Google AnalyticsAzure SQL Server
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Google Analytics + Azure SQL Server

Google Analytics is a tool for website and app analytics that provides insights into traffic and user behavior. Azure SQL Server, on the other hand, is a serverless Big Data solution developed by Google that delivers robust large-scale data storage and analytics capabilities.

With Erathos, you can integrate data from Google Analytics to Azure SQL Server in just a few minutes. Our platform handles the entire data movement process to your analytical environment, allowing you to blend this data with other sources in your Data Warehouse. This way, your time is focused on what truly drives value — extracting actionable insights and making more data-driven decisions.

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Xperiun
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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.

What Google Analytics data does Erathos sync with Azure SQL Server?

The integration automatically syncs the key Google Analytics objects:

  • Events — all tracked events with custom properties
  • Sessions — source, duration, device, and behavioral data
  • Users — profiles, properties, and activity history
  • Conversion funnels — steps, drop-offs, and rates by segment
  • Cohorts — retention and engagement over time
  • Custom properties — event properties and user properties defined by your team

Why sync Google Analytics with Azure SQL Server?

In Azure SQL Server, you integrate Google Analytics data into the Microsoft ecosystem, using Power BI for executive dashboards and Azure Data Factory to orchestrate broader pipelines.

How it works

Erathos connects to Google Analytics via the official API and syncs your data incrementally — only new or updated records are processed in each run, keeping pipelines fast and Azure SQL Server costs predictable. You choose the sync frequency (ranging from 5 minutes to daily), the objects to sync, and the target dataset. Each run is logged with full observability: execution time, rows processed, error context, and instant alerts via Slack or email if anything goes wrong.

Why companies move Google Analytics data to Azure SQL Server with Erathos

Centralizing Google Analytics data in Azure SQL Server has never been easier

Erathos is a data ingestion platform built for product and analytics teams. With our Google Analytics connector, you can automatically export events, product metrics, and conversion funnels directly to Azure SQL Server — keeping your data fresh for analysis, without manual engineering.

Why do data teams choose Erathos for Google Analytics?

Data in your data warehouse 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
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FAQ

Frequently Asked Questions

Erathos is a data ingestion platform built for reliability, transparency, and control. We help data teams connect tools like Google Analytics to their data warehouse—with full observability into every run, zero maintenance, and without the opacity of traditional market tools.

Erathos syncs Events, Sessions, Users, Conversion Funnels, and Product Metrics from Google Analytics to Azure SQL Server. Custom event properties and user properties are automatically exported along with each record.

You can configure 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 your Google Analytics pipeline efficient and Azure SQL Server 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 Google Analytics to Azure SQL Server and start syncing immediately — no credit card required.

Data ingestion with control, observability and scale