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Begin by identifying the specific data you need to move from Looker to Oracle. Use Looker's interface to create the necessary queries and reports that capture this data. Once you have the desired data, use Looker's data export functionality to export the data to a CSV format. Ensure the exported data is clean and devoid of any unnecessary fields to simplify the import process.
Before exporting, configure the export settings in Looker to ensure compatibility with Oracle. Set the delimiter to a comma and ensure all text fields are correctly quoted. Verify that date and numeric formats match the expected formats in Oracle. This step is crucial to avoid issues during data import into Oracle.
Execute the export process in Looker, saving the output file (usually a CSV) to a secure location on your local machine or a server that Oracle can access. Confirm that the export process completes successfully and that the file contains all the expected data.
In Oracle, create the necessary tables and data structures that will hold the data from Looker. Define the correct data types and constraints to match those of the exported data. If the schema is not already in place, use SQL commands to create tables, ensuring they can accommodate the CSV data structure.
Move the exported CSV file to the Oracle server. This can be done using secure file transfer methods such as SCP (Secure Copy Protocol) or any other method that securely transfers files from your local machine to the Oracle server. Ensure the file is placed in a directory accessible by the Oracle database.
Utilize Oracle's SQLLoader utility to import the CSV data into the Oracle database. Create a control file that specifies how the CSV data should be mapped to the Oracle tables. The control file should include details such as column mappings and data formats. Run SQLLoader with the control file to start the import process, and verify that the data loads correctly without errors.
After the import is complete, perform data validation checks to ensure that the data in Oracle matches the original data in Looker. Use SQL queries to verify counts, sums, and other metrics as necessary. Resolve any discrepancies by reviewing both the exported data and the Oracle import process. This validation step ensures data integrity and completeness in the Oracle database.
By following these steps, you can successfully move data from Looker to Oracle 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:
