Connect everything,in minutes
Plug Postgres, Oracle, MongoDB, ClickHouse and ten other engines — plus CSV, JSON and Parquet files — under one governed query layer. Encrypted credentials, pooled connections, schema sync out of the box.
// connectors · 10 engines
// datasource · oracle.billing
// scan
tables
284
rows
62.1M
scanned
8.2 s
freshness
live
One client for every source
Databases, files and clouds — all queried, governed and audited the same way.
10+ database engines
Postgres, MySQL, SQL Server, Oracle, MongoDB, ClickHouse, Cassandra, Neo4j, Elastic — and more shipping monthly.
Files first-class
CSV, JSON, Parquet and Excel — local, S3, GCS or Azure. Same query model, same governance.
Encrypted credentials
AES-256-GCM at rest, key rotation supported. Credentials never appear in logs, configs or query plans.
From credentials to a searchable catalog
Four steps — and the source is queryable, governed and lineage-aware.
Discover
engine · host · port · sample probe
Authenticate
user · key · iam · oauth
Sample
schemas · tables · row counts
Catalog
indexed · classified · searchable
Same shape for every engine
Datasources are declarative — code, YAML or REST. Add one, scan, query.
from sofi import Sofi
sofi = Sofi(api_key="YOUR_KEY")
ds = sofi.datasource.create(
name="oracle.billing",
engine="oracle",
host="oracle.acme.local",
port=1521,
service="BILLING",
auth={"type": "user", "user": "sofi", "password_env": "ORACLE_PWD"},
pool={"min": 2, "max": 16},
)
# scan and sample
ds.scan(sample=True)
print(ds.tables[:5])Every stack, federated
Hybrid clouds, legacy systems, file lakes, migrations — all under one connector layer.
// pattern
Hybrid stack
Operational Postgres, analytical ClickHouse, document MongoDB, legacy Oracle — federate them under one query layer without picking a winner.
// pattern
Legacy integration
Plug Oracle, SQL Server and AS/400 into modern apps without ETL pipelines. Read-only by default, write-through where needed.
// pattern
File analytics
Query CSV, JSON and Parquet on S3 like any other table. Schema is inferred, refreshable and cataloged.
// pattern
DB migration cutover
Run old and new engines side-by-side under one virtual schema. Rebalance traffic gradually, roll back instantly.
Connectors that behave in production
Pooled, encrypted, monitored — and they ship monthly.
10+
engines supported
Postgres, MySQL, SQL Server, Oracle, MongoDB, ClickHouse, Cassandra, Neo4j, Elastic — file formats included.
AES-256
credential encryption
GCM mode, per-tenant keys, rotation supported. Secrets stay encrypted at rest and in transit.
Pool
managed connections
Connection pools per datasource with health checks, retry budgets and graceful drain on rotation.
Live
schema sync
Schema changes propagate to the catalog within seconds. Views surface deprecation warnings before they break.
Questions about connect
What infra teams ask before adopting Connect as the integration layer.
Postgres, MySQL, SQL Server, Oracle, MongoDB, ClickHouse, Cassandra, Neo4j and Elasticsearch. File formats: CSV, JSON, Parquet, Excel. New connectors ship monthly — Snowflake, BigQuery and Databricks are next.
// ready to connect
Plug your sources, query in minutes.
Add a datasource via SDK or YAML, run the scanner, watch the catalog populate. Trial includes all 10 engines.