CIO Business Intelligence

APRIL 25, 2024

,” said Tyler Carlson, VP of business development and strategic partnerships at Salesforce. Currently, a handful of startups offer “reverse” extract, transform, and load (ETL), in which they copy data from a customer’s data warehouse or data platform back into systems of engagement where business users do their work.

Data Integration 119

Data Integration Data Lake Metadata Data Warehouse 119

erwin

JUNE 26, 2020

Given the value this sort of data-driven insight can provide, the reason organizations need a data catalog should become clearer. It’s no surprise that most organizations’ data is often fragmented and siloed across numerous sources (e.g., Three Types of Metadata in a Data Catalog. Technical Metadata.

Metadata 132

Metadata Cost-Benefit Measurement Data-driven 132

AWS Big Data

MARCH 7, 2023

Data lakes are more focused around storing and maintaining all the data in an organization in one place. And unlike data warehouses, which are primarily analytical stores, a data hub is a combination of all types of repositories—analytical, transactional, operational, reference, and data I/O services, along with governance processes.

Analytics 68

Analytics Data Warehouse Data Lake Metadata 68

CIO Business Intelligence

MARCH 9, 2022

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure.

Data Lake 119

Data Lake Unstructured Data Data Warehouse Data Quality 119

IBM Big Data Hub

JANUARY 18, 2023

Data platform architecture has an interesting history. Towards the turn of millennium, enterprises started to realize that the reporting and business intelligence workload required a new solution rather than the transactional applications. A read-optimized platform that can integrate data from multiple applications emerged.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

Alation

OCTOBER 21, 2021

Data lakes also support the growing thirst for analysis by data scientists and data analysts, as well as the critical role of data governance. But setting up a data lake takes a thoughtful approach to ensure it’s positioned to prevent it from becoming a data swamp. Lack of metadata.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

Octopai

FEBRUARY 27, 2020

Data sources are growing nonstop, and as soon as you think you have everything under control, more data new comes along and you’re back to square one, trying to figure out what caused a particular error in a report, for example. Specific data mapping use cases can be: Setting up data warehouses. System migrations.

Metadata 52

Metadata Data Warehouse Reporting Software 52