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Before commencing the data transfer process, familiarize yourself with the data structure and format in Railz. Identify the data sources, types, and structure (e.g., JSON, CSV) which will help in mapping the data accurately to the Databricks Lakehouse.
Use Railz's API to export the necessary data. Railz provides RESTful API endpoints that you can use to extract data. Ensure to authenticate using the provided API keys and export the data in a structured format like JSON or CSV. You may need to write a script, for example in Python, to automate this extraction process.
Log into your Databricks account and navigate to your workspace. Set up a cluster if not already available, as this will be necessary for processing and storing the data. Make sure the cluster has the necessary permissions and configurations to handle the incoming data volume.
Once exported from Railz, clean and transform the data as needed. This involves converting the data into a format that is suitable for Databricks, ensuring data types are consistent, and removing any redundant or unnecessary information. Use data processing tools or write scripts to achieve this, ensuring the data is in its optimal form for analysis.
Transfer the prepared data to the Databricks File System. You can use Databricks CLI or the Databricks UI to upload files directly to DBFS. For automation, consider using a script to automate the upload process, ensuring correct paths and file permissions are set.
Use Databricks SQL or Spark to read data from DBFS and write it into the Lakehouse. Create necessary tables and schemas in Databricks that mirror the data structure from Railz. Use Spark DataFrames to transform and load the data efficiently. Ensure that the data is properly partitioned and indexed for optimal performance.
After the data is ingested, perform thorough checks to ensure data integrity and correctness. Compare the data between Railz and Databricks Lakehouse to ensure completeness and accuracy. Implement validation scripts or queries to cross-verify data counts, types, and values. Address any discrepancies found during this validation phase.
By following these steps, you can efficiently move data from Railz to Databricks Lakehouse 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.
The Railz API connects to major accounting, banking, and eCommerce platforms to provide you quick access to normalized and analyzed financial data on your small and medium-sized customers.
Railz's API provides access to a wide range of financial data related to small and medium-sized businesses. The data can be categorized into the following categories:
1. Financial Statements: This category includes data related to income statements, balance sheets, and cash flow statements.
2. Transaction Data: This category includes data related to transactions such as sales, purchases, and expenses.
3. Banking Data: This category includes data related to bank accounts, transactions, and balances.
4. Credit Data: This category includes data related to credit scores, credit reports, and credit history.
5. Tax Data: This category includes data related to tax filings, payments, and refunds.
6. Payroll Data: This category includes data related to employee payroll, taxes, and benefits.
7. Accounting Data: This category includes data related to general ledger, accounts payable, and accounts receivable.
8. Business Data: This category includes data related to business information such as company name, address, and industry classification.
Overall, Railz's API provides a comprehensive set of financial data that can be used by businesses and financial institutions to make informed decisions.
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:
