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DataOps Architecture: 5 Key Components and How to Get Started

Databand.ai

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

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DataOps Framework: 4 Key Components and How to Implement Them

Databand.ai

One key aspect of data orchestration is the automation of data pipeline tasks. By automating repetitive tasks, such as data extraction, transformation, and loading (ETL), organizations can streamline their data workflows and reduce the risk of human error.

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Unified DataOps: Components, Challenges, and How to Get Started

Databand.ai

These experts will need to combine their expertise in data processing, storage, transformation, modeling, visualization, and machine learning algorithms, working together on a unified platform or toolset.

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The DataOps Vendor Landscape, 2021

DataKitchen

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Reflow — A system for incremental data processing in the cloud. Meta-Orchestration .