Hand-built loads are slow and fragile
Export the table, stage the files, load them into the warehouse - three steps, three tools, and scripts someone has to babysit, plus hours of runtime every time a big table moves.
Solutions / Database Replication
A billion-row table in your warehouse.
On a schedule, not a project plan.
Dataddo replicates your operational databases into any analytical destination and keeps them current - parallel extraction that moves millions of records per run at bulk-tool-beating speed, then incremental sync tuned to how each table actually changes.
Replication managed end to end - no replication platform to build.
The problem
Your business runs on databases. Your analytics runs on warehouses. Keeping them in sync is the hard part.
Operational databases hold the freshest, most valuable data in the company - and the warehouse, lake, and AI pipelines that depend on it drift out of date the moment a load finishes. Closing that gap reliably, for every database you run, is the actual problem. The usual approaches fail predictably:
Re-extracting everything hammers production
Full reloads on every run turn your analytics schedule into a recurring load the source database has to absorb, competing with the application it exists to serve.
Changes go missing, silently
Simple sync approaches catch new rows but quietly miss updated or deleted ones. Nothing fails, no alert fires, dashboards keep rendering - the numbers are just wrong.
Dataddo closes the gap with analytical replication - a fast initial full load, then ongoing sync matched to each table - across virtually every database on the market, cloud or on-prem.
How it works
One table, many readers.
Most tools read a big table through a single connection - one straw, however big the glass. Dataddo's Mesh Ingestion™ engine sends up to 64 parallel readers into the table at once, each taking its own slice, balanced so no single reader drags the run. And as your tables grow, Dataddo automatically shifts to faster loading paths behind the scenes - you never re-architect a pipeline because the data got big.
Your database doesn't have to be reachable from the internet.
Cloud databases, on-prem databases behind a firewall, or both: Dataddo's data plane deploys where your data lives - on-prem or in your own cloud - while you manage every pipeline from a single cloud control plane. The data moves next to the database; it is never forced through anyone else's cloud.
Sync tuned to how each table changes.
After the initial load, you choose per table how changes flow:
| Method | Captures | Best for |
|---|---|---|
| Timestamp replication | New + updated rows | Frequently edited tables: orders, profiles, inventory |
| Row-sequence replication | New rows (cheapest) | Append-only logs, transactions, events |
| Log-based CDC | New + updated + deleted rows, real-time | High-volume, latency-sensitive tables |
| Custom SQL | You decide | Joins, filters, pre-aggregation before extraction |
Performance - measured
Measured against native bulk tools. Not against a brochure.
1.8 s
per 1M rows · 10-column table · ~22x faster than bcp (40.2 s)
28.9 s
per 1M rows · 250-column table · ~5x faster than bcp (150.8 s)
64
parallel readers, max · primary-key chunking + worker balancing
SQL Server to BigQuery full load, versus the source's own bcp bulk-copy utility exporting the same tables from the same server. Internal benchmark.
(Probably) the fastest ongoing transfers in the industry. Bring your own workload and test us in your environment - the more complex, the better.
Why speed matters
Faster loads, faster answers.
1
Analytics
Time to insight in hours, not weeks.
POCs, backfills, and reprocessing finish in hours, not multi-day batch windows. You learn whether an idea works this afternoon, not next sprint.
2
Risk
Operational resilience.
When a full reload costs hours instead of days, schema changes, bad refreshes, and recovery become routine instead of risky.
3
Decisions
Fresh replicas for analytics and AI.
A fast initial load plus ongoing sync keeps the warehouse copy current enough for the decisions that depend on it.
Capabilities
Managed end to end.
No duplicates, ever.
Re-extracted rows update your destination instead of piling up in it - even on tables without a clean unique key, thanks to a stable key Dataddo generates for you.
Gentle on production.
Chunked reads spread load across the run, so extraction never puts more pressure on your source database than you decide it should.
History included.
A one-time backfill loads a table's full history, so replicas start complete instead of empty. And if a target ever drifts, Full Data Re-Sync reloads everything on demand - recovery is a button, not a war room.
Per-period tables, automatically.
Repeated full loads can land in per-period tables (this month, last month...) instead of overwriting one table - a time series builds itself.
Quality-checked on the way in.
The Data Quality Firewall inspects every load before it lands. Bad loads can be stopped at the door, or delivered with an alert - your call.
Virtually every database on the market.
PostgreSQL, MySQL, SQL Server, Oracle, and MariaDB as sources - into destinations like Snowflake, BigQuery, Redshift, Databricks, and more. If your data lives in a major database, Dataddo replicates from it.
Need deletes or real-time? Batch replication doesn't capture deleted rows - that's a fact of the method, not a footnote. For tables where deletes and latency matter, the same platform does log-based CDC: Real-Time CDC →
Time to value
Your first replicated table today, not next quarter.
Pick the table, pick the method, pick the destination - Dataddo handles parallel extraction, adaptive ingestion, write modes, and monitoring, with schedules down to 1 minute. The export-stage-load pipeline your team was about to build, with its three tools and its orchestration glue? We run the machinery instead.
Start a database replication proof of concept.
A scoped, time-boxed POC for your source database and destination. Bring your own workload - the more complex, the better.