Apache Airflow
Scale your data processing with Apache Airflow
Airflow ETL (Extract, Transform, Load) is an open-source platform developed by Apache for creating, scheduling, and monitoring data pipelines. It enables data engineers to define workflows as code, which can be executed on any cloud-based, on-premises or hybrid infrastructure. Airflow ETL is widely used in data engineering to manage ETL pipelines, data warehousing, data processing, and data analytics.
Airflow ETL Features
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It is open-source platform that allows workflows to be created, scheduled, and monitored programmatically.
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Platform-agnostic and can run on any infrastructure, including on-premises, cloud, and hybrid environments.
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Intuitive web interface for visualizing workflows, tracking progress, and troubleshooting issues.
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Modular architecture for easy extension with custom operators, sensors, and hooks.
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Rich ecosystem of plugins and integrations for connecting to a wide range of data sources and services.
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Highly scalable and capable of handling large volumes of data and complex workflows.
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Built-in operators for common tasks such as file manipulation, database operations, and email sending.
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Powerful and flexible scheduling system for defining complex dependencies and triggering workflows based on events or time intervals.
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Robust security model for controlling access to workflows, data, and resources.
Highlights of our Airflow ETL Services
Create intricate data pipelines.
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Extracted data from Third-party through API calls.
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Extracted data from ZOHO , Twilio etc
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Experienced in the setup of airflow and the upgrading of versions.
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Data handling on a large scale.
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Handled complex JSON parsing.
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Extract data and load it into AWS Redshift or Snowflake from sources like MongoDB, MySQL, and other databases.
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Use SFTP and FTP interfaces to manage huge volume data files.
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Work in different file formats like CSV, JSON, XML and FFR files for data transformation.
The open-source platform for workflow management
Apache Airflow Business Use Cases
Implemented AWS SNS Notification to notify if the jobs fail or succeed and send it through an email or data channels using the API’s.
A data migration was performed between two databases using operators.
In order to avoid issues, the Error Handling Mechanism was implemented at each stop of the transformation.
The data was extracted from the airflow log and the running time, row counts, etc. of the scheduled job were provided for Analytics.
Conversion between different file formats like FFR – XML.
Manage your workflows with ease
Scale your data processing with Apache Airflow
Are you tired of managing complex data pipelines and workflows that are difficult to scale and maintain? Look no further! Apache Airflow is here to revolutionize your workflow management process and take it to the next level.
FAQs
1. What is Apache Airflow?
Apache Airflow is an open-source platform used for orchestrating and scheduling workflows. It allows users to define and manage complex workflows as directed acyclic graphs (DAGs).
2. What is the use of Airflow?
Airflow allows you to author, schedule, and manage processes dynamically. These workflows can assist you with transferring data from one source to another, dataset filtering, data policies application, data manipulation, monitoring, and even launching services to initiate database management operations.
3. Is Airflow an ETL tool?
It is not an ETL tool per se, but it uses Directed Acyclic Graphs (DAGs) to manage, structure, and arrange ETL pipelines.