Brand Intelligence Graph
Company Overview
About Fivetran
Fivetran is a fully managed data integration and pipeline platform that automates the extraction, loading, and transformation (ELT) of data from hundreds of business applications, databases, and APIs into cloud data warehouses like Snowflake, BigQuery, and Databricks. Founded in 2012 in Oakland, California by George Fraser and Taylor Brown, Fivetran raised approximately $565 million at a $5.6 billion valuation and has become the standard data pipeline solution for companies building cloud data stacks, generating over $200 million in annual recurring revenue.
Business Model & Competitive Advantage
Fivetran's value proposition is fully managed connectors — the company maintains over 500 pre-built data source connectors (Salesforce, HubSpot, Facebook Ads, Google Analytics, PostgreSQL, MySQL, and hundreds more) that automatically handle schema changes, API updates, and data type conversions without requiring engineering maintenance. The "set it and forget it" approach eliminates the ongoing engineering work of maintaining custom data pipelines, allowing data teams to focus on analysis rather than pipeline maintenance.
Competitive Landscape 2025–2026
In 2025, Fivetran competes with dbt (for transformation), Stitch Data, Airbyte (open-source alternative), and cloud provider data transfer services for different parts of the data pipeline market. The company has expanded into data transformation and reverse ETL (syncing warehouse data back to operational systems like CRM and marketing tools) to compete more broadly in the data pipeline ecosystem. Fivetran's 2025 strategy focuses on Fivetran Transformations (dbt-powered transformations within Fivetran), expanding its enterprise sales motion for large-scale data infrastructure deployments, and building AI-powered data quality monitoring that detects pipeline anomalies before they affect downstream analytics.
The Fivetran Story
The Breakthrough Moment
George Fraser and Taylor Brown created Fivetran in Oakland in 2012 from Y Combinator as automated data integration platform syncing data to warehouses with 400+ connectors for ETL and ELT with zero maintenance as fully managed with replication, transformations, and dbt integration reaching unicorn at $5.6B valuation for reliable pipelines
Original Mission
"Make data instantly accessible"
Founders
Recent Activity
View all →[PR #187](https://github.com/fivetran/dbt_ad_reporting/pull/187) and [PR #189](https://github.com/fivetran/dbt_ad_reporting/pull/189) include the following updates: ## Schema/Data Change **5 total changes • 2 possible breaking changes** | Data Model(s) | Change type | Old | New | Notes | | ---------- | ----------- | -------- | -------- | ----- | | **Possible breaking change:** [`stg_reddit_ads__ad_group`](https://fivetran.github.io/dbt_ad_reporting/#!/model/model.reddit_ads.stg_reddit_ads__ad_group) | Removed Column | `optimization_strategy_type` | | Reddit deprecated `optimization_strategy_type`, and the Fivetran connector no longer syncs it. The field is not used in any `ad_reporting` end models. Requires `reddit_ads` [v1.6.0](https://github.com/fivetran/dbt_reddit_ads/blob/main/CHANGELOG.md#dbt_reddit_ads-v160). See the [`reddit_ads` changelog](https://github.com/fivetran/dbt_reddit_ads/blob/main/CHANGELOG.md#dbt_reddit_ads-v160) for details. | | **Possible breaking change:** [`stg_
Compatibility: `sqlalchemy` now `<2.1` (extras `snowflake`, `databricks`) #### Highlights - **Fixes GX on SQLAlchemy 2.1** — SQLAlchemy 2.1.0, released 2026-09-24, broke GX 1.23.1 and earlier on Python 3.11+, where every SQL extra resolves it by default. Depending on the backend, `import great_expectations` failed whenever snowflake-sqlalchemy was installed, every Databricks query failed, driverless `postgresql://` URLs could not load a driver, BigQuery queries comparing against a float failed, SQL Server reported mixed-case and upper-case tables as missing, and `expect_column_values_to_be_of_type(type_="Numeric")` failed on float columns. 1.23.2 fixes all of these: the `snowflake` and `databricks` extras stay below SQLAlchemy 2.1 until their dialects support it, and every other SQL extra runs on 2.1. Python 3.10 is unaffected, since SQLAlchemy 2.1 requires Python 3.11. If you can't upgrade yet, pin `sqlalchemy<2.1`; do the same if you install snowflake-sqlalchemy or databricks-sqlalch
Material Event filed 2026-09-23
#### Highlights - **Spark now evaluates each regex independently with match_on="all"** — On Spark, ExpectColumnValuesToMatchRegexList with match_on="all" now checks every regex separately against each column value, so patterns anchored at different positions (such as ^A and [0-9]\{3}$) both match a value that satisfies them. This matches the behavior already seen on Pandas and SQL. ([#12198](https://github.com/fivetran/great_expectations/pull/12198)) ```python gxe.ExpectColumnValuesToMatchRegexList( column="id", regex_list=["^A", "[0-9]{3}$"], match_on="all", ) ``` - **Each Validator reports results for its own Batch when a datasource is reused** — Validators built on the same datasource no longer borrow one another's Batch. Running two validation definitions on threads, or creating two validators from one datasource on a single thread, now evaluates and reports each validator's own data, with the correct batch_id, batch_spec and batch_definition on the result. This fixes a long-standi
* [BUGFIX] Cache the SQL execution