Best Data Pipeline Software for Meltano

Stitch is a cloud-based platform that allows you to extract, transform, load data. Stitch is used by more than 1000 companies to move billions records daily from SaaS databases and applications into data warehouses or data lakes.

Apache Kafka®, is an open-source distributed streaming platform.

Data teams can collaborate as software engineering teams by using version control, quality assurance, documentation, and modularity. Analytics errors should be treated as serious as production product bugs. Analytic workflows are often manual. We believe that workflows should be designed to be executed with one command. Data teams use dbt for codifying business logic and making it available to the entire organization. This is useful for reporting, ML modeling and operational workflows. Built-in CI/CD ensures data model changes are made in the correct order through development, staging, production, and production environments. dbt Cloud offers guaranteed uptime and custom SLAs.

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.