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. Data Acquisition: Extracting data from source systems and making it accessible. Implement Data Lineage and Traceability Path: Data Vault 2.0

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

IBM Journey to AI blog

JULY 3, 2024

As data types and applications evolve, you might need specialized NoSQL databases to handle diverse data structures and specific application requirements. This approach ensures that data quality initiatives deliver on accuracy, accessibility, timeliness and relevance.

AI 45

AI Data Quality AI Database 45

Pickl AI

JUNE 7, 2024

Also Read: Top 10 Data Science tools for 2024. It is a process for moving and managing data from various sources to a central data warehouse. This process ensures that data is accurate, consistent, and usable for analysis and reporting. This process helps organisations manage large volumes of data efficiently.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

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

Precisely

APRIL 15, 2025

And yet, many data leaders struggle to trust their AI-driven insights due to poor data observability. In fact, only 59% of organizations trust their AI/ML model inputs and outputs , according to the latest BARC Data Observability Survey: Observability for AI Innovation.

Data Observability 69

Data Observability Data Quality Data Pipeline ML 69

Pickl AI

MARCH 19, 2025

It is widely used for storing and managing structured data, making it an essential tool for data engineers. MongoDB MongoDB is a NoSQL database that stores data in flexible, JSON-like documents. Apache Spark Apache Spark is a powerful data processing framework that efficiently handles Big Data.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52