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Begin by familiarizing yourself with Looker's native data export options. Looker allows you to export data from a Look or an Explore in various formats such as CSV, Excel, JSON, and more. This step involves selecting the format that best suits your needs, typically CSV for simplicity and compatibility with Databricks.
Navigate to the desired Look or Explore in Looker. Use the "Download" option to export the data. Choose CSV as the format to ensure ease of import into Databricks. Save the exported file to a location accessible by your system, such as a local machine or cloud storage.
Set up your Databricks workspace if it is not already configured. This involves creating a Databricks account and a new cluster. Ensure the cluster is running and accessible for data operations. Familiarize yourself with the Databricks interface and its capabilities for managing files and executing Spark jobs.
Utilize the Databricks UI to upload the CSV file to the Databricks File System. Navigate to the "Data" tab, select "Add Data," and choose "Upload File." Follow the prompts to upload the CSV file from your local machine to DBFS, where it can be accessed by your Databricks notebooks and jobs.
Create a new notebook within your Databricks workspace. This notebook will contain the Spark code necessary to read the CSV file from DBFS and ingest it into the Databricks Lakehouse. Use PySpark or SQL within the notebook depending on your preference and the complexity of transformations required.
In your Databricks notebook, write Spark code to read the CSV file from DBFS. Utilize Spark's built-in functions to parse the CSV data and perform any necessary transformations or cleaning. This step ensures that the data is ready to be stored in the Lakehouse in an optimized format such as Delta Lake.
Use the Spark DataFrame API or SQL to write the processed data into the Databricks Lakehouse. Choose an appropriate storage format such as Delta Lake for better performance and management. Execute the code within your notebook to complete the data transfer process, ensuring the data from Looker is now stored within the Databricks Lakehouse for further analytics and exploration.
By following these steps, you can efficiently transfer data from Looker to Databricks Lakehouse without relying on third-party tools, maintaining control over the entire process.
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:
