Ylem documentation
  • 🗒️General information
    • Introduction to Ylem
    • Quick start guide
    • Release notes
  • 🔬Open-source edition
    • Installation
    • Usage of Apache Kafka
    • Task processing architecture
    • Configuring integrations with .env variables
  • 💡Integrations
    • Connecting an integration
    • Library of integrations
      • Amazon Redshift
      • Apache Kafka
      • APIs
      • Atlassian Jira
      • AWS Lambda
      • AWS RDS
      • AWS S3
      • ClickHouse
      • ElasticSearch
      • E-mail
      • Google Big Query
      • Google Cloud SQL
      • Google Pub/Sub
      • Google Sheets
      • Immuta
      • Incident.io
      • Jenkins
      • Hubspot
      • Microsoft Azure SQL
      • MySQL
      • OpenAI ChatGPT
      • Opsgenie
      • PostgreSQL
      • PlanetScale
      • RabbitMQ
      • Salesforce
      • Slack
      • Snowflake
      • Tableau
      • Twilio. SMS
      • WhatsApp (through Twilio)
    • Initial demo data source
  • 🚡Pipelines
    • Pipeline management
    • Tasks
      • Aggregator
      • API Call
      • Code
      • Condition
      • External trigger
      • Filter
      • For each
      • GPT
      • Merge
      • Notification
      • Query
      • Pipeline runner
      • Processor
      • Transformer
    • Running and scheduling pipelines
    • Library of templates
    • Environment variables
    • Mathematical functions and operations
    • Formatting of messages
  • 📈Statistics and profiling
    • Statistics of runs
    • Slow tasks
  • 📊Metrics
    • Metric management
    • Using previous values of a metric
  • 💼Use cases, patterns, templates, examples
    • Use cases
    • Messaging patterns
      • Datatype Channel
      • Message Dispatcher
      • Messaging Bridge
      • Message Bus
      • Message Filter
      • Message Router
      • Point-to-Point Channel
      • Publish-Subscribe Channel
      • Pull-Push
    • Functional use cases
      • Streaming from Apache Kafka and messaging queues
      • Streaming from APIs
      • Streaming from databases
      • Data orchestration, transformation and processing
      • Usage of Python and Pandas
      • KPI Monitoring
      • OKRs and custom metrics
      • Data Issues & Incidents
      • Reporting
      • Other functional use cases
    • Industry-specific use cases
      • Finance and Payments
      • E-commerce & Logistics
      • Customer Success
      • Security, Risk, and Anti-Fraud
      • Anti-Money Laundering (AML)
  • 🔌API
    • OAuth clients
    • API Reference
  • 👁️‍🗨️Other resources
    • FAQ
    • Our blog on Medium
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  1. Pipelines
  2. Tasks

Pipeline runner

PreviousQueryNextProcessor

Last updated 1 year ago

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The "Run pipeline" task is designed for running pipelines from each other and transferring datasets between them.

It allows you to have smaller and simpler pipelines and reuse them by calling from other ones.

It is a similar concept to the software engineering approach to separate monolithic products into small microservices.

🚡
An example of the pipeline that accepts a dataset from Apache Kafka, splits it into two subsets by filtering, and transfers each subset to a separate pipeline