OPEN DATA PRODUCT SPECIFICATION Dev
Development version
The key words “MUST”, “MUST NOT”, “REQUIRED”, “SHALL”, “SHALL NOT”, “SHOULD”, “SHOULD NOT”, “RECOMMENDED”, “NOT RECOMMENDED”, “MAY”, and “OPTIONAL” in this document are to be interpreted as described in BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here.
The specification is shared under Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license. Copyright is held by the creators of the specification: Jarkko Moilanen, Jussi Niilahti and Toni Luhti.
VERSION DEV
Version source:
ODPS examples (currently ODPS v2.1):
ODPS JSON Schema:
Editors:
Participate:
Introduction
The Open Data Product Specification is a vendor-neutral, open-source machine-readable data product metadata model. It defines the objects and attributes as well as the structure of digital data products. The work is based on existing standards (schema.org), best practices and emerging concepts like Data Mesh. The reasoning is that we reuse and proudly copy instead of reinventing the wheel. More detailed information of the origin can be found from the Open Data Product Specification homepage.
Open Data Product Specification (ODPS) changes the data product metadata model towards a standalone model, which helps to decouple data product from the systems often directly associated with it. With help of the ODPS data product can be presented and described to the customer also as such without any need for marketplace or other systems.
Development of the standard is coordinated in Open Data Product Initiative (ODPI) which was established in July 2022 to make it possible for the specification to grow and become institutionlized. The ODPI was taken under the wings of open source chapter of Open Collective.
Specification aims and aspects
Specification aims:
- enable interoperability between organizations, data platforms, marketplaces, and tools.
- reduce data product metadata conversions and errors between systems and organizations,
- increase the speed of designing, testing, and implementing data products.
- speed up tools development around data product design, development and management.
- enable creation of automated data product deployment with standard methods (DataOps)
Note! In the "Open Data Product" focus is on the latter words and the prefix 'open' refers to the openness of the standard. Any kind of connotations to open data (a different thing) are not intentional, intended, or desirable.
The specification has been designed with four major aspects of the data product in mind: 1) technical (infrastructure & access), 2) business (pricing & plans), 3) legal (licensing & IPR), and 4) ethical (privacy & mydata). The four aspects are described in 7 elements, which contain attributes and other elements.
If you see something missing, described inaccurately or plain wrong, or you want to comment the specification, raise an issue in Github
Document structure
LEFT COLUMN: Navigation
The left column is navigation which enables fluent and easy movement around the specification.
MIDDLE COLUMN: Principles and components
The middle column contains detailed information about the included components and related options. This is the theory part.
Note! Mandatory elements and attributes are listed separately in the definition tables. This enables user to construct minimum viable specification more easily and fast. https://schema.org provided ready-made definitions are applied when ever possible instead of re-inventing the wheel.
RIGHT COLUMN: Examples
The right column contains YAML formatted examples of how the specification is used. In the future other output formats are added on request basis. YAML can easily be converted to JSON if needed.
Example of YAML formatted snippet from the Open Data Product Specification:
monitoring:
url: https://monitoring.com
Document level attributes
Here's the list of attributes which can occur at the document root level. In the following description, if a field is not explicitly REQUIRED or described with a MUST or SHALL, it can be considered OPTIONAL. Optional attributes are listed in own table and examples are given on the right column.
Mandatory attributes
Example of document level attribute usage and structure:
schema: https://raw.githubusercontent.com/Open-Data-Product-Initiative/open-data-product-spec-dev/ddbc069196a664d0e28a0f3dc7c1c7fb49b64591/source/schema/odps-dev-json-schema.json
version: dev
product:
en:
name: Pets of the year
productID: 123456are
visibility: private
status": draft
type: dataset
fi:
name: Vuoden suosituimmat lemmikit
productID: 123456are
visibility: private
status: draft
type: dataset
Element name |
Type | Options | Description |
---|---|---|---|
$schema | URL | Valid URL. See more from RFC 3986. | REQUIRED Defines the URL of Schema. Used often for validation purposes. In the URL, the used ODPS version is indicated in the name, odps-VERSION-json-schema.json. |
$version | string | This is the version of ODPS, for example dev or 2.2 | REQUIRED Defines the ODPS version. |
product | element | root element | REQUIRED Root element to tie all together. |
en | element | ISO 639-1 defined 2-letter codes | REQUIRED - NOTE! This is a dynamic element! This element binds together other product attributes and expresses the langugage used. In the example this is "en", which indicates that product details are in English. If you would like to use French details, then name the element "fr". The naming of this element follows options (language codes) listed in ISO 639-1 standard. You can have product details in multiple languages simply by adding similar sets like the example - just change the binding element name to matching language code. The pattern to implement multilanguage support for data products was adopted from de facto UI translation practices. The attributes inside this element are commonly rendered in the UI for the consumer and providing a simple way to implement that was the driving reasoning. See for example JSON - Multi Language |
name | string | max length 256 chars | REQUIRED The name of the product. |
productID | string | max length 256 chars | REQUIRED Product identifier. |
visibility | one of | one of: private, organisation, public | REQUIRED The publicity level eg who can see this product. Private - just the creator. Organisation - visible to all in your organisation. Public - visible to all publicly. |
status | one of | one of: announcement, draft, development, testing, acceptance, production, sunset, retired | REQUIRED The status of the product. Lifecycle model discussed in details in here (link). |
type | one of | Options: raw data, derived data, dataset, reports, analytic view, 3D visualisation, algorithm, decision support, automated decision-making, data-enhanced product, data-driven service, data-enabled performance, bi-directional. | REQUIRED The type of the product. Options are derived from examples and lists found from academic literature. |
Optional attributes
RecommendedDataProducts OBJECT contains an array of data products which offers means to attach related data products to the data product at hand. The source of the recommended data product might be from the same marketplace/catalog or an external one. Recommended object offers method to extend the reach and promotion escpecially when data product is treated as an independent entity much like described in Data Mesh. Also when data product is published in a marketplace, the Recommended object offers means to promote other than just the data products from the given data marketplace. In short, tis object is mainly for discovery and reach purposes.
RecommendedUseCases OBJECT is an array which contains offers method to attach usefull usecases to the data product. Usecases are informatiove for the the data customer and exemplify how the data product can create value.
