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Airflow vs Amazon RDS: What are the differences?
Introduction:
In the world of data management and processing, Airflow and Amazon RDS are two popular tools that serve different functions. Understanding the key differences between Airflow and Amazon RDS will help users choose the right tool for their specific needs.
Architecture: Airflow is an open-source platform used to programmatically author, schedule, and monitor workflows. It allows users to define workflows as Directed Acyclic Graphs (DAGs) in Python code. On the other hand, Amazon RDS (Relational Database Service) is a managed database service that simplifies database setup, operation, and scaling. It provides easy access to a variety of database engines like MySQL, PostgreSQL, Oracle, and SQL Server.
Functionality: Airflow focuses on orchestrating complex workflows, automating tasks, and managing dependencies between tasks. It provides a rich set of operators for tasks such as BashOperator, PythonOperator, and more. In contrast, Amazon RDS primarily focuses on providing a cloud-based relational database service with features like automated backups, scalability, and security controls.
Deployment Model: Airflow can be deployed on-premises or in the cloud and offers flexibility in terms of infrastructure choices. Users can choose to run Airflow on platforms like AWS, Google Cloud, or Azure. On the other hand, Amazon RDS is a fully managed service provided by AWS, eliminating the need for users to manage the underlying infrastructure. Users can simply launch an RDS instance and start using it.
Scalability: Airflow offers scalability through distributed execution of tasks across a cluster of worker nodes. This horizontally scalable architecture allows users to handle large workloads and increase throughput as needed. Amazon RDS also offers scalability options through features like Multi-AZ deployments for high availability and Read Replicas for read-heavy workloads.
Cost: Airflow is an open-source tool, which means users can set up and run Airflow workflows without incurring licensing costs. However, users need to consider infrastructure costs for hosting Airflow and managing the cluster. On the other hand, Amazon RDS is a paid service where users pay for the compute and storage resources used by their database instances, along with any additional features utilized.
Integration: Airflow provides seamless integration with various data processing tools and services, making it a preferred choice for building data pipelines. It supports connections to databases, cloud storage services, messaging queues, and more. Amazon RDS integrates well with other AWS services like EC2, S3, and Lambda, enabling users to build robust and scalable applications within the AWS ecosystem.
In Summary, understanding the differences between Airflow and Amazon RDS is crucial for choosing the right tool based on specific requirements.
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.
For a non-streaming approach:
You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.
Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation
Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing
Using on-demand read/write capacity while we scale our userbase - means that we're well within the free-tier on AWS while we scale the business and evaluate traffic patterns.
Using single-table design, which is dead simple using Jeremy Daly's dynamodb-toolbox library
Pros of Airflow
- Features53
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of Amazon RDS
- Reliable failovers165
- Automated backups156
- Backed by amazon130
- Db snapshots92
- Multi-availability87
- Control iops, fast restore to point of time30
- Security28
- Elastic24
- Push-button scaling20
- Automatic software patching20
- Replication4
- Reliable3
- Isolation2
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Cons of Airflow
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1
Cons of Amazon RDS
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