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Begin by exporting the required data from Looker. Use the Looker interface to create the desired report or visualization, ensuring it contains all necessary fields and data points. Once your data is prepared, export it in a CSV or JSON format, which are both compatible with AWS services.
Log into your AWS Management Console and navigate to the S3 service. Create a new S3 bucket, providing a unique name and selecting the appropriate region. Configure the bucket settings, ensuring it allows for object uploads and has the necessary permissions for your use case.
With your data exported from Looker, upload the CSV or JSON files to your S3 bucket. Use the AWS Management Console for a manual upload, or automate the process using the AWS CLI or SDKs, making sure the files are stored in the correct directory structure within the bucket.
Access AWS Glue from the AWS Management Console and create a new Crawler. Configure the Crawler to point to your S3 bucket, specifying the location of your data files. Set the Crawler to detect the data schema automatically to prepare it for further processing.
Execute the Glue Crawler to scan the S3 bucket and extract the metadata. This step will create a table in the AWS Glue Data Catalog that represents the structure of your data. Review the Data Catalog to ensure the schema has been correctly inferred and adjust if necessary.
In AWS Glue, create a new ETL (Extract, Transform, Load) job. Set the source to the table created by the Crawler in the Data Catalog. Define any transformations needed on the data, such as cleaning or reformatting fields, using the Glue Studio interface or by writing custom scripts in Python or Scala.
Execute the Glue ETL job to process the data and store the output in your desired destination, whether it be another S3 bucket, an AWS RDS database, or other storage solutions. Once the job completes, verify the output to ensure data integrity and correctness, making adjustments to the ETL process if needed.
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: