New ETL Pipeline, Transition to New BigQuery Tables
Since May 2017, the M-Lab team has been working on an updated, open source pipeline, which pulls raw data from our servers, saves it to Google Cloud Storage, and then parses it into our BigQuery tables. The team is particularly excited about this update because it means that the pipeline no longer relies on closed source libraries.
Transitioning to a New Backend Pipeline and Data Availability
M-Lab data is collected from distributed experiments hosted on servers all over the world, processed in a pipeline, and published for free in both raw and parsed (structured) formats. The back end processing component for this has served us well for many years, but it’s been showing its age recently. As M-Lab collects an increasing amount of data thanks to new partnerships, we have been concerned that it will not be as reliable.
Making it Easier to Use M-Lab Data
In January, M-Lab launched a beta test of new BigQuery tables for M-Lab data. Today, M-Lab is pleased to announce that the beta test was successful. The new, faster-performing tables will be M-Lab’s new standard BigQuery tables.
Before we move on to specifics, when we say faster performing, we mean a lot faster. As in, certain queries that used to take over 2 hours now complete in 8 seconds. That means that playing with the data just became a lot more fun.
To help users dig in to this data as quickly and seamlessly as possible, M-Lab has consolidated all of its data documentation and updated it to show how to take advantage of the new tables.
Announcing improved performance for M-Lab BigQuery data
Today, M-Lab is happy to announce the public beta of new M-Lab BigQuery tables. These tables provide substantially improved performance and reduce the difficulty of writing BigQuery SQL.