Example of document level attribute usage and structure:
schema: https://raw.githubusercontent.com/Open-Data-Product-Initiative/open-data-product-spec-dev/ddbc069196a664d0e28a0f3dc7c1c7fb49b64591/source/schema/odps-dev-json-schema.json
version: dev
product:
en:
name: Pets of the year
productID: 123456are
valueProposition: Design a customised petstore using a data product that describes
pets with their habits, preferences and characteristics.
description: This is an example of a Petstore product.
productSeries: Lovely pets data products
visibility: private
status: draft
productVersion: '0.1'
versionNotes: New version with additional details such more accurate pet details
issues: The current issues include incorrect information in the dog breeds. The
resolution for these problems is planned for the next update, scheduled
to be released on July 15th, 2023.
categories:
- pets
standards:
- ISO 24631-6
tags:
- pet
brandSlogan: Passion for the data monetization
type: dataset
contentSample: https://download.com/pets.json
logoURL: https://data-product-business.github.io/open-data-product-spec/images/logo-dps-ebd5a97d.png
OutputFileFormats:
- JSON
- XML
- CSV
- ZIP
- PDF
useCases:
- useCase:
useCaseTitle: Build attractive and lucrative petstore!
useCaseDescription: Use case description how succesfull petstore chain was
established in Abu Dhabi
useCaseURL: https://marketplace.com/usecase1
recommendedDataProducts:
- https://marketplace.com/dataproduct.json
- https://marketplace.com/dataproduct-another.json
Element name |
Type | Options | Description |
---|---|---|---|
valueProposition | string | text content, max length 512 chars | This is the product's value proposition. Often one or two sentences and crystallizes the value for the customer. |
description | string | - | The description of the product. Text only. |
productSeries | string | - | A group of products in the product mix which are associated with each other and they can be obtained for the same type of customers or they are marketable for the same type of market place. |
categories | array | - | Comma separates array of categories. |
standards | array | - | Comma separates array of standards related e.g. to data content or quality, such as ISO 8000 or ISO 19131. |
tags | array | - | Comma separates array of tags. |
productVersion | string | The versioning scheme is major.minor.. Examples: 1.0, 2.1, 3.15 | The version of the data product. Applies for ODPS metadata as well. |
versionNotes | string | - | Additional information about the version. |
issues | string | - | There may be errors in the data product that require corrections. These issues will be briefly described to users, along with information about when the fixes will be implemented. |
contentSample | URL | Valid URL. See more from RFC 3986. | Sample content of the data product, for example JSON/XML output. This sample should match the actual data product output and give the data consumer an idea what to expect. Obviously if the data product is pure service for example dashboard or algorithm, then consider providing preview version or alike |
logoURL | URL | Valid URL | Valid URL of the logo. See more from RFC 3986. |
outputFileFormats | string | - | Output file formats for data product |
brandSlogan | string | - | Brand related slogan like Nike has just do it. |
useCases | element | array | Contains list of related use cases with description information and link to details. NOTE! These examples are expected to use same language as defined previously in the data product details content binding element. |
useCaseTitle | string | string | Title of the usecase. |
useCaseDescription | string | string | Brief description of the usecase. |
useCaseURL | URL | Valid URL, RFC 3986 | Valid URL of the more detailed usecase description. |
recommendedDataProducts | array | Array of valid URLs (RFC 3986) | Data products to recommend use next to this data product or even as replacement (for comparison). The URL provided MUST reference a description of a data product following this same standard |
If you see something missing, described inaccurately or plain wrong, or you want to comment the specification, raise an issue in Github
Data Pricing Plans
Pricing is the process whereby a business sets the price at which it will sell its products and services. Pricing OBJECT consists of mandatory and optional attributes. This element contains pricing plans related data to be used for example in displaying the items in a marketplace. If needed the standard metadata is converted to marketplace internal format. We encourage all data product owners to enforce usage of this standard.
Mandatory attributes are listed in separate table and marked with bolded names and asterix *. Next to the mandatory attributes is an example.
The same logic applies to the optional attributes as well. Optional attributes are listed in own table and an example is given in the right column.
Supported pricing models include:
- Recurring time period based (day, week, month, year) plans
- One time payments plans
- Pay-as-you-go plans
- Revenue sharing plans
- Data volume plan
- Dynamic pricing (high and low limits for automated pricing)
- Pay what you want plans
- Freemium
- Open data
- Value-based
- On Request
Mandatory attributes and elements
Example of Pricing component usage with manadatory elements and attributes. Example language is english.:
pricingPlans:
en:
- name: Premium subscription 1 year
priceCurrency: EUR
price: 50.00
billingDuration: year
unit: recurring
maxTransactionQuantity: unlimited
offering:
- High Quality Pets data
- Unlimited transactions
- Billed annually
- name: Premium Package Monthly
priceCurrency: EUR
price: 5.00
billingDuration: month
unit: recurring
maxTransactionQuantity: unlimited
offering:
- High Quality Pets data
- Unlimited transactions
- Billed monthly
- name: Freemium Package
priceCurrency: EUR
price: 0.00
billingDuration: month
unit: recurring
maxTransactionQuantity: 1000
offering:
- High Quality Pets data
- Free to use, no cost at all!
