How do I set the billing tier for queries executed through the BigQuery APIs in Google Cloud Datalab?
It is currently not possible to change the billing tier on a per-query basis in Google Datalab. I've submitted a pull request for this feature.
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Hi guys I am using GCP for the first time and while I walking through the a project's cloud function example with the mock data, I got confused about similarities/differences of each one and I would like more clarity of what makes them different because to me they seem so similar.
BigQuery is a data warehouse and a SQL Engine. You can use it to store tabular data in datasets and tables. In the tables you may as well store more complex structures like arrays and JSONs but not files for example.
Cloud Storage is a blob storage, with functionality similar to what you know in your linux/windows machine (saving files, folders, deleting, copying). Of course that in the backend it's nothing like your local file system.
BigQuery is a fully managed and serverless data warehouse. It's like Snowflake or Redshift.
Google Cloud Storage(GCS) is like Amazon S3 or Azure Storage. Storages are for storing data as the name suggests.
You usually use BigQuery to analyze & query data in order to draw some insights. BigQuery is an analytical engine.
You can store images, videos, logs, files, and etc in GCS(Google Cloud Storage), but BigQuery can't.
Google BigQuery belongs to "Big Data as a Service" category of the tech stack, while Google Cloud Storage can be primarily classified under "Cloud Storage".
Some of the features offered by Google BigQuery are:
• All behind the scenes- Your queries can execute asynchronously in the
background, and can be polled for status.
• Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.
• Affordable big data- The first Terabyte of data processed each month is free.
On the other hand, Google Cloud Storage provides the following key features:
• High Capacity and Scalability
• Strong Data Consistency
• Google Developers Console Projects
"High Performance" is the primary reason why developers consider Google BigQuery over the competitors, whereas "Scalable" was stated as the key factor in picking Google Cloud Storage.
I have an App Engine scheduled job which runs everyday and look for rows in a PostgreSQL table (hosted in gcp not a cloudsql) which meets a criteria to archive. If the criteria is met, it connects to BigQuery and streams the data to big query. Everyday there are few records qualify for archiving and we write to BigQuery. Is this the cost effective way or we can try loading data using Cloud Functions? https://cloud.google.com/solutions/performing-etl-from-relational-database-into-bigquery
App Engine and Cloud Functions have different purposes. You should use App Engine if you want to deploy a full application in a serverless environment. If you need to integrate services in the cloud, use Cloud Function. In your case it seems that Cloud Functions fits better.
It's important to remember that Cloud Function has a time limitation: the maximum time which your code has to run is 9 minutes.
You can find this and other limitations here
Furthermore, you can find here a pricing calculator for GCP products.
If you have any further questions, please let me know.
I'm a backend developer who has no experience with Google Analytics, but I've a requirement to find a way to collect the Marketing Medium/Source for each user from Google Analytics and save it in my database, I've been searching and looking how to get it from an API request but I didn't find a way yet, could you guys help?
You can use the Google Python API to fetch the Google Analytics data. You can read more here.
Medium and Source information can be found out by using the dimension ga:sourceMedium
You can find more info about dimensions and metrics here
Following which you can setup a daily script and fetches the data from your Google Analytics account and dumps data into csv which you can successively load into your database using libraries such as psycopg2.
I am new to BigQuery and I have a question regarding billing - I have a recurring (almost daily) charge on my account and I think it is related to a query I have embedded into a published Tableau report - people are viewing the report and I am being charged - however the charge is more that I am expecting. How can I track the charge back to the specific query to confirm which one is raising the charge?
Thank you for your help,
Ben
I would start by enabling audit logs and inspecting the logs.
Audit logs are available via Google Cloud Logging, where they can be immediately filtered to provide insights on specific jobs or queries, or exported to Google Cloud Pub/Sub, Google Cloud Storage, or BigQuery.
To analyze your aggregated BigQuery usage using SQL, set up export of audit logs back to BigQuery. For more information about setting up exports from Cloud Logging, see Overview of Logs Export in the Cloud Logging documentation.
Analyzing Audit Logs Using BigQuery: https://cloud.google.com/bigquery/audit-logs
Members, I have been trying to learn how to use google bigquery and the cloud sql though I have had a challenge with enabling billing issues, this is all because I needed is to have a free access package.
Question:
Is there a free package to enable me practice google cloud sql and bigquey, if yes please get me the link.
Besides any one experiencing the same problem?
This topic is not a programming question, so I will close it.
FYI:
BigQuery offers a free query tier for all users (the first 100 GB of data processed per month is at no charge). If you plan on using your own data, and not just test BigQuery with our sample public datasets, then you must enable billing, as there is no free storage tier. See: https://developers.google.com/bigquery/pricing
The D0 tier of Cloud SQL is less than a dollar a day, see:
https://developers.google.com/cloud-sql/docs/billing