AWS Big Data

MARCH 7, 2023

A data hub contains data at multiple levels of granularity and is often not integrated. It differs from a data lake by offering data that is pre-validated and standardized, allowing for simpler consumption by users. Data hubs and data lakes can coexist in an organization, complementing each other.

Analytics 74

Analytics Data Warehouse Data Lake Metadata 74

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

IBM Big Data Hub

JANUARY 18, 2023

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. Data fabric and data mesh as concepts have overlaps.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

Corinium

APRIL 25, 2019

And it’s become a hyper-competitive business, so enhancing customer service through data is critical for maintaining customer loyalty. And more recently, we have also seen innovation with IOT (Internet Of Things). In data-driven organizations, data is flowing. But I’ll give an example in favour of each.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

FineReport

APRIL 11, 2023

Every user can now create interactive reports and utilize data visualization to disseminate knowledge to both internal and external stakeholders. BI dashboards typically display a variety of data visualizations to give users a comprehensive view of relevant KPIs and trends for both strategic planning and operational decision-making.

Dashboards 40

Dashboards Business Intelligence Cost-Benefit Metrics 40

Cloudera

OCTOBER 7, 2021

Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.

Metadata 124

Metadata Cost-Benefit Enterprise Interactive 124