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Begin by exploring the data formats available in Whisky Hunter. Typically, data might be presented in JSON, CSV, or an online HTML format. Identify, download, and save the data in a manageable format like CSV or JSON for ease of processing.
Set up the Oracle database environment where the data will be imported. Ensure you have the necessary permissions to create tables and insert data. Define the schema and create tables in Oracle DB to match the structure of the data from Whisky Hunter, considering data types and constraints.
If the data is in JSON or another non-CSV format, convert it to CSV using a script or tool available in your programming environment (Python, Java, etc.). Ensure the data fields match the schema you've set up in the Oracle database. This might involve cleaning or transforming data to fit SQL standards.
Develop a script in a language like Python or PL/SQL to read the prepared CSV file and generate SQL INSERT statements. This script will read data line-by-line from the CSV and output SQL commands that can be executed against the Oracle database. Consider handling exceptions for data types and malformed data.
Use Oracle’s built-in tools or libraries to establish a direct connection to the Oracle database from your script. For example, if using Python, leverage the cx_Oracle library to create a connection string with the necessary credentials (username, password, host, and port).
With the direct database connection established, execute the generated SQL INSERT statements from your script. Batch these operations if possible to improve performance and ensure data integrity during the import process. Monitor the process for errors, and log any issues for review.
After executing the SQL commands, verify that the data in the Oracle database matches the original data from Whisky Hunter. Perform data quality checks, such as counting records and checking for data truncation or format mismatches. Use Oracle’s SQL tools to query the database and validate the import process.
By following these steps, you can manually move data from Whisky Hunter to an Oracle Database 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.
Whisky Hunter is one kinds of market research tool which is largely used for collectors, investors & whisky lovers. There are many market intelligence remaining the access to the WhiskyHunter.net that have a database of previous and live lot prices from online whisky auctions.
Whisky Hunter's API provides access to a wide range of data related to the whisky industry. The following are the categories of data that can be accessed through the API:
1. Whisky information: This includes details about the whisky such as its name, brand, age, type, and region.
2. Distillery information: This includes information about the distillery where the whisky is produced, such as its name, location, and history.
3. Tasting notes: This includes information about the flavor profile of the whisky, such as its aroma, taste, and finish.
4. Ratings and reviews: This includes ratings and reviews of the whisky by other users, which can help users make informed decisions about which whiskies to try.
5. Price information: This includes information about the price of the whisky, both in retail stores and online.
6. Availability: This includes information about where the whisky is available for purchase, both online and in physical stores.
7. Whisky news and events: This includes news and updates about the whisky industry, as well as information about upcoming whisky events and festivals.
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?
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