PostgreSQL
MongoDB
Extract tables from PostgreSQL databases, transform and validate records, and load structured data directly into MongoDB collections without writing pipeline code. Dagflux handles schema detection, document mapping, data quality gates, and loading from a visual canvas.
PostgreSQL stores structured relational data for applications, CRMs, and internal systems. MongoDB is built for flexible document-based workloads. Dagflux bridges the two by extracting PostgreSQL tables, transforming rows into documents, validating outputs, and loading clean records into MongoDB without custom pipeline scripts.
Dagflux uses a visual node-based canvas to build the PostgreSQL to MongoDB pipeline. Connect your source, describe the document mapping, validate, and load.
Add a Data Source node for your PostgreSQL instance. Dagflux detects schemas, table names, column types, and row counts automatically.
Join related PostgreSQL tables, clean fields, rename keys, cast types, and shape records into MongoDB-ready documents.
Run schema checks, null checks, and row-count validation before loading clean documents into your selected MongoDB database and collection.
Raw PostgreSQL rows often need reshaping before they fit a document collection. Dagflux helps you generate transformations from plain English, review the logic, and approve changes before execution.
Convert PostgreSQL timestamps, numeric fields, booleans, arrays, and text values into MongoDB-friendly document fields.
Select only the fields you need, rename keys, create nested customer or order objects, and add derived fields.
Generated transformation logic is visible before it runs, so your team can review, refine, or edit before moving data.
The Branch node runs validation checks before the output step, so only clean documents reach your MongoDB collection.
Check that output documents contain required keys, nested objects, IDs, and expected data types before loading begins.
Validate required fields, null rates, document counts, and key metrics before data reaches target collections.
Route failed records to review paths while clean documents continue into MongoDB.
Dagflux gives data, analytics, and engineering teams a reviewable, configurable pipeline from PostgreSQL to MongoDB. Every transformation is visible, every validation rule is configurable, and every run produces logs with document counts, duration, and error details.
Create a working PostgreSQL to MongoDB pipeline without manually writing extraction, transformation, and load scripts.
Inspect field selections, nested objects, type casts, filters, and joins before any data is moved.
Use Branch nodes to validate required keys, type compatibility, null rates, and document counts before loading.
Move application tables such as orders, users, events, and subscriptions from PostgreSQL into MongoDB.
Join and transform PostgreSQL tables into clean MongoDB documents for apps, APIs, or operational stores.
Extract historical PostgreSQL snapshots, reshape schemas, and load structured records into MongoDB collections.
Schedule recurring runs to sync new or updated PostgreSQL rows to MongoDB.
Add CSVs, JSON files, MySQL tables, or cloud warehouse sources alongside PostgreSQL data.
Enforce document shape, required keys, and completeness checks before records reach production MongoDB collections.
Dagflux supports multiple source types alongside PostgreSQL. Add CSV exports, JSON files, MySQL databases, cloud warehouse tables, or object storage files and join them with your PostgreSQL tables before loading into MongoDB.
Extract from any schema or table with auto-detected columns and types.
Add flat file exports alongside database sources and join on shared keys.
Use MongoDB collections as part of larger transformation and validation workflows.
Source from MySQL databases and merge with PostgreSQL tables in one pipeline.
Pull from Snowflake warehouse tables and join them with operational data.
Read Parquet, CSV, or JSON files from S3 and join with PostgreSQL sources.
Connect your PostgreSQL database, describe the transformation, validate the output, and load structured documents into MongoDB.
