Blog Data Process Data Security Metadata 
Cloudera
MARCH 3, 2022
Data users in these enterprises don’t know how data is derived and lack confidence in whether it’s the right source to use. . If data access policies and lineage aren’t consistent across an organization’s private cloud and public clouds, gaps will exist in audit logs. From Bad to Worse.
Databand.ai
AUGUST 30, 2023
Slow data processing: Due to the manual nature of many data workflows in legacy architectures, data processing can be time-consuming and resource-intensive. In a DataOps architecture, it’s crucial to have an efficient and scalable data ingestion process that can handle data from diverse sources and formats.
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AltexSoft
FEBRUARY 11, 2023
If you are not familiar with the above-mentioned concepts, we suggest you to follow the links above to learn more about each of them in our blog posts. Bad data management be like, Source: Makeameme Data architects are sometimes confused with other roles inside the data science team.
DataKitchen
AUGUST 9, 2021
The domain also includes code that acts upon the data, including tools, pipelines, and other artifacts that drive analytics execution. The domain requires a team that creates/updates/runs the domain, and we can’t forget metadata: catalogs, lineage, test results, processing history, etc., ….
Knowledge Hut
SEPTEMBER 29, 2023
Moreover, what benefits can you expect from a career in Azure Data Engineering? This blog aims to answer these questions, providing a straightforward and professional insight into the world of Azure Data Engineering. Join us on this journey through the exciting realm of Azure Data Engineering.
Cloudera
AUGUST 21, 2020
Security needs to be treated at a mission-critical level and data security also needs to be a core part of a business’s strategic approach. The governance aspect is perhaps even more important and businesses need to be able to understand where the data comes from.
Rockset
MARCH 18, 2024
That’s because successfully deploying an AI application requires retrieval augmented generation or “RAG” pipelines, processing real-time data streams, chunking data, generating embeddings, storing embeddings and running vector search. These additional inputs are referred to as metadata filtering. What is RAG?
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