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 71

Analytics Data Warehouse Data Lake Metadata 71

Cloudera

MAY 12, 2021

This category is open to organizations that have tackled transformative business use cases by connecting multiple parts of the data lifecycle to enrich, report, serve, and predict. . DATA FOR ENTERPRISE AI. Industry Transformation: Telkomsel — Ingesting 25TB of data daily to provide advanced customer analytics in real-time .

Digital Transformation 70

Digital Transformation Machine Learning Optimization Data Lake 70

AWS Big Data

NOVEMBER 14, 2023

However, in many organizations, data is typically spread across a number of different systems such as software as a service (SaaS) applications, operational databases, and data warehouses. Such data silos make it difficult to get unified views of the data in an organization and act in real time to derive the most value.

IoT 55

IoT Data-driven Data Lake Data Strategy 55

Alation

APRIL 14, 2022

Similar to a data warehouse schema, this prep tool automates the development of the recipe to match. For example, data science always consumes “historical” data, and there is no guarantee that the semantics of older datasets are the same, even if their names are unchanged. Scheduling. Target Matching.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

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