- Fair amount of transactions for testing and small business
- name: Revenue sharing
priceCurrency: percentage
price: 5.50
billingDuration: month
unit: revenue-sharing
maxTransactionQuantity: 20000
offering:
- High Quality Pets data
- No upfront fee
- Billed monthly
Element name |
Type | Options | Description |
---|---|---|---|
pricingPlans | element | - | Binds the pricing plans related elements and attributes together |
en | element | ISO 639-1 defined 2-letter codes | REQUIRED - NOTE! This is a dynamic element! This element binds together other product pricing plan attributes and expresses the langugage used. In the example this is "en", which indicates that pricing plan details are in English. If you would like to use French details, then name the element "fr". The naming of this element follows options (language codes) listed in ISO 639-1 standard. You can have product pricing plan details in multiple languages simply by adding similar sets like the example - just change the binding element name to matching language code. The pattern to implement multilanguage support for data products was adopted from de facto UI translation practices. The attributes inside this element are commonly rendered in the UI for the consumer and providing a simple way to implement that was the driving reasoning. See for example JSON - Multi Language |
priceCurrency | string | Use standard formats: ISO 4217 currency format e.g. "USD"; Ticker symbol for cryptocurrencies e.g. "BTC" | REQUIRED The primary currency used in pricing. Platforms are assumed to use this as primary currency if currency conversions are used to display product pricing in different locations for various currencies. If the unit is revenue-sharing, then this attribute value MUST be percentage. |
price | string | - | REQUIRED The offer price of a product, or of a price component, or revenue-sharing percentage. If the unit of pricing is revenue-sharing, then this price attribute value is percentage value. Use '.' (Unicode 'FULL STOP' (U+002E)) rather than ',' to indicate a decimal point. Avoid using these symbols as a readability separator. Use values from 0123456789 (Unicode 'DIGIT ZERO' (U+0030) to 'DIGIT NINE' (U+0039)) rather than superficially similiar Unicode symbols. With data-volume the price is for each 1GB of data. |
billingDuration | string | options: instant, day, week, month, year | REQUIRED Specifies for how long this price (or price component) will be billed. Can be used, for example, to model the contractual duration of a subscription or payment plan. |
unit | string | One of: One-time-payment, Pay-per-use, Recurring, Revenue-sharing, Data-volume , Pay-what-you-want, Freemium, Open-data, Value-based, On-request | REQUIRED One-time-payment is for single time purchase purposes, further purchaces are not intended to continue under same agreement. Pay-per-use is intended for continuous usage and price set is for each successful usage action. Recurrring is intended for continuous time period plans. Revenue sharing is a performance-based income model. An effective revenue sharing deal structure is offering your expertise to a business owner to help them grow their business. In return, you get paid a percentage of the revenue as a royalty fee. Freemium is for free access. Use this option also for open data. Data-volume is for data amount based pricing in which customer pays based on the served data amount. The price is always for 1GB of data. Pay-what-you-want is a pricing system where buyers pay any desired amount for a given commodity, sometimes including zero. In some cases, a minimum (floor) price may be set, and/or a suggested price may be indicated as guidance for the buyer. The buyer can also select an amount higher than the standard price for the commodity. If the floor price is set, use minPrice attribute. Open-data is an explicit pricing plan category for open data. By default open data should be free, but in some cases it can have a price. Value-based is value-based selling unit. Present the outcome of your story with solid data and a measurable impact with help of offering attribute. Example: “We can lower the energy bill in heating by $8-13/square meter in a year. Try out simulator to calculate your value!”. Use optional valueSimulator attribute to provide link (URL) to value simulator you have created. In order to set base fee for value-based plan, you can for example set monthly (billingDuration) plan with base see with help of minPrice attribute. On-request is for plans in which customer is given access to data product after contacting provider. Use provider contact information in providing means to contact data product provider for access permissions request. |
maxTransactionQuantity | Integer | Integer | REQUIRED The maximum transaction quantity for the given billing duration. Use this to define for example monthly (or any other period) request limit to the data product. Note! If you want to set unlimited use, value must be 0 (zero). |
offering | string | array | REQUIRED The element that contains pricing plan content as array of strings. Think of this as the list of what is included in the pricing plan and what you offer in return to the price asked. Use the language defined in the plan |
If you see something missing, described inaccurately or plain wrong, or you want to comment the specification, raise an issue in Github
Optional attributes and elements
Example of Pricing component usage with some of the optional elements and attributes:
pricingPlans:
en:
- name: Premium subscription 1 year
priceCurrency: EUR
price: 10.00
minPrice: 5.00
maxPrice: 15.000
additionalPrice: 0.02
- name: Premium Package
priceCurrency: EUR
price: 10.00
maxPrice: 20.00
valueAddedTaxIncluded: False
Element name |
Type | Options | Description |
---|---|---|---|
minPrice | string | - | The lowest price if the price is a range. If dynamic pricing is used with this product, this is the lowest price allowed. In dynamic pricing businesses are able to change prices based on algorithms that take into account competitor pricing, supply and demand, and other external factors in the market. Use '.' (Unicode 'FULL STOP' (U+002E)) rather than ',' to indicate a decimal point. Avoid using these symbols as a readability separator. Use values from 0123456789 (Unicode 'DIGIT ZERO' (U+0030) to 'DIGIT NINE' (U+0039)) rather than superficially similiar Unicode symbols. |
maxPrice | string | - | The highest price if the price is a range. If dynamic pricing is used with this product, this is the highest price allowed. Use '.' (Unicode 'FULL STOP' (U+002E)) rather than ',' to indicate a decimal point. Avoid using these symbols as a readability separator. Use values from 0123456789 (Unicode 'DIGIT ZERO' (U+0030) to 'DIGIT NINE' (U+0039)) rather than superficially similiar Unicode symbols. |
valueAddedTaxIncluded | boolean | true/false | Specifies whether the applicable value-added tax (VAT) is included in the price specification or not. |
valueAddedTaxPercentage | Integer | Number percentage value, range 0-100 | Use '.' (Unicode 'FULL STOP' (U+002E)) rather than ',' to indicate a decimal point. Avoid using these symbols as a readability separator. Use values from 0123456789 (Unicode 'DIGIT ZERO' (U+0030) to 'DIGIT NINE' (U+0039)) rather than superficially similiar Unicode symbols. |
validFrom | DateTime | A combination of date and time in ISO 8601 format yyyy-MM-dd'T'HH:mm:ss.SSSZ. | The date when the item becomes valid. |
validTo | DateTime | A combination of date and time in ISO 8601 format yyyy-MM-dd'T'HH:mm:ss.SSSZ. | The date after when the item is not valid. |
additionalPrice | string | - | This is used to define fees for usage which exceeds the defined max transaction quantity. This value is for each additional transaction. Use '.' (Unicode 'FULL STOP' (U+002E)) rather than ',' to indicate a decimal point. Avoid using these symbols as a readability separator. Use values from 0123456789 (Unicode 'DIGIT ZERO' (U+0030) to 'DIGIT NINE' (U+0039)) rather than superficially similiar Unicode symbols. |
maxDataQuantity | Integer | - | The maximum amount of data transferred during the billing duration. Unit is GB. |
valueSimulator | url | valid url | Intended to be used with value-based pricing plan. Provide url to value simulator in which customer can see the value in various cases. In the simulator customer might be able to input own variables to match their exact case and see the gained value. |
If you see something missing, described inaccurately or plain wrong, or you want to comment the specification, raise an issue in Github
DataOps
DataOps is a process whereby a data product pipeline deployment method is defined. Usually the deployment script contains the logic of the individual steps as well as the code chaining the steps together.
DataOps OBJECT describes building, deploying, and running data product's code, and storing and giving access to data and metadata. This principle has been adopted from the Data Mesh.
