Today we are announcing a new dataset that has been added to the RUM Archive: Akamai Employee Individual Websites A few Akamai employees have opted-in to including their personal websites in the RUM Archive: How does the RUM data from these…
The RUM Archive was released in December 2022, and we've been releasing new data on a monthly cadence for over two years now. This month we're announcing an improvement to the data release schedule: RUM Archive data is now being uploaded on…
The Web Platform Baseline project aims to track when exactly Web features became available in all major browsers (Chrome, Edge, Firefox, Safari). This is important since not all browsers implement all features at the same time; using cuttin…
We're excited to announce a preview of a new type of data available in the RUM Archive: Third-Party Resources At mPulse, we are using boomerang.js to capture RUM data. Boomerang uses the ResourceTiming API to gather data about all of the su…
We've completed an export of mPulse daily RUM data into the BigQuery akamai-mpulse-rumarchive project up through 2023-09-30. This brings the RUM Archive's mPulse dataset to a running total of 394 straight days of data available since 2022-0…
The Akamai mPulse team studies Real User Monitoring (RUM) data to help our customers understand how to optimize website performance and how user behavior may be altered by changes to configurations or resource requests on their site. As a d…
The RUM Archive datasets contain aggregated Real User Monitoring (RUM) data from one or more websites. Each dataset in the RUM Archive publishes aggregated RUM data to public Google BigQuery tables. There are two types of tables within the
Most of the data in the RUM Archive is sampled. The sampling rate may not be publicly disclosed, so BEACONS counts should only be used for relative weighting for that day. The sampling rate may also change from day-to-day. Some datasets may…
The RUM Archive specifies two types of tables: All tables are partitioned by the DATE column to assist with reducing the amount of data queried. We suggest you limit all queries to a specific date (or date range) to limit your BigQuery quer…
Here are some sample queries to get you started. All of these queries can be run against the Akamai mPulse RUM dataset. NOTE: It is recommended you always use a DATE filter to minimize query size and cost. To understand the unique values av…
This page captures a collection of basic visualizations for the data contained in the RUM Archive. It's goal is to allow users to get a feel for the most important insights that can be gained from the data, without having to execute SQL que…
RUM Archive datasets are stored in Google BigQuery. To execute queries against RUM Archive datasets, you will need a Google account. Note: BigQuery has costs for storage and querying. You'll want to review what's available in the free tier,…