challenge.

Data Governance 52

Data Governance Metadata Data-driven Unstructured Data 52