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Before uploading data to BigQuery, ensure that your data is in a format that BigQuery can accept, such as CSV, JSON, Avro, Parquet, ORC, or Cloud Datastore export files. Clean the data to remove errors or inconsistencies and ensure it matches the table schema you plan to use in BigQuery.
If you haven't already, create a Google Cloud Platform (GCP) project. Go to the Google Cloud Console, click on the project dropdown, and select "New Project." Provide a project name and other required information, then click "Create."
Once your project is set up, you need to enable the BigQuery API. In the Google Cloud Console, navigate to APIs & Services > Library. Search for "BigQuery API" and click on it, then click "Enable" to activate the API for your project.
In the Google Cloud Console, go to BigQuery. Click on your project name in the Explorer panel, then click "Create dataset." Enter a name for your dataset and configure any additional settings such as data location and default table expiration. Click "Create dataset" to finalize the setup.
With your dataset ready, create the table where you will import your data. You can create a table manually by clicking "Create table" in the BigQuery UI. Specify the source format, and if needed, define the schema by listing field names, types, and modes. Alternatively, you can use a schema auto-detect feature if your data format supports it.
Before importing data into BigQuery, upload it to Google Cloud Storage (GCS). In the Google Cloud Console, navigate to Storage > Browser, and create a new bucket if necessary. Upload your data file to the bucket by clicking "Upload files" and selecting your data file.
Once your data is in GCS, you can load it into BigQuery. In BigQuery, click on your dataset, then the "Create table" option. Choose "Google Cloud Storage" as the source, and provide the GCS URI of your data file. Configure the remaining settings such as file format and schema settings. Click "Create table" to start the import. BigQuery will load the data from the specified GCS location into your table.
By following these steps, you can successfully move data to BigQuery without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
