- Recipes
- Google Storage NDJSON file to Google BigQuery
Connect Google Storage NDJSON file and Google BigQuery in our serverless environment
Use this template to Read NDJSON file entries from Google Storage bucket using them to insert rows into Google BigQuery table.
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Read NDJSON file entries from Google Storage bucket
Used integrations:
JavaScript
Python
class GoogleStorageSourceReadRemoteNdjson {
async init() {
// TODO: Create your google-storage credential
// More info at https://yepcode.io/docs/integrations/google-storage/#credential-configuration
const googleStorage = yepcode.integration.googleStorage(
"your-google-storage-credential-name"
);
// TODO: Customize your bucket name
this.bucket = googleStorage.bucket("your-bucket-name");
}
async fetch(publish, done) {
// TODO: Customize your ndjson file path to download
await this.bucket
.file("one-folder/my-filename-1653415231696.ndjson")
.createReadStream()
.pipe(ndjson.parse())
.on("data", async (line) => {
await publish(line);
})
.on("end", done);
}
async close() {}
}
import ndjson
import io
class GoogleStorageSourceReadRemoteNdjson:
def setup(self):
# TODO: Create your Google Storage credential:
# More info at https://yepcode.io/docs/integrations/google-storage/#credential-configuration
self.storage_client = yepcode.integration.googleStorage(
"your-storage-credential-name"
)
def generator(self):
# TODO: Customize your bucket name and object key
bucket = self.storage_client.get_bucket("bucket_name")
blob = bucket.blob("object_key")
bytes_stream = io.BytesIO()
blob.download_to_file(bytes_stream)
bytes_stream.seek(0)
ndjson_file_stream = io.TextIOWrapper(bytes_stream, encoding="utf-8")
reader = ndjson.reader(ndjson_file_stream)
for row in reader:
yield row
def close(self):
pass
Do you need help solving this integration with YepCode?
Let's talkInsert rows into Google BigQuery table
Used integrations:
- JavaScript
- Python
class GoogleBigqueryTargetInsert {
async init() {
// TODO: Create your google-bigquery credential
// More info at https://yepcode.io/docs/integrations/google-bigquery/#credential-configuration
const googleBigQuery = yepcode.integration.googleBigQuery(
"your-google-bigquery-credential-name"
);
// TODO: Customize dataset and table names
// See how to get dataset name: https://cloud.google.com/bigquery/docs/dataset-metadata#console
this.table = await googleBigQuery
.dataset("your-dataset")
.table("your-table");
}
async consume(item) {
// TODO: Map item to your bigquery table row
await this.table.insert([item]);
}
async close() {}
}
class GoogleBigqueryTargetInsert:
def setup(self):
# TODO: Create your BigQuery credential:
# More info at https://yepcode.io/docs/integrations/google-bigquery/#credential-configuration
self.big_query_client = yepcode.integration.googleBigQuery(
"your-bigquery-credential-name"
)
def consume(self, generator, done):
# TODO: Customize dataset and table ids
dataset_id = "dataset_id"
table_id = "table_id"
table_ref = self.big_query_client.dataset(dataset_id).table(table_id)
table = self.big_query_client.get_table(table_ref)
# TODO: Customize the row to insert
for item in generator:
item_to_insert = (item.get("name"), item.get("age"))
self.big_query_client.insert_rows(table, [item_to_insert])
done()
def close(self):
pass
NdjsonFAQs
YepCode is a SaaS platform that enables the creation, execution and monitoring of integrations and automations using source code in a serverless environment.
We like to call it the Zapier for developers, since we bring all the agility and benefits of NoCode tools (avoid server provisioning, environment configuration, deployments,...), but with all the power of being able to use a programming language like JavaScript or Python.
These recipes are an excellent starting point for creating your own YepCode processes and solving complex integration and automation problems.
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YepCode has been created with a clear enterprise focus, offering a multi-tenant environment, team management capabilities, high security and auditing standards, Identity Provider (IdP) integrations, and on-premise options. It serves as the Swiss army knife for engineering teams, especially those requiring the extraction or transmission of information to external systems. It excels in scenarios demanding flexibility and adaptability to change within the process.
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