How do you migrate data to a document database?

Migrating data to a document database involves extracting data from its current source, transforming it to fit the document model, and loading it into the target database. The process starts by analyzing the existing data structure and determining how it maps to a schema-less document format like JSON or BSON. For example, if migrating from a relational database, tables might be converted into nested documents or collections, denormalizing relationships to avoid joins. Tools like ETL (Extract, Transform, Load) pipelines, custom scripts, or database-specific utilities (e.g., MongoDB’s mongoimport) are commonly used to automate parts of this workflow.

The transformation phase is critical. Document databases prioritize flexible schemas, so data often needs restructuring. For instance, a relational table with customer orders might split orders and customer details into separate tables, requiring joins. In a document database, this could become a single document embedding order items and customer data. Developers must also handle data type conversions, such as translating SQL date formats to BSON-compatible dates. Handling relationships that don’t fit naturally into embedded documents may require using references (like document IDs) and application-level logic to resolve them. Tools like Apache NiFi or Python libraries (e.g., pandas for data manipulation) can streamline these transformations.

Finally, the loading phase involves importing the transformed data into the document database. Batch operations or bulk writes are preferred for efficiency. For example, MongoDB’s insertMany() method allows inserting thousands of documents in a single call. Validation is essential: checks for data consistency, missing fields, or duplicate keys ensure integrity. Post-migration, indexing strategies should be tested to optimize query performance. Developers might also implement incremental migration for large datasets to minimize downtime, updating only changed data. Testing with a subset of data first helps identify issues early. Once validated, the full migration can proceed, followed by application updates to use the new database’s query patterns (e.g., replacing SQL with MongoDB’s aggregation pipelines).