Pickl AI

OCTOBER 11, 2023

Data Observability and Data Quality are two key aspects of data management. The focus of this blog is going to be on Data Observability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data.

Data Observability 52

Data Observability Data Quality Data Pipeline Data Governance 52

Precisely

DECEMBER 12, 2023

It includes streaming data from smart devices and IoT sensors, mobile trace data, and more. Data is the fuel that feeds digital transformation. But with all that data, there are new challenges that may prompt you to rethink your data observability strategy. Learn more here.

Data Observability 52

Data Observability Data Quality Data Pipeline Machine Learning 52

IBM Journey to AI blog

FEBRUARY 26, 2024

That’s why data pipeline observability is so important. Data lineage expands the scope of your data observability to include data processing infrastructure or data pipelines, in addition to the data itself.

Data Pipeline 112

Data Pipeline Data Engineering Data Engineering Data Engineering 112

phData

AUGUST 10, 2023

The implementation of a data vault architecture requires the integration of multiple technologies to effectively support the design principles and meet the organization’s requirements. Leverage dbt’s `test` macros within your models and add constraints to ensure data integrity between data vault entities.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Alation

MAY 24, 2022

This has created many different data quality tools and offerings in the market today and we’re thrilled to see the innovation. People will need high-quality data to trust information and make decisions. This kit offers an open DQ API, developer documentation, onboarding, integration best practices, and co-marketing support.

Data Quality 98

Data Quality Data Governance ETL Data Observability 98

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. Check out the Kubeflow documentation. For example, neptune.ai

Machine Learning 134

Machine Learning Machine Learning ML ML 134

DagsHub

AUGUST 23, 2024

Datafold is a tool focused on data observability and quality. It is particularly popular among data engineers as it integrates well with modern data pipelines (e.g., Source: [link] Monte Carlo is a code-free data observability platform that focuses on data reliability across data pipelines.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52