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Begin by exporting your data from FaunaDB. This can be achieved by using FaunaDB's FQL (Fauna Query Language) to query your data and export it to a JSON or CSV format. Utilize FaunaDB's dashboard or Fauna shell to execute a query that retrieves the necessary data and writes it to a file.
Once the data is exported, ensure its integrity and completeness. Open the exported file and verify that all necessary fields and records are present. Check for any anomalies or missing data that might have occurred during the export process.
Log into your Databricks account and set up a new cluster if needed. Ensure that your cluster is properly configured with the necessary compute resources and configurations suited to handle the data you plan to import.
Use the Databricks interface to upload your exported data file to the Databricks File System (DBFS). You can do this by navigating to the 'Data' section in Databricks, selecting 'DBFS', and using the upload functionality to transfer the JSON or CSV file.
Open a new notebook in Databricks and read the uploaded file from DBFS. Use Spark's built-in functions (e.g., `spark.read.json()` or `spark.read.csv()`) to load the file into a DataFrame. Perform any necessary transformations or cleaning operations to prepare the data for integration into the Databricks Lakehouse.
Create a new table in the Databricks Lakehouse to store the imported data. Define the schema of the table to match the structure of your DataFrame. This can be achieved using SQL commands within a Databricks notebook to create a Delta table.
Finally, load the prepared DataFrame into the newly created table. Use Spark's DataFrame API to write data to the Lakehouse. For example, use the `DataFrame.write.format("delta").saveAsTable("tableName")` method to save the DataFrame to the Delta table. Verify that the data has been successfully loaded by querying the table and checking for accuracy.
By following these steps, you can effectively transfer data from FaunaDB 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.
Fauna merges the flexibility of NoSQL with the relational querying capabilities and ACID consistency of SQL systems. Fauna implements a semi-structured, schema-free, object-relational data model, strict superset of relational, document, object-oriented, and graph. Fauna is a tool in Databases category of tech stack. Inventory of fauna as a tool for sustainable use of economically important mammal species. This is used by animals is a phenomenon in which an animal uses any kind of tool to attain a goal such as acquiring food and water, grooming, defense.
Fauna's API gives access to various types of data, including:
1. Documents: This includes JSON documents that can be stored, retrieved, and queried using Fauna's API.
2. Collections: Collections are groups of documents that share a common schema. They can be used to organize data and make it easier to query.
3. Indexes: Indexes are used to speed up queries by precomputing results. They can be created on any field in a collection.
4. Functions: Functions are reusable blocks of code that can be called from within queries. They can be used to perform complex calculations or manipulate data.
5. Roles: Roles are used to control access to data. They can be used to define permissions for different types of users or applications.
6. Keys: Keys are used to authenticate requests to Fauna's API. They can be used to control access to data and to track usage.
Overall, Fauna's API provides a flexible and powerful way to store, retrieve, and manipulate data. It can be used for a wide range of applications, from simple data storage to complex data analysis and processing.
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: