I am trying to connect third party ranking management system (https://tranco-list.eu/) with metabase. Tranco is giving us an option to see the record on Google BigQuery but when I am trying to connect the Tranco with Metabase then it is asking for dataset from my Google cloud console project. Since Tranco is an external database source and I don't have access to the dataset Id from this.
If you want to get the result of tranco in Google BigQuery then run below query.
select * from `tranco.daily.daily` where domain ='google.com' limit 10
When I am searching Tranco in public dataset then also I am not finding this over their also. Is anyone aware of, how to add the third party dataset to our Google cloud project.
Thanks in advance.
Unfortunately, you can’t read the Tranco dataset directly from BigQuery; but, what you can do is to load the CSV data from Tranco into a Cloud Storage Bucket and then read your bucket in BigQuery.
When you load data from Cloud Storage into a BigQuery table, the dataset that contains the table must be in the same regional or multi- regional location as the Cloud Storage bucket.
Note that it has the next limitations:
CSV files do not support nested or repeated data.
Remove byte order mark (BOM) characters. They might cause unexpected
issues.
If you use gzip compression, BigQuery cannot read the data in
parallel. Loading compressed CSV data into BigQuery is slower than
loading uncompressed data.
You cannot include both compressed and uncompressed files in the same
load job.
The maximum size for a gzip file is 4 GB. When you load CSV or JSON
data, values in DATE columns must use the dash (-) separator and the
date must be in the following format: YYYY-MM-DD (year-month-day).
When you load JSON or CSV data, values in TIMESTAMP columns must use
a dash (-) separator for the date portion of the timestamp, and the
date must be in the following format: YYYY-MM-DD (year-month-day).
The hh:mm:ss (hour-minute-second) portion of the timestamp must use a
colon (:) separator.
Also, you can see this documentation if you don’t know how you can upload and read your CSV data.
And also in the next link I'm sending you is a step by step guide in how yo can create / select the bucket you will use.
Related
I have CSV files added to a GCS bucket daily or weekly each file name contains (date + specific parameter)
The files contain the schema (id + name) columns and we need to auto load/ingest these files into a bigquery table so that the final table have 4 columns (id,name,date,specific parameter)
We have tried dataflow templates but we couldn't get the date and specific parameter from the file name to the dataflow
And we tried cloud function (we can get the date and specific parameter value from file name) but couldn't add it in columns while ingestion
Any suggestions?
Disclaimer: I have authored an article for this kind of problem using Cloud Workflows. When you want to extract parts of filename, to use as table definition later.
We will create a Cloud Workflow to load data from Google Storage into BigQuery. This linked article is a complete guide on how to work with workflows, connecting any Google Cloud APIs, working with subworkflows, arrays, extracting segments, and calling BigQuery load jobs.
Let’s assume we have all our source files in Google Storage. Files are organized in buckets, folders, and could be versioned.
Our workflow definition will have multiple steps.
(1) We will start by using the GCS API to list files in a bucket, by using a folder as a filter.
(2) For each file then, we will further use parts from the filename to use in BigQuery’s generated table name.
(3) The workflow’s last step will be to load the GCS file into the indicated BigQuery table.
We are going to use BigQuery query syntax to parse and extract the segments from the URL and return them as a single row result. This way we will have an intermediate lesson on how to query from BigQuery and process the results.
Full article with lots of Code Samples is here: Using Cloud Workflows to load Cloud Storage files into BigQuery
I have a csv file (delimiter | instead of ,) in the cloud storage bucket, which has data formats say 10022019 as DATE , however I need to transform it to the accepted DATE format of 2019-02-10 in bigquery, can I achieve this by a could function which reads and transform the data at the same time before it streams insert the data into a bigquery table?
Thanks.
Best regards,
Since you have your data in cloud storage you may consider a cloud function to adjust data quality before streaming/loading to BigQuery.
If you were to use load BigQury API you can consider serverless rule base data quality adjustment with StorageMirror, followed by rule base data loading with BqTail
Problem
I'm attempting to create a BigQuery table from a CSV file in Google Cloud Storage.
I'm explicitly defining the schema for the load job (below) and set header rows to skip = 1.
