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So, what can you do to ensure your data is up to par and […]. The post Data Trustability: The Bridge Between Data Quality and DataObservability appeared first on DATAVERSITY. You might not even make it out of the starting gate.
Without access to all critical and relevant data, the data that emerges from a data fabric will have gaps that delay business insights required to innovate, mitigate risk, or improve operational efficiencies. You must be able to continuously catalog, profile, and identify the most frequently used data.
This includes integration with your datawarehouse engines, which now must balance real-time data processing and decision-making with cost-effective object storage, open source technologies and a shared metadata layer to share data seamlessly with your data lakehouse.
For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization.
NoSQL Databases: Flexible, scalable solutions for unstructured or semi-structured data. DataWarehouses : Centralised repositories optimised for analytics and reporting. DataLakes : Scalable storage for raw and processed data, supporting diverse data types.
The cloud is especially well-suited to large-scale storage and big data analytics, due in part to its capacity to handle intensive computing requirements at scale. BI platforms and datawarehouses have been replaced by modern datalakes and cloud analytics solutions.
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