Supports MongoDB, Firestore, DynamoDB & more

NoSQL to SQL migration
done right

The complete platform for migrating NoSQL databases to SQL. Schema mapping, live CDC replication, zero downtime, and support for every major source and target.

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Why teams migrate from NoSQL to SQL

NoSQL databases excel at flexibility and scale, but many teams reach a point where SQL's structure, query power, and ecosystem become essential.

JOIN

Ad-hoc querying

SQL gives you JOINs, aggregations, window functions, and the entire ecosystem of BI tools that speak SQL natively. No more writing MapReduce or aggregation pipelines.

SCHEMA

Schema enforcement

As your data model stabilizes, schema enforcement prevents data quality issues that flexible schemas allow. Catch type mismatches at write time, not at query time.

BI

Analytics ecosystem

SQL databases plug directly into Looker, Tableau, Metabase, and dbt. No need for ETL pipelines to move data from NoSQL to an analytics store — your operational DB is your analytics DB.

SQL

Team expertise

SQL is the lingua franca of data. Your analysts, data scientists, and backend engineers all speak it. Consolidating on SQL reduces the cognitive overhead of maintaining two query paradigms.

ACID

Transactions and ACID

Multi-row transactions, foreign key constraints, and referential integrity are first-class in SQL. No more writing client-side logic to simulate transactional guarantees.

COST

Cost optimization

For structured workloads, SQL databases offer predictable pricing (per instance or per query), vs. NoSQL pricing models (per operation) that can balloon at scale.

Sources & targets

Supported databases

Any combination of source and target is supported. Mix and match freely.

NoSQL Sources
MongoDB
Community & Atlas. Sharded clusters supported.
Firestore
Google Cloud Firestore. Native & Datastore mode.
DynamoDB
AWS DynamoDB with Streams for CDC.
Cosmos DB
MongoDB API and SQL API (coming Q3 2026).
SQL Targets
PostgreSQL
All versions. RDS, Aurora, Cloud SQL, Supabase.
MySQL
5.7 & 8.0. RDS, Cloud SQL, PlanetScale.
BigQuery
Storage Write API. Partitioned & clustered tables.
Snowflake
VARIANT & structured. Snowpipe integration.
Redshift
Amazon Redshift. DISTKEY & SORTKEY config.
SQL Server
2019+. RDS, Azure SQL, self-hosted.
How it works

The NoSQL to SQL migration pipeline

01

Schema Discovery

NoSQLSync samples your source database and infers a complete schema. Detects fields, types, nested structures, and relationships across all collections or tables.

02

Type Mapping

Every NoSQL type (ObjectId, ISODate, NumberInt, Array, nested Object) is mapped to the best SQL equivalent for your target. You can override any mapping in the visual editor.

03

Bulk Migration

The initial load transfers all historical data. Parallel workers maximize throughput. You choose the batch size, concurrency, and whether to pause the source application.

04

Live CDC Sync

Once the bulk load reaches a steady state, CDC takes over. All new writes flow from NoSQL source to SQL target in real time with sub-second latency.

05

Validation

Compare record counts, checksums, and sample documents between source and target. Our validation dashboard shows exactly which records differ — and why.

06

Cutover

When you're confident the data is consistent, switch your application reads and writes to the SQL target. Dual-write mode means you can roll back to the NoSQL source instantly.

Frequently asked questions

Is NoSQL to SQL migration really zero downtime?

Yes — our dual-write mode keeps both databases in sync. Your application continues writing to the NoSQL source while NoSQLSync replays those writes to the SQL target. Once validated, you switch your application to the SQL target as primary. If anything goes wrong, you reverse the cutover in one click.

What happens to embedded documents and arrays?

Embedded documents become JSONB (PostgreSQL), JSON (MySQL), STRUCT/VARIANT (BigQuery/Snowflake), or normalized join tables. Our schema mapper lets you choose per field. Arrays can become native SQL arrays, JSON arrays, or separate tables.

How do you handle schema-less collections?

NoSQLSync samples documents and infers a schema that covers the union of all observed fields. Fields that appear in some documents but not others become nullable columns. You can configure sampling depth and field inference thresholds.

Can I migrate a subset of my data?

Yes. Apply filter queries to select specific documents or table rows. Filter by date range, status field, or any query your source database supports. Partial migrations are fully supported for both bulk load and CDC.

How secure is the migration pipeline?

All data is encrypted in transit (TLS 1.3) and at rest (AES-256). Database credentials use short-lived tokens. Migration workers run in an isolated environment and never store your data after the job completes.

What if my NoSQL source has a custom schema?

Our schema mapper supports custom type overrides, column renaming, field exclusion, and custom SQL transformations. You can write SQL expressions to transform data during migration (e.g., format dates, concat fields, compute derived columns).

Popular migration paths

MongoDB → PostgreSQLMongoDB → MySQLMongoDB → BigQueryMongoDB → SnowflakeDynamoDB → PostgreSQLFirestore → BigQuery

Start your NoSQL to SQL migration

Free plan includes 3 migrations per month. Production-ready in minutes.

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