I want to load firebase performance(performance monitoring) data into big query so that I can create custom visualization on Google data studio.
is it possible to do this with performance monitoring? I am not able to find this in docs anywhere?
There is currently no export of Firebase Performance Monitoring data.
It's happening now. You can export your firebase performance data in BigQuery for custom queries or reports
https://firebase.google.com/docs/perf-mon/bigquery-export
Related
We use Firebase with a firestore database.
I would like to do some data analysis to identify business logic that performs unnecessary read / write operations. Is it possible to export detailed data on read/write operations, or am I limited to what Google give us via Firestore Usage.
Ideally, I would like to export detailed usage data for analysis in R / Python.
Is this possible?
Google Cloud Platform does not provide a straight-forward way to analyze read/write operations by documents/collections.
In the end, I solved this problem by exporting the Firebase audit logs to BigQuery, and cleaning the data manually.
https://cloud.google.com/logging/docs/audit
Need some help with accessing historical data for Firebase Crashlytics and Events data in BigQuery.
We have linked BigQuery to firebase and we are able to get only last 2 months of data in BigQuery at this moment.
Can you please suggest a way to get the data since the inception of the app?
Firebase doesn't keep the events data indefinitely which makes this feature not feasible at the moment.
Currently, your data will start being exported since the moment you enable the BigQuery connection, i.e. you can't access your historical data.
If you think this feature would be useful for you and for other people, I encourage you to request it in this link.
I hope it helps
I am researching of a way to regularly sync Firebase data to BigQuery, then display that data to Data Studio. I saw this instruction in the documentation:
https://support.google.com/firebase/answer/6318765?hl=en
According to the above instruction, it says once Firebase is linked to BigQuery, the data from Firebase is being streamed to BigQuery real-time.
Let's say I have initial export of Firebase data to BigQuery (before linking) and I made a Data Studio visualization out of that initial data, we call it Dataset A. Then I started linking Firebase to BigQuery. I want Dataset A to be in sync with Firebase every 3 hours.
Based on the documentation, does this mean I don't have to use some external program to synchronize Firebase data every 3 hours to BigQuery, since it is streaming real-time already? After linking, does the streamed data from Firebase automatically goes to Dataset A?
I am asking because I don't want to break the visualization if the streaming behaves differently than the expected (expected means that Firebase streams to BigQuery's Dataset A consistent with the original schema). Because if it does (break the original dataset or it doesn't stream to the original dataset), I might as well write a program that does the syncing.
Once you link your Firebase project to BigQuery, Firebase will continuously export the data to BigQuery, until you unlink the project. As the documentation says, the data is exported to daily tables, and a single fixed intraday table. There is no way for you to control the schedule of the data export beyond enabling/disabling it.
If you're talking about Analytics data, schema changes to the exported data are very rare. So far there's been a schema change once, and there are currently no plans to make any more schema changes. If a schema change ever were to happen again though, all collaborators on the project will be emailed well in advance of the change.
I am planning on creating a ML model using Google Datalab.
I plan to keep the source data (json, structured) on datastore.
Still, I am not finding lot of examples onhow to query datastore form datalab.
Is that something that can be done? Is that a good practice?
Should I better write a process to send the trainning data to a CSV on Google Cloud Storage?
Thanks!
#Kolban answered it on the comments.
This is a duplicate of Google Datastore API from Datalab
Also, there are not many examples because it is not used as much as the other products
Thanks!
I am using Firebase as my authentication and database platform in my React Native-Expo app. I have not yet decided if I will be using the realtime-database or Firestore database.
I need to perform statistical analysis on daily data gathered from my users, which is stored in the database. I.e. the users type in their daily intake of protein, from it I would like to calculate their weekly average, expected monthly average, provide suggestions of types of food if protein intake is too low and etc.
What would be the best approach in order to achieve the result wanted in my specific situation?
I am really unfamiliar and stepping into uncharted territory regarding on how I can accomplish this. I have read that Firebase Analytics generates different basic analytics regarding usage of the app, number crash-free users etc. But can it perform analytics on custom events? Can I create a custom event for Firebase analytics to keep track of a certain node in my database, and output analytics from that? And then of course, if yes, does it work with React Native-Expo or do I need to detach from Expo? In addition, I have read that Firebase Analytics can be combined with Google BigQuery. Would this be an alternative for my case?
Are there any other ways of performing such data analysis on my data stored in Firebase database? For example, export the data and use Python and SciKit Learn?
Whatever opinion or advice you may have, I would be grateful if you could share it!
You're not alone - many people building web apps on GCP have this question, and there is no single answer.
I'm not too familiar with Firebase Analytics, but can answer the question for Firestore and for your custom analytics (e.g. weekly avg protein consumption)
The first thing to point out is that Firestore, unlike other NoSQL databases, is storage only. You can't perform aggregations in real time like you can with MongoDB, so the calculations have to be done somewhere else.
The best practice recommended by GCP in this case is indeed to do a regular export of your Firestore data into BQ (BigQuery), and you can run analytical calculations there in the meantime. You could also, when a user inputs some data, send that to Pub/Sub and use one of GCP Dataflow's streaming templates to stream the data into BQ, and have everything in near real time.
Here's the issue with that however: while this solution gives you real time, and is very scalable, it gets expensive fast, and if you're more used to Python than SQL to run analytics it can be a steep learning curve. Here's an alternative I've used for smaller webapps, which scales well for <100k users and costs <$20 a month on GCP's current pricing:
Write a Python script that grabs the data from Firestore (using the Firestore Python SDK), generates the analytics you need on it, and writes the results back to a Firestore collection
Create an endpoint for that function using Flask or Django
Deploy that server application on Cloud Run, preventing unauthenticated invocations (you'll only be calling it from within GCP) - see this article, steps 1 and 2 only. You can also deploy the Python script(s) to GCP's Vertex AI or hosted Jupyter notebooks if you're more comfortable with that
Use Cloud Scheduler to call that function every x minutes - see these docs for authentication
Have your React app query the "analytics results" collection to get the results
My solution is a FlutterWeb based Dashboard that displays relevant data in (near) realtime like the Regular Flutter IOS/Android app and likewise some aggregated data.
The aggregated data is compiled using a few nodejs based triggers in the database that does any analytic lifting and hence is also near realtime. If you study pricing you will learn, that function invocations are pretty cheap unless of-course you happen to make a 'desphew' :)
I came up with a great solution.
I used the inbuilt firebase BigQuery plugin. Then I used Cube.js (deployed on GCP - cloud run on docker) on top of bigquery.
Cube.js just makes everything just so easy. You do need to make a manual query It tries to do optimize queries. On top of that, it uses caching so you won't get big bills on GCP. I think this is the best solution I was able to find. And this is infinitely scalable and totally real-time.
Also if you are a small startup then it is mostly free with GCP - free limits on cloud run and BigQuery.
Note:- This is not affiliated in any way with cubejs.