IBM Journey to AI blog

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.

Machine Learning 183

Machine Learning Metadata Automation AI 183

AWS Machine Learning Blog

OCTOBER 11, 2024

Analyze the events’ impact by examining their metadata and textual description. Create business intelligence (BI) dashboards for visual representation and analysis of event data. Dispatch notifications through instant messaging tools or emails. Log tickets or page the appropriate personnel in the chosen ITSM tools.

AI 93

AI AI Automation Chatbots 93

IBM Journey to AI blog

JULY 17, 2023

Watsonx.data is built on 3 core integrated components: multiple query engines, a catalog that keeps track of metadata, and storage and relational data sources which the query engines directly access. ” Vitaly Tsivin, EVP Business Intelligence at AMC Networks. Later this year, it will leverage watsonx.ai

Machine Learning 186

Machine Learning Metadata Automation AI 186

IBM Journey to AI blog

JULY 1, 2024

Irina Steenbeek introduces the concept of descriptive lineage as “a method to record metadata-based data lineage manually in a repository.” Extraction, transformation and loading (ETL) tools dominated the data integration scene at the time, used primarily for data warehousing and business intelligence.

ETL 100

ETL Automation Metadata Business Intelligence 100

ODSC - Open Data Science

SEPTEMBER 27, 2023

Data Warehouses Some key characteristics of data warehouses are as follows: Data Type: Data warehouses primarily store structured data that has undergone ETL (Extract, Transform, Load) processing to conform to a specific schema. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

Big Data 98

Big Data Metadata ETL Data Science 98

Pickl AI

NOVEMBER 15, 2023

It involves the extraction, transformation, and loading (ETL) process to organize data for business intelligence purposes. Transactional databases, containing operational data generated by day-to-day business activities, feed into the Data Warehouse for analytical processing.

ETL 52

ETL Business Intelligence Metadata Data Analysis 52

AWS Machine Learning Blog

JUNE 25, 2024

To create and share customer feedback analysis without the need to manage underlying infrastructure, Amazon QuickSight provides a straightforward way to build visualizations, perform one-time analysis, and quickly gain business insights from customer feedback, anytime and on any device.

Automation 117

Automation Prompt Engineering Prompt Engineer Categorization 117

Artificial Corner

JUNE 6, 2023

Data warehouses were designed to support business intelligence activities, providing a centralized data source for reporting and analysis. This multidimensional analysis capability makes OLAP ideal for business intelligence applications, where users must analyze data from various perspectives.

Data Mining 52

Data Mining Big Data ETL Machine Learning 52

Pickl AI

DECEMBER 15, 2024

Familiarise yourself with ETL processes and their significance. It enables organisations to perform complex queries and analyses, making it a crucial element for business intelligence and decision-making processes. ETL Process: Extract, Transform, Load processes that prepare data for analysis.

ETL 52

ETL Metadata Data Quality Explainability 52