Optional attributes and elements
Example of DataOps component usage:
dataOps:
infrastructure:
platform: Azure
region: West US 2 (Washington)
storageTechnology: Azure SQL
storageType: sql
containerTool: helm
format: yaml
schemaLocationURL: http://http://192.168.10.1/schemas/2016/petshopML-2.3/schema/petstore.xsd
scriptURL: http://192.168.10.1/rundatapipeline.yml
deploymentDocumentationURL: http://192.168.10.1/datapipeline
dataLineageTool: Collibra
dataLineageOutput: http://192.168.10.1/lineage.json
hashType: SHA-2
checksum: 7b7444ab8f5832e9ae8f54834782af995d0a83b4a1d77a75833eda7e19b4c921
signatureType: JWK
Element name |
Type | Options | Description |
---|---|---|---|
dataOps | element | - | Binds the dataOps related elements and attributes together. |
infrastructure | element | - | Infrastructure is a process whereby a data product pipeline deployment method is defined. |
platform | string | any | Platform infrastructure, such as AWS, GCP, Azure. |
region | string | any | Provide details of cloud region of AWS, Azure or alike. Examples for AWS: US West (Oregon), Canada (Central), US East (N. Virginia), US East (Ohio). Examples for Azure: Canada Central (Toronto), East US 2 (Virginia), West US 2 (Washington) |
storageTechnology | string | any | Describes the internal storage area technology, such as Amazon S3, Google Cloud Storage, Azure Blob Storage, Azure SQL. |
storageType | string | any | Describes the internal storage type, such as files, sql, events, MQTT. |
containerTool | string | any | A name of the package manager, container or infrastructure as code tool. |
format | string | any | Type of script language. |
schemaLocationURL | URL | Valid URL | The URL of the data product schema, such as XSD, XML or JSON Schema. |
scriptURL | URL | Valid URL | The URL of the deployment script. Script can be used for implementing the data product. In a Data Mesh -model it can be used to define, for example, one or more outputs which take the data from source systems or other data products. |
deploymentDocumentationURL | URL | Valid URL | The URL of the deployment documentation. |
datalineageTool | URL | Valid URL | A tool to view the data lineage. |
datalineageOutput | URL | Valid URL | The URL of the data lineage output. Data lineage output shows the mapping of source data to target output on a metadata level |
hashType | string | One of: SHA-1, SHA-2, SHA-3 | Type of secure hash algorithm for checksum. |
checksum | string | any | Script checksum. |
signatureType | string | any | A public-key cryptosystem,such as JWK, PKCS#12, or PEM. |
Data Access
Data Access OBJECT describes the authorised ability to retrieve, edit, copy or transfer data from IT systems.
Optional attributes and elements
Example of Data Access component usage:
dataAccess:
interface:
outputPorttype: API
authenticationMethod: OAuth
specification: OAS
format: GraphQL
specsURL: http://192.168.10.1/petshop.json
documentationURL: http://192.168.10.1/petshop
Element name |
Type | Options | Description |
---|---|---|---|
dataAccess | element | - | Binds the data access related elements and attributes together. |
interface | element | - | Reference to the ability to use data. |
outputPorttype | string | any | Type of data access, such as API, SQL, sFTP, gRPC. |
hashType | string | any | Type of secure hash algorithm, such as SHA-1, SHA-2, for checksum, when output is file(s). |
checksum | string | any | File checksum. |
authenticationMethod | string | any | Data access authentication method type, such as API key, HTTP Basic, OAuth, No authentication. |
specification | string | any | Type of the data access specification, such as OAS, RAML, Slate. |
format | string | any | Data access file format type, such as JSON, XML, GraphQL, plain text. |
specsURL | URL | Valid URL | The URL of the data access documentation, preferably in a machine-readable format, such as OpenAPI specs. |
documentationURL | URL | Valid URL | The URL of the separated data access documentation or guide. For example, it may contain instructions on how to create and manage api keys. |
Data SLA
Data Service Level Agreement (SLA) Object contains attributes which define the desired and promised quality of the data product.
No mandatory attributes at the moment. Optional attributes are listed in own table and an example is given in the right column.
Optional attributes and elements
Example of SLA component usage:
SLA:
updateFrequency:
unit: hours
value: 1
uptime:
unit: percentage
value: 99
responseTime:
unit: milliseconds
value: 200
support:
company:
phoneNumber: ''
phoneServiceHours: ''
chatURL: ''
chatServiceHours: ''
chatResponseTime: ''
email: support@opendataproducts.org
emailServiceHours: ''
emailResponseTime: ''
documentationURL: ''
guidesURL: ''
community:
stackoverflowURL: ''
forumURL: ''
slackURL: ''
twitterURL: ''
observability:
healthStatus: true
logsURL: https://logs.opendataproducts.org
dashboardURL: https://dashboard.opendataproducts.org
uptimeURL: https://uptime.opendataproducts.org
Element name |
Type | Options | Description |
---|---|---|---|
SLA | element | - | Binds the SLA related elements and attributes together |
updateFrequency | element | Options for unit are: milliseconds, seconds, minutes, days, weeks, months, years, never, null. Value attribute is Integer. |
Name of the quality attribute indicating the timely interval how often data is updated. |
uptime | element | Options for unit are: percentage, string, null. The value attribute can be integer or string "best effort". |
Uptime is the amount of time that a service is online available and operational. Guaranteed uptime is expressed as SLA level and is generally the most important metric to measure the quality of a hosting provider. An SLA level of 99.99% for example equates to 52 minutes and 36 seconds of downtime per year. in this context uptime is SLA. |
responseTime | element | Unit options are: milliseconds, seconds, null. Value can be integer or null |
Response time is the total amount of time it takes to respond to a request for service. |
support | element | - | Support element describes how the customer can reach for help in case of difficulties in usage, billing, or otherwise. Support can be based on company provided support and community driven support. |
phoneNumber | string | - | The support phone number |
phoneServiceHours | string | - | Describes the service hours company provides. Contains information often in week level eg Mon-Fri at 8am - 4pm. |
chatURL | URL | Valid URL | The URL of chat service to use. Service hours and response time defined in other attributes. |
chatServiceHours | string | - | Describes the chat service hours company provides. Contains information often in week level eg Mon-Fri at 8am - 4pm. |
chatResponseTime | string | - | Describes aimed maximum delay in responding to chat support requests. This doesn't normally guarantee a resolution to the problem. |
string | - | Email information for support requests. | |
emailServiceHours | string | - | Describes the email service hours company provides. Contains information often in week level eg Mon-Fri at 8am - 4pm. |
emailResponseTime | string | - | Describes aimed maximum delay in responding to email support requests. This doesn't normally guarantee a resolution to the problem. |
documentationURL | URL | - | URL to the documentation of the product. |
guidesURL | URL | Valid URL | URL to the guides offering more information and examples about how to use the data product. Guides might be platform specific. |
community | Element | - | Element that contains community based support function information. |
stackoverflowURL | URL | Valid URL | URL to the Stack Overflow. Could be for example list of resolved issues related to the product. |
forumURL | URL | Valid URL | URL to the community forum in which product related support requests can be raised. |
slackURL | URL | Valid URL | URL to the Slack workspace in which product related support requests can be raised. |
twitterURL | URL | Valid URL | URL to the Twitter account for which product related support requests can be raised. |
observability | element | - | Observability is a superset of monitoring. It provides not only high-level overviews of the system’s health but also highly granular insights into the implicit failure modes of the system. In addition, an observable system furnishes ample context about its inner workings, unlocking the ability to uncover deeper, systemic issues. |
healthStatus | boolean | true/false | The usability of the data product can be determined through (an automated) review that assesses the data quality, accuracy, and other characteristics to ensure its usability. In this process, a value of 'true' indicates that the review has deemed the product to be usable. Conversely, a value of 'false' signifies that the automated review has identified deficiencies or errors that render the product unusable. |
logsURL | URL | Valid URL | URL to service which offers access to event logs including errors, response times, call information. |
dashboardURL | URL | Valid URL | URL to dashboard application which visualizes product behaviour. This service should support at least part of the given product quality indicators. |
uptimeURL | URL | Valid URL | URL to service which shows uptime statistics as well as other statistical information. This service should support at least part of the given product quality indicators. |
If you see something missing, described inaccurately or plain wrong, or you want to comment the specification, raise an issue in Github
Data Quality
Data quality is essential for one main reason: You give customers the best experience when you make decisions using accurate data. A great customer experience leads to happy customers, brand loyalty, and higher revenue for your business. Information is only valuable if it is of high quality. How can you assess your data quality? Data quality meets six dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness.
