DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Refcards Trend Reports Events Over 2 million developers have joined DZone. Join Today! Thanks for visiting DZone today,
Edit Profile Manage Email Subscriptions Moderation Admin Console How to Post to DZone Article Submission Guidelines
View Profile
Sign Out
Refcards
Trend Reports
Events
Zones
Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
  1. DZone
  2. Data Engineering
  3. Data
  4. Generating AVRO Schemas for Data and Making Sure Names Are Correct

Generating AVRO Schemas for Data and Making Sure Names Are Correct

Learn how to use Apache NiFi to generate AVRO schemas while ensuring that the field names meet strict naming conventions.

Tim Spann user avatar by
Tim Spann
CORE ·
Dec. 13, 17 · Tutorial
Like (2)
Save
Tweet
Share
19.06K Views

Join the DZone community and get the full member experience.

Join For Free

Building schemas is tedious work and is often fraught with errors. The InferAvroSchema processor can get you started. It generates a compliant schema for use. There is one caveat: you have to make sure you are using Apache Avro-safe field names. I have a custom processor that will clean your attributes if you need them to be Avro-safe. See the processor listed below.

Example flow utilizing InferAvroSchema:

InferAvroSchema details:

The steps are as follows:

  1. Use Apache NiFi to convert data to JSON or CSV.

  2. Send JSON or CSV data to InferAvroSchema. I recommend setting the output destination to flowfile-attribute, input content type to json, and the pretty Avro output to true.

  3. The new schema is now in the following attribute format: inferred.avro.schema.

inferred.avro.schema    
{ "type" : "record", "name" : "schema1", 
 "fields" : [ { 
   "name" : "table", "type" : "string", 
   "doc" : "Type inferred from '\"schema1.tableName\"'" } ] 
}   

This schema can then be used for conversions directly or can be stored in Hortonworks Schema Registry or Apache NiFi's built-in Avro Registry.

Now, you can use it for ConvertRecord, QueryRecord, and other Record processing.

Example generated schema in Avro-JSON format stored in Hortonworks Schema Registry:

Source

And that's it!

Schema Data (computing) avro

Opinions expressed by DZone contributors are their own.

Popular on DZone

  • Data Mesh vs. Data Fabric: A Tale of Two New Data Paradigms
  • GPT-3 Playground: The AI That Can Write for You
  • Problems of Cloud Cost Management: A Socio-Technical Analysis
  • DevSecOps Benefits and Challenges

Comments

Partner Resources

X

ABOUT US

  • About DZone
  • Send feedback
  • Careers
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 600 Park Offices Drive
  • Suite 300
  • Durham, NC 27709
  • support@dzone.com
  • +1 (919) 678-0300

Let's be friends: