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Airflow vs Amazon Athena: What are the differences?
Introduction
Apache Airflow and Amazon Athena are both popular tools used in data processing and analysis. However, they differ in several key aspects. Let's explore the key differences between Airflow and Amazon Athena.
Architecture and Purpose: Apache Airflow is a workflow management platform that allows users to define, schedule, and monitor complex data pipelines. It provides a way to programmatically author, schedule, and monitor workflows. On the other hand, Amazon Athena is an interactive query service that allows users to analyze data in Amazon S3 using standard SQL queries. It is primarily used for ad-hoc querying and analysis of data stored in S3.
Data Sources and Formats: Airflow supports a wide range of data sources, including databases (like MySQL, PostgreSQL), cloud services (like Amazon S3, Google Cloud Storage), and more. It also supports various file formats such as CSV, JSON, Parquet, Avro, etc. On the other hand, Amazon Athena specifically focuses on querying data stored in Amazon S3 using standard SQL. It does not support data sources other than S3.
Data Processing Paradigm: Airflow allows users to define and schedule data processing tasks using a Directed Acyclic Graph (DAG) structure. Tasks can be chained together and dependencies can be defined between them. It provides a visual representation of the workflow and allows for easy monitoring and troubleshooting. Amazon Athena, on the other hand, follows a serverless query processing model where queries run on-demand without the need for provisioning or managing any infrastructure.
Query Performance and Cost: Airflow delegates data processing tasks to specific engines or services, such as Apache Spark or Google Cloud Dataproc, which can provide scalable and high-performance query processing capabilities. The performance of Airflow pipelines can be customized based on the chosen engines and resources allocated. On the other hand, Amazon Athena is optimized for querying data stored in Amazon S3 and leverages Presto, a distributed SQL query engine. While Athena provides on-demand query capabilities, the performance and cost efficiency depend on the size and structure of the data being queried.
Data Transformation and Manipulation: Airflow provides a rich set of operators and hooks that allow users to manipulate data and perform transformations as part of their workflows. Users can write custom Python code or use pre-defined operators to perform tasks like filtering, aggregating, joining, etc. Amazon Athena, however, focuses mainly on querying and analysis of data rather than providing extensive data manipulation capabilities. It is more suited for retrieving and analyzing data rather than transforming it.
Integration with Ecosystem and Services: Airflow integrates well with various external tools and services, such as cloud platforms (like Amazon Web Services, Google Cloud Platform), databases, message brokers, and more. It provides out-of-the-box integration with popular services like Spark, Hive, BigQuery, etc. On the other hand, Amazon Athena is tightly integrated with the AWS ecosystem, making it easy to access and analyze data stored in S3. It works seamlessly with other AWS services like AWS Glue, AWS Lambda, AWS CloudTrail, etc.
In summary, Apache Airflow provides a powerful workflow management platform for defining, scheduling, and monitoring complex data pipelines, while Amazon Athena is a serverless SQL query service specifically designed for analyzing data stored in Amazon S3 using SQL queries. The key differences lie in their architecture, data sources and formats supported, data processing paradigms, query performance and cost, data transformation capabilities, and integration with other tools and services.
Hi all,
Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?
you can use aws glue service to convert you pipe format data to parquet format , and thus you can achieve data compression . Now you should choose Redshift to copy your data as it is very huge. To manage your data, you should partition your data in S3 bucket and also divide your data across the redshift cluster
First of all you should make your choice upon Redshift or Athena based on your use case since they are two very diferent services - Redshift is an enterprise-grade MPP Data Warehouse while Athena is a SQL layer on top of S3 with limited performance. If performance is a key factor, users are going to execute unpredictable queries and direct and managing costs are not a problem I'd definitely go for Redshift. If performance is not so critical and queries will be predictable somewhat I'd go for Athena.
Once you select the technology you'll need to optimize your data in order to get the queries executed as fast as possible. In both cases you may need to adapt the data model to fit your queries better. In the case you go for Athena you'd also proabably need to change your file format to Parquet or Avro and review your partition strategy depending on your most frequent type of query. If you choose Redshift you'll need to ingest the data from your files into it and maybe carry out some tuning tasks for performance gain.
I'll recommend Redshift for now since it can address a wider range of use cases, but we could give you better advice if you described your use case in depth.
It depend of the nature of your data (structured or not?) and of course your queries (ad-hoc or predictible?). For example you can look at partitioning and columnar format to maximize MPP capabilities for both Athena and Redshift
you can change your PSV fomat data to parquet file format with AWS GLUE and then your query performance will be improved
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
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 Athena
- Use SQL to analyze CSV files16
- Glue crawlers gives easy Data catalogue8
- Cheap7
- Query all my data without running servers 24x76
- No data base servers yay4
- Easy integration with QuickSight3
- Query and analyse CSV,parquet,json files in sql2
- Also glue and athena use same data catalog2
- No configuration required1
- Ad hoc checks on data made easy0
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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 Athena
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