IBM Big Data Hub

FEBRUARY 2, 2023

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights.

Data Warehouse 57

Data Warehouse Cost-Benefit Unstructured Data Data Architecture 57

AWS Big Data

MARCH 10, 2023

Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats. However, as data processing at scale solutions grow, organizations need to build more and more features on top of their data lakes.

Data Lake 102

Data Lake Sales Data Warehouse Snapshot 102

Sisense

OCTOBER 6, 2020

These programs and systems are great at generating basic visualizations like graphs and charts from static data. The challenge comes when the data becomes huge and fast-changing. Why is quantitative data important? Despite its many uses, quantitative data presents two main challenges for a data-driven organization.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

BizAcuity

MAY 10, 2022

2007: Amazon launches SimpleDB, a non-relational (NoSQL) database that allows businesses to cheaply process vast amounts of data with minimal effort. The platform is built on S3 and EC2 using a hosted Hadoop framework. An efficient big data management and storage solution that AWS quickly took advantage of.

Data-driven 52

Data-driven IoT Unstructured Data Data Lake 52

datapine

FEBRUARY 22, 2022

1) What Is A Business Intelligence Strategy? 4) How To Create A Business Intelligence Strategy. Odds are you know your business needs business intelligence (BI). Over the past 5 years, big data and BI became more than just data science buzzwords. Table of Contents.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

Domino Data Lab

JULY 2, 2019

Of course, if you use several different data management frameworks within your data science workflows—as just about everybody does these days—much of that RDBMS magic vanishes in a puff of smoke. Some may ask: “Can’t we all just go back to the glory days of business intelligence, OLAP, and enterprise data warehouses?”

Metadata 105

Metadata Machine Learning Data Science Data-driven 105