The values of the QA attributes are given by the vendor. Should you trust in the values, is the choice made by the data consumer. If possbile utilize automatic checking of data quality against the source and update the values accordingly.
The QA object is general in nature and should be enough for common (80%) of the use cases. Note that you can make extensions to the standard with "x-" mechanism in order to fulfill any industry specific needs. The "Specification extensions" section provides details on how to use this feature.
Data integrity is the maintenance of, and the assurance of, data accuracy and consistency over its entire life-cycle. That is why integrity is not in the attributes, but accuracy and consistency as well as completeness are.
Optional attributes and elements
Example of Data Quality component with some of the voluntary attributes:
dataQuality:
- dimension: accuracy
objective: 98
unit: percentage
monitoring:
type: SodaCL
spec:
- require_unique(member_id)
- require_range(age_band, 18, 100)
- dimension: completeness
objective: 98
unit: percentage
monitoring:
type: SodaCL
spec:
- for each column:
name: [member_id, gender, age_band]
checks:
- not null:
fail: when > 2% # Fail if more than 2% of records are null
- dimension: consistency
objective: 98
unit: percentage
- dimension: timeliness
objective: 100
unit: percentage
- dimension: validity
objective: 98
unit: percentage
- dimension: uniqueness
objective: 100
unit: percentage
Element name |
Type | Options | Description |
---|---|---|---|
dataQuality | element | - | Contains array of data quality dimensions with optional computational monotoring object. Binds the data quality related elements and attributes together |
dimension | attribute | string, one of | Defines the data quality dimension. Can be one of: accuracy, completeness, consistency, timeliness, validity, or uniqueness |
objective | attribute | integer | Defines the target value for the data quality dimension |
unit | attribute | string | Defines the unit used in stating the target quality level. One of: percentage, number |
monitoring | element | - | Contains the monitoring (computational "as code") structure to validate target state for the selected data quality dimension. |
type | attribute | string | monitoring system name name such as SodaCL and Montecarlo. The systems enable as code approach to monitor data quality. |
spec | element | - | contains the as code part for monitoring. Content is intended to be in a form that can be injected as is to defined monitoring system. |
If you see something missing, described inaccurately or plain wrong, or you want to comment the specification, raise an issue in Github
Data Licensing
The data product may be exploited e.g. by licensing its use and exploitation to third parties. Machine-readable license as part of the specification is implemented for this purpose. It can be used to conclude various agreements regarding data protection, processing and intellectual property rights (IPR). Data can be protected by one or more intellectual property rights. Principle is that when a third party (Data User) exploits the data, it must have a license or other right from Data Holder to exploit to the data.
Optional attributes and elements
Example of License Object usage:
license:
scope:
definition: The purpose of this license is to determine the terms and conditions
applicable to the licensing of the data product, whereby Data Holder grants
Data User the right to use the data.
language: en-us
restrictions: Data User agrees not to, directly or indirectly, participate in
the unauthorized use, disclosure or conversion of any confidential information.
geographicalArea:
- EU
- US
permanent: False
exclusive: False
rights:
- Reproduction
- Display
- Distribution
- Adaptation
- Reselling
- Sublicensing
- Transferring
termination:
noticePeriod: 90
terminationConditions: After the expiry of the right
of use, the product and its derivatives must be removed.
continuityConditions: Expired license will automatically continued without written
cancellation (termination) by Data Holder
governance:
ownership: Mindmote Oy, a company specializing in pet industry insights, owns
the license to its proprietary data product 'Pets of the Year'.
damages: During the term of license, except for the force majeure or the Data
Holders reasons, Data User is required to follow strictly in accordance with
the license. If Data User wants to terminate the license early, it needs to
pay a certain amount of liquidated damages.
confidentiality: Data User undertakes to maintain confidentiality as regards all
information of a technical (such as, by way of a non-limiting example, drawings,
tables, documentation, formulas and correspondence) and commercial nature (including
contractual conditions, prices, payment conditions) gained during the performance
of this license.
applicableLaws: This license shall be interpreted, construed and enforced in accordance
with the law of Finland, including Copyright Act 404/1961.
warranties: Data Holder makes no warranties, express or implied, guarantees or
conditions with respect to your use of the data product. To the extent permitted
under local law, Data Holder disclaims all liability for any damages or losses,
including direct, consequential, special, indirect, incidental or punitive,
resulting from Data User use of the data product.
audit: Data Holder will reasonably cooperate with Data Users by providing available
additional information about the data product. Both parties will bear their
own audit-related costs.
forceMajeure: Both parties may suspend their contractual obligations when fulfillment
becomes impossible or excessively costly due to unforeseeable events beyond
their control, such as strikes, fires, wars, and other force majeure events.
Element name |
Type | Options | Description |
---|---|---|---|
license | element | - | Binds the licensing related elements and attributes together. |
scope | element | - | Extent, range, coverage, area or space of the license. |
definition | string | text content, max length 512 chars | Background and purpose of the license. |
language | string | ISO 639-1 standard language codes | License language. |
restrictions | string | text content, max length 512 chars | Restrictions of the license. |
geographicalArea | string | ISO 3166-1 alpha-2 codes | License right restricted to the geographical area. |
permanent | boolean | true/false | License with no expiration date. |
exclusive | boolean | true/false | The exclusive license holder is given complete control over the use of the data product, and no other person or organization is allowed to use it during the term of the license agreement. |
rights | array | Options: Reproduction (rights to reproduce), Display (disclose data to others), Distribution (right to distribute), Adaptation (right for derivate work), Reselling (right to resell), Transferring (transferable data license), Sublicensing (license grant may include a right to sublicense) | Rights granted by the licence. The texts in brackets and italic are intended to describe rights. |
termination | element | - | Licence termination and continuity related conditions. |
noticePeriod | integer | unit is days | The notice period is a particular time that an data product provider or consumer must give before ending the contract. This time window allows both sides to make the necessary preparations, guaranteeing an unhindered transfer. |
terminationConditions | string | text content, max length 512 chars | Cancellation conditions of the license. |
continuityConditions | string | text content, max length 512 chars | Continuity conditions of the license. |
governance | element | - | Governance is the approach taken to ensure that the agreed outcomes are being fulfilled. |
ownership | string | text content, max length 512 chars | Data product licensing ownership. |
audit | string | text content, max length 512 chars | License auditing terms. |
warranties | string | text content, max length 512 chars | License warranties. |
damages | string | text content, max length 512 chars | Damages refers to the sum of money (i.e. indemnifications) for a breach of some duty or violation of license right. |
confidentiality | string | text content, max length 512 chars | Restrictions and requirements imposed on the Data User regarding e.g. the use and disclosure of the Data Holder's confidential information. |
applicableLaws | string | text content, max length 512 chars | Applicable laws not covered in applicaplePrivacyLaws, i.e local acts, degrees or law. |
forceMajeure | string | text content, max length 512 chars | Force majeure is a clause that is included in contracts to remove liability for unforeseeable and unavoidable catastrophes that interrupt the expected course of events and prevent participants from fulfilling obligations. These clauses generally cover both natural disasters and catastrophes created by humans. |
Data Holder
DataHolder Object describes the Organization legally allowed to create, develop and publish data products.
Data holder means "a legal person, public body, international organisation, or a natural person who is not a data subject with respect to the specific data in question, which, in accordance with applicable Union or national law, has the right to grant access to or to share certain personal data or non-personal data." (Data Governance Act)
The data holder might not be the original IPR owner of the data used, but has rights operate with it. The contract or other agreement between Provider and possible data owner is not part of the standard as metadata or licence wise.
Mandatory attributes are listed in separate table and marked with REQUIRED text. Next to the mandatory attributes is an example.
The same logic applies to the optional attributes as well. Optional attributes are listed in own table and an example is given in the right column.
Mandatory attributes and elements
Example of Holder component mandatory attributes usage:
dataHolder:
legalName: MindMote Oy
businessId: 12243434-12
email: contact@mindmote.fi
Element name |
Type | Options | Description |
---|---|---|---|
dataHolder | element | - | REQUIRED Binds the provider related business elements and attributes together |
legalName | string | text content, max length 256 chars | REQUIRED The official name of the organization, e.g. the registered company name. |
businessID | string | As defined in RFC 5322 | REQUIRED The business identifier code of the company. Often this is given to the company by authorized public sector organization managing register of businesses. |
string | - | REQUIRED Email to be used in contacting the organization. |
If you see something missing, described inaccurately or plain wrong, or you want to comment the specification, raise an issue in Github
Optional attributes and elements
Example of Holder component with some of the voluntary attributes:
dataHolder:
taxID: 12243434-12
vatID: 12243434-12
businessDomain: Product catalogs
logoURL: https://mindmote.fi/logo.png
description: Digital Economy services and tools
URL: https://mindmote.fi
telephone: "+35845 0232 2323"
streetAddress: Koulukatu 1
postalCode: 33100
addressRegion: Pirkanmaa
addressLocality: Tampere
addressCountry: Finland
aggregateRating: ''
ratingCount: 1245
slogan: ''
parentOrganization: ''
Element name |
Type | Options | Description |
---|---|---|---|
taxID | string | - | The Tax / Fiscal ID of the organization or person, e.g. the TIN in the US or the CIF/NIF in Spain. |
vatID | string | - | The Value-added Tax ID of the organization or person. |
businessDomain | string | - | In a data mesh architecture, data (or data product) ownership and management are distributed across self-contained business domains. |
logoURL | URL | Valid URL. See more from RFC 3986. | The URL pointing to organisation logo. |
description | string | Max length 512 chars | The introduction to the organization. Often contains information of what the organisation does and focuses on. |
URL | URL | Valid URL. See more from RFC 3986. | The URL of the organization's website. |
telephone | string | Valid telephone number | The telephone number. Use E.164 standard. |
streetAddress | string | - | The street address. For example, 1600 Amphitheatre Pkwy. |
postalCode | string | - | The postal code. For example, 94043. |
addressRegion | string | - | The region in which the locality is, and which is in the country. For example, California or another appropriate first-level Administrative division |
addressLocality | string | - | The locality in which the street address is, and which is in the region. For example, Mountain View. |
addressCountry | string | two-letter ISO 3166-1 alpha-2 country code | The country. |
aggregateRating | string | - | The average rating based on multiple ratings or reviews. |
ratingCount | integer | - | The amount of ratigns and reviews used in calculating the aggregateRating. |
slogan | string | Max length 256 chars | The slogan of the organization. This is often related to showing the brand |
parentOrganization | string | - | The larger organization that this organization is a subOrganization of, if any. |
If you see something missing, described inaccurately or plain wrong, or you want to comment the specification, raise an issue in Github
Specification extensions
While the Open Data Product Specification tries to accommodate most use cases, additional data can be added to extend the specification at certain points.
The extensions properties are implemented as patterned fields that are always prefixed by "x-". The extensions may or may not be supported by the available tooling, but those may be extended as well to add requested support (if tools are internal or open-sourced). Open Data Product Initiative Technical Steering Committee does not officially approve external extensions - they are fully independent. Popular extensions however are natural candidates for future additions of the standard.
We encourage you to let us know of useful extensions so that we can consider those in the future releases, raise an issue in Github
Example of extension usage:
product:
name: Pets of the year
productID: 123456are
description: ''
x-internal-id: foobar123
Element name |
Type | Options | Description |
---|---|---|---|
^x- | any | Allows extensions to the Open Data Product Schema. The field name MUST begin with x-, for example, x-internal-id. The value can be null, a primitive, an array or an object. Can have any valid JSON format value. |
Hello world example
You'll find a complete machine-readbale example of a data product from the right column. It is imaginary data product Pets of the year which contains derived data about the most common pets in the world. The product has 4 pricing plans which are mostly based on recurring subscription model. Note! Not all voluntary attributes are used in the example and multilingualism has not been fully applied.
Example of complete working Data Product specification instance:
---
schema: https://raw.githubusercontent.com/Open-Data-Product-Initiative/open-data-product-spec-dev/ddbc069196a664d0e28a0f3dc7c1c7fb49b64591/source/schema/odps-dev-json-schema.json
version: dev
product:
en:
name: Pets of the year
productID: 123456are
valueProposition: Design a customised petstore using a data product that describes
pets with their habits, preferences and characteristics.
description: This is an example of a Petstore product.
productSeries: Lovely pets data products
visibility: private
status: draft
version: '0.1'
categories:
- pets
standards:
- ISO 24631-6
tags:
- pet
brandSlogan: Passion for the data monetization
type: derived data
logoURL: https://data-product-business.github.io/open-data-product-spec/images/logo-dps-ebd5a97d.png
OutputFileFormats:
- json
- xml
- csv
- zip
useCases:
- useCase:
useCaseTitle: Build attractive and lucrative petstore!
useCaseDescription: Use case description how succesfull petstore chain was
established in Abu Dhabi
useCaseURL: https://marketplace.com/usecase1
recommendedDataProducts:
- https://marketplace.com/dataproduct.json, https://marketplace.com/dataproduct-another.json
pricingPlans:
en:
- name: Premium subscription 1 year
priceCurrency: EUR
price: '50.00'
billingDuration: year
unit: recurring
maxTransactionQuantity: unlimited
offering:
- item 1
- name: Premium Package Monthly
priceCurrency: EUR
price: '5.00'
billingDuration: month
unit: recurring
maxTransactionQuantity: 10000
offering:
- item 1
- name: Freemium Package
priceCurrency: EUR
price: '0.00'
billingDuration: month
unit: recurring
maxTransactionQuantity: 1000
offering:
- item 1
- name: Revenue sharing
priceCurrency: percentage
price: '5.50'
billingDuration: month
unit: revenue-sharing
maxTransactionQuantity: 20000
offering:
- item 1
dataOps:
infrastructure:
platform: Azure
storageTechnology: Azure SQL
storageType: sql
containerTool: helm
format: yaml
schemaLocationURL: http://http://192.168.10.1/schemas/2016/petshopML-2.3/schema/petstore.xsd
scriptURL: http://192.168.10.1/rundatapipeline.yml
deploymentDocumentationURL: http://192.168.10.1/datapipeline
dataLineageTool: Collibra
dataLineageOutput: http://192.168.10.1/lineage.json
hashType: SHA-2
checksum: 7b7444ab8f5832e9ae8f54834782af995d0a83b4a1d77a75833eda7e19b4c921
dataAccess:
type: API
authenticationMethod: OAuth
specification: OAS
format: JSON
documentationURL: https://swagger.com/petstore.json
SLA:
updateFrequency:
unit: hours
value: 1
uptime:
unit: percentage
value: 99
responseTime:
unit: milliseconds
value: 200
nullValues:
unit: percentage
value: 0.01
support:
company:
phoneNumber: ''
phoneServiceHours: ''
chatURL: ''
chatServiceHours: ''
chatResponseTime: ''
email: ''
emailServiceHours: ''
emailResponseTime: ''
documentationURL: ''
guidesURL: ''
community:
stackoverflowURL: ''
forumURL: ''
slackURL: ''
twitterURL: ''
observability:
logsURL: https://logs.com
dashboardURL: https://dashboard.com
uptimeURL: https://uptime.com
dataQuality:
accuracy: 100
completeness: 100
consistency: 100
timeliness: high
validity: 100
uniqueness: 100
dataQualityAssuranceMethods: Data quality assurance suite of tools and methods
include both data quality auditing (DQA) tools designed for use by external
audit teams and routine data quality assessment (RDQA) tools designed for capacity
building and self-assessment.
dataQualityMonitoring: Soda
monitoringScriptURL: http://192.168.10.1/soda-petshop.py
license:
scope:
definition: The purpose of this license is to determine the terms and conditions
applicable to the licensing of the data product, whereby Data Holder grants
Data User the right to use the data.
language: en-us
restrictions: Data User agrees not to, directly or indirectly, participate in
the unauthorized use, disclosure or conversion of any confidential information.
geographicalArea:
- EU
- US
permanent: false
exclusive: false
rights:
- Reproduction
- Display
- Distribution
- Adaptation
- Reselling
- Sublicensing
- Transferring
termination:
terminationConditions: Cancellation before 30 days. After the expiry of the
right of use, the product and its derivatives must be removed.
continuityConditions: Expired license will automatically continued without written
cancellation (termination) by Data Holder
governance:
ownership: Mindmote Oy, a company specializing in pet industry insights, owns
the license to its proprietary data product 'Pets of the Year'.
damages: During the term of license, except for the force majeure or the Data
Holders reasons, Data User is required to follow strictly in accordance with
the license. If Data User wants to terminate the license early, it needs to
pay a certain amount of liquidated damages.
confidentiality: Data User undertakes to maintain confidentiality as regards
all information of a technical (such as, by way of a non-limiting example,
drawings, tables, documentation, formulas and correspondence) and commercial
nature (including contractual conditions, prices, payment conditions) gained
during the performance of this license.
applicableLaws: This license shall be interpreted, construed and enforced in
accordance with the law of Finland, including Copyright Act 404/1961.
warranties: Data Holder makes no warranties, express or implied, guarantees
or conditions with respect to your use of the data product. To the extent
permitted under local law, Data Holder disclaims all liability for any damages
or losses, including direct, consequential, special, indirect, incidental
or punitive, resulting from Data User use of the data product.
audit: Data Holder will reasonably cooperate with Data Users by providing available
additional information about the data product. Both parties will bear their
own audit-related costs.
forceMajeure: Both parties may suspend their contractual obligations when fulfillment
becomes impossible or excessively costly due to unforeseeable events beyond
their control, such as strikes, fires, wars, and other force majeure events.
dataHolder:
taxID: 12243434-12
vatID: 12243434-12
businessDomain: Data Product Business
logoURL: https://mindmote.fi/logo.png
description: Digital Economy services and tools
URL: https://mindmote.fi
telephone: "+358 45 232 2323"
streetAddress: Koulukatu 1
postalCode: '33100'
addressRegion: Pirkanmaa
addressLocality: Tampere
addressCountry: Finland
aggregateRating: ''
ratingCount: 1245
slogan: ''
parentOrganization: ''
Mandatory-only example
Example data product with just the mandatory elements and attributes. This is the minimal representation of a data product metadata that is expected to be found from every data product following ODPS standard. This bare minimum can be expanded with other elements and attributes defined in the specification. Also the possibilty to use extensions exists if local additions are needed.
