Best Data Pipeline Software for IRI Voracity

Find and compare the best Data Pipeline software for IRI Voracity in 2024

Use the comparison tool below to compare the top Data Pipeline software for IRI Voracity on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Apache Kafka Reviews

    Apache Kafka

    The Apache Software Foundation

    1 Rating
    Apache Kafka®, is an open-source distributed streaming platform.
  • 2
    Actifio Reviews
    Integrate with existing toolchain to automate self-service provisioning, refresh enterprise workloads, and integrate with existing tools. Through a rich set APIs and automation, data scientists can achieve high-performance data delivery and re-use. Any cloud data can be recovered at any time, at any scale, and beyond legacy solutions. Reduce the business impact of ransomware and cyber attacks by quickly recovering with immutable backups. Unified platform to protect, secure, keep, govern, and recover your data whether it is on-premises or cloud. Actifio's patented software platform turns data silos into data pipelines. Virtual Data Pipeline (VDP), provides full-stack data management - hybrid, on-premises, or multi-cloud -- from rich application integration, SLA based orchestration, flexible movement, data immutability, security, and SLA-based orchestration.
  • 3
    Apache Airflow Reviews

    Apache Airflow

    The Apache Software Foundation

    Airflow is a community-created platform that allows programmatically to schedule, author, and monitor workflows. Airflow is modular in architecture and uses a message queue for managing a large number of workers. Airflow can scale to infinity. Airflow pipelines can be defined in Python to allow for dynamic pipeline generation. This allows you to write code that dynamically creates pipelines. You can easily define your own operators, and extend libraries to suit your environment. Airflow pipelines can be both explicit and lean. The Jinja templating engine is used to create parametrization in the core of Airflow pipelines. No more XML or command-line black-magic! You can use standard Python features to create your workflows. This includes date time formats for scheduling, loops to dynamically generate task tasks, and loops for scheduling. This allows you to be flexible when creating your workflows.
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