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Begin by examining the data structure and schema in Fauna. This involves identifying the collections, indexes, and field types. Use the Fauna Query Language (FQL) to list and describe these resources to create a map of the data that needs to be migrated.
Write a script in FQL or use a Fauna client library (such as JavaScript, Java, Python, etc.) to extract data from Fauna collections. Query the data and serialize it into a JSON or CSV format, which can be easily processed and imported into Apache Iceberg.
Install and set up Apache Iceberg on your local machine or server. Ensure that you have the necessary environment, such as a Hadoop-compatible file system or an object store like S3, as Iceberg requires a storage layer to manage its data files.
Convert the exported JSON or CSV data into a format compatible with Apache Iceberg, typically Parquet or Avro. Use data processing tools like Apache Spark or local scripts to read the exported files and write them in the desired format. Ensure that the data types and schema are consistent with what Iceberg expects.
Create the schema for your Iceberg table using SQL-like DDL commands in an environment that supports Iceberg queries (e.g., Apache Spark or Trino). Define the table structure, including column names, data types, and any partitioning strategy that aligns with your data access patterns.
Use a compatible processing engine such as Apache Spark to load the transformed data files (Parquet/Avro) into the Iceberg table. Write a script to read the prepared files and insert them into the defined Iceberg table, making sure to respect schema evolution and partitioning logic.
Conduct thorough verification to ensure that all data has been accurately moved from Fauna to Apache Iceberg. Perform checks to compare row counts and sample data between the two systems. Use Iceberg"s built-in features to validate the data consistency and integrity, and adjust any discrepancies found during the verification process.
By following these steps, you can successfully migrate your data from Fauna to Apache Iceberg 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?
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