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Getting DataOps Right
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Getting DataOps Right
by Mark Marinelli, Liam Cleary, Nik Bates-Haus, Michael Stonebraker, Andy Palmer
Getting DataOps Right
1. Introduction
DevOps and DataOps
The Catalyst for DataOps: “Data Debt”
Paying Down the Data Debt
From Data Debt to Data Asset
DataOps to Drive Repeatability and Value
Organizing by Logical Entity
2. Moving Toward Scalable Data Unification
A Brief History of Data Unification Systems
Unifying Data
Rules for Scalable Data Unification
3. DataOps as a Discipline
DataOps: Building Upon Agile
The Agile Manifesto
Agile Practices
Agile Operations for Data and Software
DataOps Tenets
DataOps Practices
DataOps Challenges
Application Data Interface
Data Processing Architecture
Query Interface
Resource Intensive
Schema Change
Governance
The Agile Data Organization
4. Key Principles of a DataOps Ecosystem
Highly Automated
Open
Best of Breed
Table(s) In/Table(s) Out Protocol
Three Core Styles of Interfaces for Components
Tracking Data Lineage and Provenance
Data Integration: Deterministic, Probabilistic, and Humanistic
Combining Aggregated and Federated Storage
Processing Data in Both Batch and Streaming Modes
Conclusion
5. Key Components of a DataOps Ecosystem
Catalog/Registry
Movement/ETL
Alignment/Unification
Storage
Publishing
Feedback
Governance
6. Building a DataOps Toolkit
Interoperability
Composable Agile Units
Results Import
Metadata Exchange
Automation
Continuous Automation
Batch Automation
7. Embracing DataOps: How to Build a Team and Prepare for Future Trends
Building a DataOps Team
Data Supply
Data Preparation
Data Consumption
So Where Are They?
The Future of DataOps
The Need for Smart, Automated Data Analysis
Custom Solutions from Purpose-Built Components
Increased Approachability of Advanced Tools
Subject Matter Experts Will Become Data Curators and Stewards
A Final Word
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Getting DataOps Right
Getting DataOps Right
Andy Palmer, Michael Stonebraker,
Nik Bates-Haus,
Liam Cleary,
and Mark Marinelli
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