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| Joseph Arnold
Data Consistency vs Data Integrity: Similarities and Differences
Data consistency refers to the state of data in which all copies or instances are the same across all systems and databases.
Joseph Arnold
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| Joseph Arnold
Unified DataOps: Components, Challenges, and How to Get Started
Unified DataOps represents a fresh approach to managing and synchronizing data operations across several domains, including data engineering, data science, DevOps, and analytics.
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| Joseph Arnold
DataOps vs. MLOps: Similarities, Differences, and How to Choose
DataOps is an emerging discipline that focuses on improving the collaboration, integration, and automation of data management processes. MLOps is a practice derived from DevOps and data engineering principles.
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| Joseph Arnold
7 Data Pipeline Examples: ETL, Data Science, eCommerce, and More
Data pipelines are a series of data processing steps that enable the flow and transformation of raw data into valuable insights for businesses.
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| Joseph Arnold
Data Observability Tools: Types, Capabilities, and Notable Solutions
Data observability tools offer data engineers insight into the health of their data infrastructure, by giving visibility into crucial metrics like latency, throughput, and error rates.
