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1. Create a Look or Explore: Identify the data you want to export from Looker. You can create a new Look or use an existing one that contains the data you need.
2. Run the Query: Execute the query in Looker to ensure it returns the desired data. Make any necessary adjustments to the query to get the correct dataset.
3. Export the Data: Once you are satisfied with the dataset, export the data from Looker. You can usually do this by clicking on the "Gear" icon or "Download" button and selecting the option to export the results, typically in a CSV or TXT format. Choose a format that is compatible with BigQuery data import requirements.
1. Clean the Data: If necessary, clean the data using a text editor or a script to ensure it meets BigQuery's data formatting requirements, such as proper delimiters, escaping, and encoding.
2. Data Schema: Define the schema that matches the data you've exported from Looker. You'll need this schema to create a table in BigQuery or to ensure the data aligns with an existing table's schema.
1. Create a GCS Bucket: If you don't already have one, create a new Google Cloud Storage bucket in your Google Cloud project where you can upload the exported data file.
2. Upload the File: Upload the data file to the newly created GCS bucket. You can use the Google Cloud Console, `gsutil`, or the Google Cloud Storage API to upload the file.
1. Create a BigQuery Dataset: If you haven't already, create a new dataset in BigQuery where your new table will reside.
2. Create a BigQuery Table: Create a table in BigQuery with the appropriate schema that matches the data you're importing. You can do this in the BigQuery UI or using the `bq` command-line tool.
3. Load Data into the Table: Import the data from the GCS bucket into your BigQuery table. You can do this through the BigQuery UI by selecting your dataset, clicking on "Create Table," and specifying the source as your GCS file. Alternatively, you can use the `bq` command-line tool to load the data:
bq load --source_format=CSV --autodetect \
mydataset.mytable gs://mybucket/mydata.csv
Replace `mydataset`, `mytable`, `mybucket`, and `mydata.csv` with your dataset name, table name, GCS bucket name, and filename, respectively. The `--autodetect` flag is optional and instructs BigQuery to automatically detect the schema.
1. Check the Data: After loading the data into BigQuery, run some queries to ensure that the data has been imported correctly and completely.
2. Data Validation: Compare the results of a few queries in BigQuery with the data you see in Looker to ensure consistency.
1. Remove Temporary Files: After verifying the data integrity, you can delete the exported data file from the GCS bucket to avoid incurring storage costs.
2. Documentation: Document the process, including the schema and any transformations applied to the data, for future reference or for other team members.
By following these steps, you should be able to move data from Looker to BigQuery without using third-party connectors or integrations. Remember to handle sensitive data with care and ensure that you comply with all relevant data protection regulations.
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.
Looker is a Google-Cloud-based enterprise platform that provides information and insights to help move businesses forward. Looker reveals data in clear and understandable formats that enable companies to build data applications and create data experiences tailored specifically to their own organization. Looker’s capabilities for data applications, business intelligence, and embedded analytics make it helpful for anyone requiring data to perform their job—from data analysts and data scientists to business executives and partners.
Looker's API provides access to a wide range of data categories, including:
1. User and account data: This includes information about users and their accounts, such as user IDs, email addresses, and account settings.
2. Query and report data: Looker's API allows users to retrieve data from queries and reports, including metadata about the queries and reports themselves.
3. Dashboard and visualization data: Users can access data about dashboards and visualizations, including the layout and configuration of these elements.
4. Data model and schema data: Looker's API provides access to information about the data model and schema, including tables, fields, and relationships between them.
5. Data access and permissions data: Users can retrieve information about data access and permissions, including which users have access to which data and what level of access they have.
6. Integration and extension data: Looker's API allows users to integrate and extend Looker with other tools and platforms, such as custom applications and third-party services.
Overall, Looker's API provides a comprehensive set of data categories that enable users to access and manipulate data in a variety of ways.
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:
