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Start by exporting the required data from Looker. In the Looker interface, navigate to the desired report or dashboard. Use the "Download" option to export the data in a suitable format like CSV or JSON. Ensure that the downloaded file is saved to a location accessible for further processing.
Set up the AWS Command Line Interface (CLI) on your local machine or server where the data file was exported. Download and install the AWS CLI from the official AWS website. Once installed, configure it with your AWS credentials by running `aws configure` and entering your AWS Access Key, Secret Key, default region, and output format.
Log in to your AWS Management Console and navigate to the S3 service. Create a new S3 bucket if you don’t already have one for this data. Choose a unique bucket name and select the appropriate region. Make sure to configure the bucket permissions to allow the necessary read/write access.
Ensure the data file exported from Looker is named appropriately and located in an accessible directory. Confirm that the file format (e.g., CSV or JSON) is consistent with your data handling and storage requirements. If needed, compress the file using tools like gzip to optimize upload time and storage.
Use the AWS CLI to upload the prepared data file to your S3 bucket. Open a terminal or command prompt and run the command:
```bash
aws s3 cp /path/to/your/file.csv s3://your-bucket-name/target-folder/file.csv
```
Replace `/path/to/your/file.csv` with the actual file path, `your-bucket-name` with your S3 bucket's name, and `target-folder` with the desired folder path inside the bucket.
After uploading, verify that the file has been successfully transferred to S3. You can do this by navigating to your S3 bucket in the AWS Management Console and checking the contents of the target folder to ensure the file appears there. Alternatively, use the AWS CLI command:
```bash
aws s3 ls s3://your-bucket-name/target-folder/
```
to list the contents of the folder.
If you need to move data from Looker to S3 on a regular basis, consider automating steps 1 through 6 using a script. Create a shell script or a Python script that includes these steps, and use a scheduler like cron (on Unix-based systems) or Task Scheduler (on Windows) to run the script at your desired intervals.
By following these steps, you can manually transfer data from Looker to Amazon S3 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.
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:
