IBM Journey to AI blog

OCTOBER 22, 2024

Data must be combined and harmonized from multiple sources into a unified, coherent format before being used with AI models. This process is known as data integration , one of the key components to improving the usability of data for AI and other use cases, such as business intelligence (BI) and analytics.

Data Silos 102

Data Silos Data Pipeline DataOps Business Intelligence 102

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. The Lineage & Dataflow API is a good example enabling customers to add ETL transformation logic to the lineage graph.

Data Quality 98

Data Quality Data Governance ETL Data Observability 98

Pickl AI

MARCH 19, 2025

It helps data engineers collect, store, and process streams of records in a fault-tolerant way, making it crucial for building reliable data pipelines. Amazon Redshift Amazon Redshift is a cloud-based data warehouse that enables fast query execution for large datasets.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

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. Having model-level data validations along with implementing a data observability framework helps to address the data vault’s data quality challenges.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

IBM Journey to AI blog

JULY 3, 2024

To power AI and analytics workloads across your transactional and purpose-built databases, you must ensure they can seamlessly integrate with an open data lakehouse architecture without duplication or additional extract, transform, load (ETL) processes.

AI 45

AI AI Data Quality Database 45