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- Create the Report or Look: Define the data you want to export from Looker by creating a report (Look) or using an existing one.
- Run the Query: Execute the query to ensure it returns the expected data.
- Export Data: Once the data is ready, export it from Looker in a suitable format, typically CSV or Excel, which can be later imported into Snowflake.
- Log in to Snowflake: Access your Snowflake account.
- Create a Database and Schema: If not already present, create a new database and schema where you want to store the Looker data.
CREATE DATABASE my_database;
USE DATABASE my_database;
CREATE SCHEMA my_schema; - Create a Table: Define a table in Snowflake with the appropriate schema to hold the data you’re exporting from Looker.
CREATE TABLE my_schema.my_table (
column1 TYPE,
column2 TYPE,
...
);
Replace TYPE with the corresponding data types for your columns.
- Format the Data: Ensure the exported data file is in a format compatible with Snowflake’s data loading methods (CSV is commonly used).
- Clean the Data: Make sure the data is clean, with no formatting issues that could cause errors during the import process.
- Choose a Staging Area: Decide whether to use Snowflake’s internal staging area or an external cloud storage service (like Amazon S3, Google Cloud Storage, or Azure Blob Storage) to temporarily store the data file.
- Upload the File: Use the PUT command for Snowflake’s internal staging or the appropriate method for your chosen cloud storage service to upload the data file.
- For Snowflake’s internal staging:
PUT file://path_to_your_file.csv @~;
- Copy Command: Use the COPY INTO command to load the data from the staging file into the target table in Snowflake.
COPY INTO my_schema.my_table
FROM @my_stage/path_to_your_file.csv
FILE_FORMAT = (TYPE = 'CSV' FIELD_OPTIONALLY_ENCLOSED_BY = '"' SKIP_HEADER = 1); - Validate Data Load: Check the loaded data to ensure it matches the exported data from Looker.
SELECT * FROM my_schema.my_table;
- Remove Temporary Files: If you used Snowflake’s internal staging, remove the temporary files.
REMOVE @~ 'path_to_your_file.csv'; - Review and Optimize: Evaluate the data transfer process for any optimizations for future transfers, such as automating the process with Snowflake’s tasks or stored procedures if this is a recurring need.
- Manage Access: Grant appropriate permissions to users or roles that need access to the new data in Snowflake.
GRANT SELECT ON my_schema.my_table TO ROLE my_role; - Audit: Ensure that the data transfer complies with your organization’s data governance and security policies.
Important Notes:
- The data types in the Snowflake table should match the data types of the Looker exported data to prevent data conversion errors.
- Ensure that you have the necessary permissions in both Looker and Snowflake to perform these operations.
- Always validate the data after each step to ensure the integrity of the transfer.
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:
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.
A cloud data platform, Snowflake Data Cloud provides a warehouse-as-a-service built specifically for the cloud. The Snowflake platform is designed to empower many types of data workloads, and offers secure, immediate, governed access to a comprehensive network of data. Snowflake’s innovative technology goes above the capabilities of the ordinary database, supplying users all the functionality of database storage, query processing, and cloud services in one package.
1. Open Looker and navigate to the Admin panel.
2. Click on "Connections" and then "New Connection".
3. Select "Airbyte" as the type of connection.
4. Enter a name for the connection and the URL for the Airbyte instance.
5. In the "Authentication" section, select "OAuth2" as the authentication method.
6. Enter the Client ID and Client Secret provided by Airbyte.
7. In the "Advanced" section, set the "API Version" to "v1".
8. Click "Test" to ensure the connection is successful.
9. Save the connection and navigate to the "Explore" panel.
10. Select the Airbyte connection as the data source and choose the relevant tables to explore.
Note: It is important to ensure that the Airbyte instance is properly configured and the necessary connectors are installed before attempting to connect to Looker. Additionally, the specific steps for adding credentials may vary depending on the version of Looker being used.
1. First, navigate to the Airbyte website and log in to your account.
2. Once you are logged in, click on the "Destinations" tab on the left-hand side of the screen.
3. Scroll down until you find the Snowflake Data Cloud destination connector and click on it.
4. You will be prompted to enter your Snowflake account information, including your account name, username, and password.
5. After entering your account information, click on the "Test" button to ensure that the connection is successful.
6. If the test is successful, click on the "Save" button to save your Snowflake Data Cloud destination connector settings.
7. You can now use the Snowflake Data Cloud destination connector to transfer data from your Airbyte sources to your Snowflake account.
8. To set up a data transfer, navigate to the "Sources" tab on the left-hand side of the screen and select the source you want to transfer data from.
9. Click on the "Create New Connection" button and select the Snowflake Data Cloud destination connector as your destination.
10. Follow the prompts to set up your data transfer, including selecting the tables or data sources you want to transfer and setting up any necessary transformations or mappings.
11. Once you have set up your data transfer, click on the "Run" button to start the transfer process.
With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.
We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Ready to get started?
Frequently Asked Questions
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.