I have data in BigQuery from my Google Analytics account, along with some extra tables where I have transformed some of this data.
I would like to export some of my transformed data from BigQuery and import it into Google Analytics as a custom dimension.
I have done this manually, by downloading a CSV from my table in BigQuery and importing this using the GA admin UI. I would like to automate the process, but not sure where to start.
What would be the most efficient tool to automate this process? The process being:
Run a SQL query on by BQ data every day and overwrite a table.
Export this table as a file and upload it to a GA account as a query time import.
Not sure why you'd want do this, but one (rather clunky) solution that pops into my head is to spin up a small GCE instance, and using the gcloud tool and some simple bash:
Run a BigQuery query job (SQL) to truncate your table
Monitor the progress of that query job i.e wait
When it's finished, trigger an export job and dump the table to GCS
Monitor the progress of that BigQuery export job i.e. wait
When it's finished, download the file from GCS
Upload the file to GA using the management API (https://developers.google.com/analytics/devguides/config/mgmt/v3/mgmtReference/management/uploads/uploadData)
Schedule a cron job to run the above bash script daily
A nicer way would be to use Cloud Functions listening on the GCS bucket, but in my opinion, CFs are not designed for performing long running batch/data workloads. They have e.g. time limits (540s). Also, if GA supported direct load from GCS it would be much better. But, I wasn't able to find support for that.
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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 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.
I am looking for a way to quickly upload offline data into Google Analytics. This is possible using Data Import which is a feature provided by Google Analytics itself. But doing this on daily basis is a hectic task. Is there any other functionality available using which i can automatically upload data on daily basis and view the report?
You can automate data imports by using the Management API. Data Import is documented here.
To follow the examples you first need to install the Google API client for the programming language of your choice. Then you create the custom data source (same as for the manual upload) and send data there via the uploadData method. Run this at a schedule (e.g. via cron) and the task stops being hectic.
So I have a 150 GB database that I want to import to Google Cloud SQL (it doesn't matter if its PSQL or MySQL.) The reason I want to do this is to have more flexible space, and faster computation.
However, I find no easy intro on how this is done. It seems like the way to do this is to also make a Google Cloud Storage instance, then dump my SQLite database to an SQL file, upload it to the Cloud Storage bucket, then import it to Google Cloud SQL. Is this the best and quickest way?
Dumping a 150 GB database would probably require lots of space and lots and lots of time.
Ultimately, what you need to do is to:
make sql dump of your sqlite database
convert it to be mysql- or postgresql- compatible (either manually or using some tool)
upload it to Google
import into your CloudSQL instance
You can try to minimize intermediate steps by using something like https://github.com/dimitri/pgloader. It seems like you can use this tool to connect your sqlite database directly to CloudSQL instance. It will take time - there's no getting around trasferring ~150G worth of data to Google
Where is your sqlite database stored now? If it's already in GCE VM, running pgloader from the same region as your CloudSQL instance should make it much faster.
I have setup Analytics Export to BigQuery. Everytime when a new ga_sessions_yyyymmdd gets created I would like to run some queries aggregating some data for future use.
I can't figure out how to do this. Do I have to create a job and trigger it from outside or is there a way to trigger this in BigQuery directly (prefably using the Web UI).
You cannot schedule queries to run via the Web UI. You'll need to write a small piece of software to do this by using use the BigQuery API, and cron(s).
You may also want to check out Cloud Functions - bearing in mind that it's still in Alpha.