IBM Big Data Hub

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. Then, it applies these insights to automate and orchestrate the data lifecycle.

Data Architecture 60

Data Architecture Data Lake Machine Learning Data Governance 60

Jet Global

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

FineReport

MARCH 24, 2024

They identify and interpret trends in complex datasets, optimize statistical results, and maintain databases while devising new data collection processes. Additionally, they facilitate organizational risk assessments, provide consulting services to leadership, and mentor junior analysts.

Statistics 52

Statistics Data mining Visualization Reporting 52

bridgei2i

MARCH 11, 2021

We all know how Artificial Intelligence helps giants like Google and Amazon reshape their commercial techniques and business strategies. From eliminating the need for human assistance in repetitive tasks to reducing the risk of human errors in manual processes – AI can do a lot for large-scale businesses. in the system.

Key Performance Indicator 52

Key Performance Indicator Data Governance Data Lake Metadata 52

datapine

AUGUST 29, 2022

The new edition also explores artificial intelligence in more detail, covering topics such as Data Lakes and Data Sharing practices. 6) Lean Analytics: Use Data to Build a Better Startup Faster, by Alistair Croll and Benjamin Yoskovitz. Product/market fit is THE most important factor to get right.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Peter James Thomas

AUGUST 29, 2018

I have since run and driven transformation in Reference Data, Master Data , KYC [3] , Customer Data, Data Warehousing and more recently Data Lakes and Analytics , constantly building experience and capability in the Data Governance , Quality and data services domains, both inside banks, as a consultant and as a vendor.

Data-driven 61

Data-driven Cost-Benefit Metadata Technology 61