SQL ServerAmazon S3

SQL Server + Amazon S3

SQL Server is a relational database management system developed by Microsoft, used to store and manage large volumes of data.

With Erathos, you can integrate data from SQL Server into Amazon S3 in just a few minutes. Our platform handles the entire data movement process into your data lake and makes it possible to combine that data with other sources. That way, your time goes toward what truly creates value — extracting insights and making data-driven decisions.

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Convenia
Vakinha
Linus
Rabbot
Auvo
Roqt
Xperiun
Tecnova
WePayments
Brick
CCM
Simplified extraction and ingestion

Move data in a few clicks

1

Connect your source

Authenticate your account and you're done. 100+ ready-made connectors, no code required.

2

Configure the sync

Pick the tables, the schedule and the update type: batch, cursor-based incremental or CDC.

3

Choose your destination

Load into BigQuery, Redshift, Databricks, PostgreSQL, ClickHouse, Amazon S3 and more.

Which SQL Server data does Erathos sync to Amazon S3?

The integration automatically syncs the main SQL Server objects:

  • Tables and collections — structure, records, and metadata
  • Records and documents — raw data with creation and update timestamps
  • Indexes — settings and performance metrics
  • Schemas — data type definitions and relationships
  • Audit logs — write, update, and delete operations
  • Connections — user and session metadata

Why sync SQL Server with Amazon S3?

In Amazon S3 with Iceberg, your data is stored as Parquet files with support for time travel and schema evolution — ready to be queried with Athena, Spark, Trino, or any query engine in your stack. Ideal for low-cost archiving and ML feature stores.

How it works

Erathos connects to SQL Server through the official API and syncs your data incrementally — only new or updated records are processed on each run. You choose the sync frequency (from every 5 minutes to daily), the objects to sync, and the destination in Amazon S3. The sync uses automatic partitioning and Parquet file optimization. You choose the sync frequency, the objects to sync, and the destination bucket. Each run is logged with full observability: execution time, processed rows, errors with context, and alerts via Slack or email.

Why Companies Move Data from SQL Server to Amazon S3 with Erathos

Centralizing SQL Server data in Amazon S3 has never been easier

Erathos is a data ingestion platform built for data and engineering teams. With the SQL Server connector, you can automatically centralize SQL Server tables and objects in Amazon S3 — always up-to-date data, full visibility into every run, and zero maintenance.

Why do data teams choose Erathos for SQL Server?

SQL Server data in Amazon S3 in minutes

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Building data-driven stories

Every new source implementation, if I had to do it myself, would take about two or three months. With Erathos, it's done in a few hours. I don't need the skills of a data engineer to generate real value from the company's data.

Bruno Rosso
Bruno RossoData Analyst, Brick Software

Erathos revolutionized data management at WE. By integrating multiple SaaS tools into a single DW, our technical team now focuses on the core business. We implemented dashboards with insights across every area, enriching our organizational culture and improving our decision-making.

Matheus Nunes
Matheus NunesCTO & co-founder, WePayments

We used to have a lot of rework, and now we don't. It's more efficient to work this way. If it's not your core business, hire Erathos. Spend your time modeling your own domain, not someone else's.

Carlos Schwabe
Carlos SchwabeCo-Founder & CDO, Brick

If I didn't have this infrastructure, I'd need three or four people to do what Erathos does today. I was able to generate real value from my data with a much leaner setup than I thought I'd need. That's the core of building a data-driven culture: the team only uses data when they can trust it.

Thiago Paz
Thiago PazCFO, Brick

Erathos brought a practical turnaround at CCM. We were able to integrate financial systems, CRM, and processes with BigQuery in just a few clicks, with no technical team required. That gave us a reliable data warehouse that powers automations, dashboards, and even our customer service bots.

Gustavo Loforte
Gustavo LoforteTechnology Manager, CCM Group

The robustness and efficiency of Erathos's connectors — whether for Meta, Google, RD Station, or ERPs like Bling and Conta Azul — make the whole process much faster and more reliable. The technical documentation is extremely well put together and intuitive, which makes implementation much easier.

Sayuri Valente
Sayuri ValenteProject Manager, Xperiun
Explore

Other paths for your data

Other sources that land in Amazon S3

CRMs, ERPs, ad platforms and databases, all through the same managed pipeline.

See every source
FAQ

Frequently Asked Questions

Erathos is a data ingestion platform built for reliability, transparency, and control. We help data teams connect tools like SQL Server to their data warehouse — with full observability into every run, zero maintenance, and none of the black-box behavior common in traditional market tools.

Erathos performs incremental or full replication from SQL Server to your destination data warehouse, including tables, schemas, and relationships. Sync frequency and schedule are configurable, with no code required.

You can set the sync frequency from every 5 minutes to daily, at the table level. Erathos uses incremental sync — only new or updated records are processed in each run, keeping the SQL Server pipeline efficient and Amazon S3 costs predictable.

Erathos automatically detects failures and sends alerts to your email, Slack, or Discord with full context — not just “job failed.” Smart retries handle transient errors, and every run is logged with runtime, rows processed, and error context so your team can debug in minutes, not hours.

Yes. Every Erathos connector includes a 14-day free trial. Connect SQL Server to Amazon S3 and start syncing right away — no credit card required.

Data ingestion with control, observability and scale