DataOps

COMMUNITY DATA OPERATIONS FOR REPRODUCIBLE TLP

Thurston Sexton

Knowledge Extraction and Application for Smart Manufacturing Operations Management

Systems Integration Division

Engineering Laboratory
DISCLAIMER

The use of any products described in any presentation does not imply 

recommendation or endorsement by the National Institute of Standards 

and Technology, nor does it imply that products are necessarily the best 

available for the purpose.
OVERVIEW

I. Our Domains

A. Map Data and Domain “pipelines”

B. Immediate needs

II. Our TLP Community

A. The Problem

B. Lessons from the “front lines”
APPLYING DATA-OPS IN OUR DOMAINS

Example from Maintenance Management
MWO DATA “PIPELINE”

5

• Extract

• Transform 

• Load 

• Collection and Storage

• Cleaning and Parsing

• Analysis and Visualization
MWO DATA “PIPELINE”

6

• Extract

• Transform 

• Load 

• Collection and Storage

• Cleaning and Parsing

• Analysis and Visualization

Collection &

Storage

Cleaning & 

Parsing

Analysis & 

Visualization
7

Collection &

Storage

Cleaning & 

Parsing

Analysis & 

Visualization

Decisions made at 

each stage will impact

the strategies that are 

•Available

•Efficient

at each other stage. 

Keep in mind ...

MWO DATA “PIPELINE”
8

Collection &

Storage

Cleaning & 

Parsing

Analysis & 

Visualization

These are all supporting activities to 

“actual” maintenance tasks [1].

Perform

Prepare

Discover 

Needs

[1] Brundage, M. P., Sexton, T., Hodkiewicz, M., Morris, K., Arinez, J., Ameri, F., Ni, J., and Xiao, G. (July 22, 2019). "Where 

Do We Start? Guidance for Technology Implementation in Maintenance Management for Manufacturing." ASME. J. 

Manuf. Sci. Eng. September 2019; 141(9): 091005. 

MWO DATA “PIPELINE”
Needs - Data Collection and Storage 

9

• MWO Terminology Definitions

What defines its components? Who is involved? What is it recording?

• Atomic data types and formats for information flow in MWOs

Issue meta-data (dates, descriptions, etc.), personnel, asset IDs

• Adaptive database schemas for storing varied MWO data 

Desirable information will shift over time—what are the core invariable relations?

• Mapping from disparate CMMS solutions into standard data types

Current software uses proprietary/custom schemas—unification?
OUR COMMUNITY: The Problem

Developers vs “Hackers”
DEALING WITH COMPLEXITY
MOVIES VS REALITY

Programmers? Researchers

See: Science as Amateur Software Development, R. McElreath 2020
So programmers use Dev-Ops...

Science and Research is fueled by Data...

→ Data-Ops
WHAT IS DATA OPS? 

● Establish progress and performance measurements everywhere

● Abstract validation layer: Ensure everyone is 

a. “speaking the same language” 

b. agrees on what the data (and metadata) is and is not.

“DataOps (data operations) is an approach to designing, implementing and maintaining a distributed data 

architecture that will support a wide range of open source tools and frameworks in production.” - Jack Vaughan

Toph Whitmore, Principal Analyst at Blue Hill Research
WHAT IS DATA OPS? cont’d...

● Validate with the “eyeball test”: 

a. Include continuous-improvement-oriented human feedback loops. 

b. Trust in the data comes from incremental validation.

● Automate data flow.... As much as possible:

a. preprocessing

b. testing

c. data science

d. analytics 

“DataOps (data operations) is an approach to designing, implementing and maintaining a distributed data 

architecture that will support a wide range of open source tools and frameworks in production.” - Jack Vaughan

Toph Whitmore, Principal Analyst at Blue Hill Research
WHAT IS DATA OPS? cont’d...

● Identify bottlenecks, then optimize for them. 

a. Use performance measurements here! 

b. Investment: hardware, automation, etc.

● Governance discipline

a. data ownership & transparency, 

b. data lineage tracking

● Design for growth and extensibility

a. Must accommodate volume and variety of data. 

b. Enabling technologies should be priced affordably

“DataOps (data operations) is an approach to designing, implementing and maintaining a distributed data 

architecture that will support a wide range of open source tools and frameworks in production.” - Jack Vaughan

Toph Whitmore, Principal Analyst at Blue Hill Research
LESSONS WE’VE LEARNED

From the “front lines”
Pull(Merge) Requests

● Projects as iterative collaborations

○ Start exploration as a branch

○ Can be “empty”

○ Track small commits w/ conversation

○ Integrated review, suggestions, @’s

○ Inline change views/comments

● Prototype, test, complete, review, merge

○ All without breaking “main”

○ Can apply to all steps in the pipeline

References:

- Ten Simple Rules for Taking Advantage of Git and GitHub

- Ask students to iterate on their work with draft pull requests

EXPLOIT GIT(FORGE) ARCHITECTURE
Data Science Environments

● Reproducible Compute (e.g. Python?)

○ Jupyter Notebooks + git??? → Jupytext

○ Lightweight environments? → miniconda

○ Simple Packages (w/o setuptools) → poetry

● Documentation and Interop.

○ Easier documentation → mkdocs-material

○ Use automated docstring extraction

○ Data-oriented programming

○ Unify styles: Type-hinting, functions-first.

○ property-based tests → Hypothesis

Also see:

- Tom Augspurger, Modern Pandas

EXPLOIT GIT(FORGE) ARCHITECTURE
Data Itself

● Data-as-Code: makefiles+git=DVC

○ Don’t reinvent the wheel, use git.

○ Language-agnostic, w/ python API

○ Every step of the pipeline, version-controlled 

with automated cache-updates

○ Make registries for your entire community (!) 

(data is just an “import” away...)

● Validate all the things 

○ Data shape, types, etc., make explicit: datatest

○ Schemas once-and-for-all: → pydantic

References:

- Ten Simple Rules for Taking Advantage of Git and GitHub

- Ask students to iterate on their work with draft pull requests

EXPLOIT GIT(FORGE) ARCHITECTURE
THE PLAN

Distributed Collaboration for the TLP CoI

I. GitHub Organization: TLP-COI

A. Documentation - best practices for TLP, theory, etc

B. Networking - curated list for state-of-the-practice (“awesome-tlp”)

C. Collaboration - base or forks for open tool repositories 

II. Communication: 

A. TLP-COI Slack Workspace - QR code → 

B. Other options? Possible “Discourse”? Webinars? Let us know!
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