Today is a short one, but hopefully a valuable devOps tip, if you are currently setting up remote logging integration to S3 of Airflow logs using Airflow version 1.9.0.
Basically this stackoverflow post provides the main solution. However, the current template incubator-airflow/airflow/config_templates/airflow_local_settings.py present in master branch contains a reference to the class “airflow.utils.log.s3_task_handler.S3TaskHandler”, which is not present in apache-airflow==1.9.0 python package. The fix is simple – use rather this base template: https://github.com/apache/incubator-airflow/blob/v1-9-stable/airflow/config_templates/airflow_local_settings.py (and follow all other instructions in the mentioned answer)
Hope this helps!
In this post, I build up on the knowledge shared in the post for creating Data Pipelines on Airflow and introduce new technologies that help in the Extraction part of the process with cost and performance in mind. I’ll go through the options available and then introduce to a specific solution using AWS Athena. First we’ll establish the dataset and organize our data in S3 Buckets. Afterwards, you’ll learn how to make it so that this information is queryable through AWS Athena, while making sure it is updated daily.
Data dump files of not so structured data are a common byproduct of Data Pipelines that include extraction. dumps of not-so-structured data. This happens by design: business-wise and as Data Engineers, it’s never too much data. From an investment stand point, object-relational database systems can become increasingly costly to keep, especially if we aim at keeping performance while the data grows.
Having this said, this is not a new problem. Both Apache and Facebook have developed open source software that is extremely efficient in dealing with extreme amounts of data. While such softwares are written in Java, they maintain an abstracted interface to the data that relies on traditional SQL language to query data that is stored on filesystem storage, such as S3 for our example and in a wide range of different formats from HFDS to CSV.
Today we have many options to tackle this problem and I’m going to go through on how to welcome this problem in today’s serverless world with AWS Athena. For this we need to quickly rewind back in time and go through the technology Continue reading “Build a Data Pipeline with AWS Athena and Airflow (part 1)”