Example of mandatory-only elements and attributes Open Data Product specification instance:
schema: https://raw.githubusercontent.com/Open-Data-Product-Initiative/open-data-product-spec-dev/ddbc069196a664d0e28a0f3dc7c1c7fb49b64591/source/schema/odps-dev-json-schema.json
version: dev
product:
en:
name: Pets of the year
productID: 123456are
visibility: private
status: draft
type: derived data
pricingPlans:
en:
- name: Freemium Package
priceCurrency: EUR
price: '0.00'
billingDuration: month
unit: recurring
maxTransactionQuantity: 1000
offering:
- item 1
dataHolder:
legalName: MindMote Oy
businessId: 12243434-12
email: contact@mindmote.fi
Terms used
Here's list of terms used and what we mean with them. The meaning of terms is mostly taken from existing knowledge eg articles and other trusted sources. The source is always linked to the term. In some rare cases term is defined for the specification purposes only.
Term |
Description |
---|---|
Data point | A data point refers to a single, individual unit of data. It can be a number, a word, a measurement, or any other piece of information that is recorded and used for analysis. Some mix the data point with a dataset. In a dataset, each data point represents one observation or measurement. For example, in a dataset of temperatures recorded every day, each daily temperature is a data point. |
Data product | As a strategic resource for companies, data is considered an asset that, like any other material good, has a financial value and whose management generates costs. Data created, collected or used in individual business processes can be sold to other organisations as raw or processed data, so that it no longer serves as an enabler of products, but is the product itself. This leads to the paradigm that data assets can be monetised by exchanging and trading data between organisations as data products and services. There are multiple definitions for data product. In an article authored by Jian Pei (2020), data products "refer to data sets as products and information services derived from data sets." Simon O'Regan's defines data product as a product whose primary objective is to use data to facilitate an end goal. From the academic literature we have found several subtypes of data products: raw data, derived data, data sets, reports, analytic views, 3D visualisations, algorithms, decision support (dashboards) and automated decision-making (Netflix product recommendations or Spotify’s Discover Weekly would be common examples). Typically raw data, derived data and algorithms have technical users. Most often they tend to be internal products in an organisation. If we dive in the data mesh world, this quote from Zhamak Dehghani’s book is key to understand the definition of data as a product: “Domain data teams must apply product thinking […] to the datasets that they provide; considering their data assets as their products and the rest of the organization’s data scientists, ML and data engineers as their customers.” While many of the standard Product Development Rules apply — solve a customer need, learn from feedback, prioritise relentlessly, etc. — data has different characteristics compared to tangible products that prevent the direct transfer of established processes and rules of trading goods, especially in terms of pricing mechanisms. In trading data, there is less willingness to pay. For example, data buyers often do not recognise the potential value of data items because it cannot be fully disclosed prior to purchase (known as the ‘Arrow paradox’). In addition, there is often a lack of notion that the creation, processing, storage and distribution of high-quality data is a major cost factor for the data provider. Another obstacle is the lack of trust and security causing potential data providers to fear that competitors could benefit from disclosure of in-house data. One of the aims of this specification is to tackle above mentioned issues which hinder the growth of data ecosystem and market volatility. |
Data as a service | In computing, data as a service, or DaaS, is a term used to describe cloud-based software tools used for working with data, such as managing data in a data warehouse or analyzing data with business intelligence. It is enabled by software as a service (SaaS). DaaS like all "as a service" (aaS) technology, builds on the concept that its data product can be provided to the user on demand, regardless of geographic or organizational separation between provider and consumer. According to Daniel Newman from Forbes (2017) DaaS is essentially a data stream that subscribers can access on demand. Some people use the term data product in a meaning which contains also data commodities which have more service alike attributes than product attributes. In those cases we prefer to use the term data as a service and call the creation process as data servitization. The term productizement is reserved for the process which creates data products as end result. |
Data as a service business model | Data as a service as a business model is a concept when two or more organizations buy, sell, or trade machine-readable data in exchange for something of value. Data as a service is a general term that encompasses data-related services. Now DaaS service providers are replacing traditional data analytics services or happily clustering with existing services to offer more value-addition to customers. The DaaS providers are curating, aggregating, analyzing multi-source data in order to provide additional more valuable analytical data or information. Typically, DaaS business is based on subscriptions and customers pay for a package of services or definite services. |
Data pipeline | According to Aiswarya et al. the complex chain of interconnected activities or processes from data gen- eration through data reception constitutes a data pipeline. In other words, data pipelines are the connected chain of processes where the output of one or more processes becomes an input for another. It is a piece of software that removes many manual steps from the workflow and permits a streamlined, automated flow of data from one node to another. Moreover, it automates the operations involved in the selection, extraction, transformation, aggregation, validation, and loading of data for further analysis and visualization. It offers end to end speed by removing errors and resisting bottlenecks or delay. Data pipelines can process multiple streams of data simultaneously. |
Infrastructure as Code | Infrastructure as Code (IaC) transforms infrastructure management by using code instead of manual processes. Configuration files capture infrastructure specifications, ensuring consistent environment provisioning. The "as code" paradigm extends beyond infrastructure to encompass quality control and data product processes. This approach, applied to the entire data pipeline, enhances repeatability, traceability, and scalability, fostering collaboration and systematic data management. |
DataOps | In DataOps, the focus lies in creating automated processes for releasing and updating data products throughout their lifecycle, from development to production. This automation spans the entire journey from development to transitioning into production. The objective is to enhance operational efficiency through automation, reduce errors, and enable faster release cycles for data products. |
Editors and contributors
This specification is openly developed and a lot of the work comes from community. We list all community contributors as a sign of appreciation. The editors (as initial creators of the the specification) are Jarkko Moilanen and Jussi Niilahti. Editors take the feedback and draft new candidate releases, which may become the versions of the specification.
List of community contributors
The work around the specification would not be possible without enormous help from the community. Here's list of contributors so far.
- Toni Luhti
- Topi Santakivi
- Antti Loukiala