Key Difference 1: Approach to Data Pipeline Orchestration:
Airflow and Flyway have different approaches when it comes to data pipeline orchestration. Airflow is primarily designed for scheduling and executing workflows, allowing users to define dependencies between tasks and monitor the status of their workflows. On the other hand, Flyway is focused on database version control, automating the migration of SQL scripts to ensure that database schemas are always up-to-date.
Key Difference 2: Domain of Application:
Airflow is widely used for data engineering tasks, such as ETL (Extract, Transform, Load) processes, and is often integrated with popular big data technologies like Apache Hadoop and Apache Spark. Flyway, on the other hand, is specifically tailored for database migration tasks, making it a popular choice among database administrators and developers working on database-centric projects.
Key Difference 3: Workflow Definition Language:
Airflow uses Python as its workflow definition language, allowing users to define complex workflows using Python code. This provides flexibility and the ability to leverage the rich ecosystem of Python libraries. Flyway, on the other hand, uses SQL-based migration scripts for defining the database schema changes. While it may not offer the same level of flexibility as Airflow's Python-based approach, it simplifies the process for database migrations.
Key Difference 4: Component Architecture:
Airflow follows a modular architecture, where components like scheduler, web server, and executor can be distributed across different machines or containers. This allows for scalable and distributed execution of workflows. Flyway, however, is a standalone tool that is typically executed locally on the developer's machine or as part of a database deployment pipeline.
Key Difference 5: Monitoring and Alerting Capabilities:
Airflow provides a rich set of monitoring and alerting capabilities, allowing users to track the progress of their workflows, set up alert notifications for failures, and visualize the execution history through its user interface. Flyway, being focused on database migrations, does not provide extensive monitoring and alerting capabilities out of the box. Users might need to rely on external tools or custom scripts for this purpose.
Key Difference 6: Community and Ecosystem:
Airflow has a larger and more active community, with a wide range of contributors and a vibrant ecosystem of plugins and extensions. This makes it easier to find support, share knowledge, and discover additional functionality. Flyway, while it does have a community, has a relatively smaller footprint compared to Airflow.
In Summary, Airflow and Flyway have key differences in their approach to data pipeline orchestration, domain of application, workflow definition language, component architecture, monitoring and alerting capabilities, and community and ecosystem size.
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I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted.
I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points.
I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.
It lets you regain control of your database migrations with pleasure and plain sql. Solves only one problem and solves it well. It migrates your database, so you don't have to worry about it anymore.
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
Supported databases: Oracle, SQL Server, SQL Azure, DB2, DB2 z/OS, MySQL, MariaDB, Google Cloud SQL, PostgreSQL, Redshift, Vertica, H2, Hsql, Derby, SQLite;Supported build tools: Maven, Gradle, Ant and SBT;Works on: Windows, Mac OSX, Linux, Java and Android
Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
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Pros & Cons
Pros
13
Superb tool, easy to configure and use
9
Very easy to config, great support on plain sql scripts
6
Is fantastic and easy to install even with complex DB
4
Simple and intuitive
1
Easy tool to implement incremental migration
Cons
3
"Undo Migrations" requires pro version, very expensive
Pros
53
Features
14
Task Dependency Management
12
Beautiful UI
12
Cluster of workers
10
Extensibility
Cons
2
Observability is not great when the DAGs exceed 250
2
Open source - provides minimum or no support
2
Running it on kubernetes cluster relatively complex
1
Logical separation of DAGs is not straight forward