Flipboard

JUNE 26, 2023

Typically, companies ingest data from multiple sources into their data lake to derive valuable insights from the data. These sources are often related but use different naming conventions, which will prolong cleansing, slowing down the data processing and analytics cycle. This will open the ML transforms page.

AWS 123

AWS ML ML ETL 123

Alation

MAY 16, 2023

Data is a valuable resource, especially in the world of business. A McKinsey survey found that companies that use customer analytics intensively are 19 times higher to achieve above-average profitability. But with the sheer amount of data continually increasing, how can a business make sense of it? Robust data pipelines.

Data Pipeline 69

Data Pipeline Data Governance Data Lakes Data Warehouse 69

IBM Journey to AI blog

JULY 3, 2024

Align your data strategy to a go-forward architecture, with considerations for existing technology investments, governance and autonomous management built in. Look to AI to help automate tasks such as data onboarding, data classification, organization and tagging.

AI 45

AI AI Data Quality Database 45

Alation

MAY 16, 2023

Data is a valuable resource, especially in the world of business. A McKinsey survey found that companies that use customer analytics intensively are 19 times higher to achieve above-average profitability. But with the sheer amount of data continually increasing, how can a business make sense of it? Robust data pipelines.

Data Pipeline 40

Data Pipeline Data Governance Data Lakes Data Warehouse 40

Flipboard

MARCH 21, 2025

The ability for organizations to quickly analyze data across multiple sources is crucial for maintaining a competitive advantage. Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems.

SQL 134

SQL Data Analyst Data Warehouse AWS 134

AWS Machine Learning Blog

MARCH 27, 2025

An example of Software Defect case is [Customer: "Our data pipeline jobs are failing with a 'memory allocation error' during the aggregation phase. The same ETL workflows were running fine before the upgrade. The same ETL workflows were running fine before the upgrade. Agent: "I understand your need for cross-tenant analytics.

AWS 105

AWS ETL ML ML 105