Data
$ cat date_formatting_test.csv
id,shipped,name
0,1/10/2019,ryan
1,2/1/2019,blah
2,10/1/2013,asdf
Schema
id:INTEGER,
shipped:DATE,
name:STRING
Error
BigQuery produces the following error:
Error while reading data, error message: Could not parse '1/10/2019' as date for field shipped (position 1) starting at location 17
Questions
I understand that this date isn't in ISO format (2019-01-10), which I'm assuming will work.
However, I'm trying to define a more flexible input configuration whereby BigQuery will correctly load any date that the average American would consider valid.
Is there a way to specify the expected date format(s)?
Is there a separate configuration / setting to allow me to successfully load the provided CSV in with the schema defined as-is?
According to the listed limitations:
When you load CSV or JSON data, values in DATE columns must use
the dash (-) separator and the date must be in the following
format: YYYY-MM-DD (year-month-day).
So this leaves us with 2 options:
Option 1: ETL
Place new CSV files in Google Cloud Storage
That in turn triggers a Google Cloud Function or Google Cloud Composer job to:
Edit the date column in all the CSV files
Save the edited files back to Google Cloud Storage
Load the modified CSV files into Google BigQuery
Option 2: ELT
Load the CSV file as-is to BigQuery (i.e. your schema should be modified to shipped:STRING)
Create a BigQuery view that transforms the shipped field from a string to a recognised date format. Use SELECT id, PARSE_DATE('%m/%d/%Y', shipped) AS shipped, name
Use that view for your analysis
I'm not sure, from your description, if this is a once-off job or recurring. If it's once-off, I'd go with Option 2 as it requires the least effort. Option 1 requires a bit more effort, and would only be worth it for recurring jobs.
For moving data from a BigQuery (BQ) table that resides in the US, I want to export the table to a Cloud Storage (GCS) bucket in the US, copy it to an EU bucket, and from there import it again.
The problem is that AVRO does not support DATE types, but it is crucial to us as we are using the new partitioning feature that is not relying on ingestion time, but a column in the table itself.
The AVRO files contain the DATE column as a STRING and therefore a
Field date has changed type from DATE to STRING error is thrown, when trying to load the files via bq load.
There has been a similar question, but it is about timestamps - in my case it absolutely needs to be a DATE as dates don't carry timezone information and timestamps are always interpreted in UTC by BQ.
It works when using NEWLINE_DELIMITED_JSON, but is it possible to make this work with AVRO files?
As #ElliottBrossard pointed out in the comments, there's a public feature request regarding this where it's possible to sign up for the whitelist.
My requirement is to pull the data from Different sources(Facebook,youtube, double click search etc) and load into BigQuery. When I try to pull the data, in some of the sources I was getting "NULL" when the column is empty.
I tried to load the same data to BigQuery and BigQuery is treating as a string instead of NULL(empty).
Right now replacing ""(empty string) where NULL is there before loading into BigQuery. Instead of doing this is there any way to load the file directly without any manipulations(replacing).
Thanks,
What is the file format of source file e.g. CSV, New Line Delimited JSON, Avro etc?
The reason is CSV treats an empty string as a null and the NULL is a string value. So, if you don't want to manipulate the data before loading you should save the files in NLD Json format.
As you mentioned that you are pulling data from Social Media platforms, I assume you are using their REST API and as a result it will be possible for you to save that data in NLD Json instead of CSV.
Answer to your question is there a way we can load this from web console?:
Yes, Go to your bigquery project console https://bigquery.cloud.google.com/ and create table in a dataset where you can specify the source file and table schema details.
From Comment section (for the convenience of other viewers):
Is there any option in bq commands for this?
Try this:
bq load --format=csv --skip_leading_rows=1 --null_marker="NULL" yourProject:yourDataset.yourTable ~/path/to/file/x.csv Col1:string,Col2:string,Col2:integer,Col3:string
You may consider running a command similar to: bq load --field_delimiter="\t" --null_marker="\N" --quote="" \
PROJECT:DATASET.tableName gs://bucket/data.csv.gz table_schema.json
More details can be gathered from the replies to the "Best Practice to migrate data from MySQL to BigQuery" question.