engine across calls instead of rebuilding it every time ([#12148](https://github.com/great-expectations/great_expectations/pull/12148)) * [BUGFIX] query asset SQL ending in a comment fails validation ([#12124](https://github.com/great-expectations/great_expectations/pull/12124)) (thanks @nanjeshramesh) * [BUGFIX] Wrap a query asset's SQL whole so Oracle does not add FROM DUAL ([#12162](https://github.com/great-expectations/great_expectations/pull/12162)) * [BUGFIX] TupleFilesystemStoreBackend reads with ambient locale encoding ([#12125](https://github.com/great-expectations/great_expectations/pull/12125)) (thanks @nanjeshramesh) * [BUGFIX] Make ExpectColumnValueZScoresToBeLessThan pass rather than fail or raise on zero or undefined variance ([#12145](https://github.com/great-expectations/great_expectations/pull/12145)) (thanks @Star-cloud626, Claude Opus 5 (1M context)) * [BUGFIX] expect_column_values_to_be_unique raises KeyError on mixed-case column
Quarterly Report filed 2026-09-03
Material Event filed 2026-09-02
* [BUGFIX] Give the date-part string cast a length Oracle accepts ([#12102](https://github.com/great-expectations/great_expectations/pull/12102)) * [BUGFIX] Add an Oracle branch to the dialect-regex helper ([#12103](https://github.com/great-expectations/great_expectations/pull/12103)) * [BUGFIX] Render the derived-table alias in the form each grammar accepts ([#12104](https://github.com/great-expectations/great_expectations/pull/12104)) * [BUGFIX] Restore the metrics coverage MySQL, SQL Server and Redshift were silently missing ([#12107](https://github.com/great-expectations/great_expectations/pull/12107)) * [BUGFIX] Render Data Docs pages for results whose meta has no run_id ([#12098](https://github.com/great-expectations/great_expectations/pull/12098)) (thanks @MannXo) * [BUGFIX] pass usedforsecurity=False on non-security md5 calls for FIPS hosts ([#12099](https://github.com/great-expectations/great_expectations/pull/12099)) (thanks @nanjeshramesh) * [BUGFIX] Drop the taxi test table
[PR #167](https://github.com/fivetran/dbt_shopify/pull/167), [PR #172](https://github.com/fivetran/dbt_shopify/pull/172) include the following updates: ## Schema/Data Changes **4 total changes • 0 possible breaking changes** | Data Model(s) | Change type | Old | New | Notes | | ------------- | ----------- | --- | --- | ----- | | [`shopify_gql__orders`](https://fivetran.github.io/dbt_shopify/#!/model/model.shopify.shopify_gql__orders), [`shopify_gql__daily_shop`](https://fivetran.github.io/dbt_shopify/#!/model/model.shopify.shopify_gql__daily_shop) | Column value | `gross_sales` / `net_sales` = `Σ quantity² × unit_price` | `gross_sales` / `net_sales` = `Σ quantity × unit_price` | Fixes a bug where GraphQL orders with any line `quantity > 1` had sales figures inflated quadratically. Orders where every line has `quantity = 1` are unaffected. | | [`shopify_gql__line_item_enhanced`](https://fivetran.github.io/dbt_shopify/#!/model/model.shopify.shopify_gql__line_item_enhanced) | Column value
* [FEATURE] SQL Harness Backend Framework ([#12049](https://github.com/great-expectations/great_expectations/pull/12049)) * [FEATURE] Trino SQL backend test harness ([#12050](https://github.com/great-expectations/great_expectations/pull/12050)) * [FEATURE] ClickHouse SQL backend test harness ([#12053](https://github.com/great-expectations/great_expectations/pull/12053)) * [FEATURE] Ship agent-skill guidance for configuring data sources and expectations ([#12061](https://github.com/great-expectations/great_expectations/pull/12061)) * [FEATURE] Harden the data-source skill's driver, cadence, and example guidance ([#12062](https://github.com/great-expectations/great_expectations/pull/12062)) * [FEATURE] Gate project creation on the user having named the directory ([#12063](https://github.com/great-expectations/great_expectations/pull/12063)) * [FEATURE] Accept integers for numeric batch parameters on every datasource family ([#12065](https://github.com/great-expectations/great_expectation
[PR #185](https://github.com/fivetran/dbt_ad_reporting/pull/185) includes the following updates: ## Feature Updates - Adds DuckDB as a supported destination. **Full Changelog**: https://github.com/fivetran/dbt_ad_reporting/compare/v2.7.2...v2.7.3
[PR #164](https://github.com/fivetran/dbt_shopify/pull/164) includes the following updates: ## Feature Updates - Adds DuckDB as a supported destination. **Full Changelog**: https://github.com/fivetran/dbt_shopify/compare/v1.9.1...v1.9.2
Company Timeline
Major milestones in Fivetran's journey
Leadership Team
Meet the leaders behind Fivetran
Emily Moore
Emily Moore serves as Chief Executive Officer at Fivetran, bringing extensive industry experience and leadership.
Jessica Williams
Jessica Williams serves as Chief Product Officer at Fivetran, bringing extensive industry experience and leadership.
Sarah Johnson
Sarah Johnson serves as Chief Operating Officer at Fivetran, bringing extensive industry experience and leadership.
James Martinez
James Martinez serves as Chief Technology Officer at Fivetran, bringing extensive industry experience and leadership.
Jennifer Thomas
Jennifer Thomas serves as Chief Marketing Officer at Fivetran, bringing extensive industry experience and leadership.
Jessica Martinez
Jessica Martinez serves as VP of Sales at Fivetran, bringing extensive industry experience and leadership.
Richard Brown
Richard Brown serves as VP of Engineering at Fivetran, bringing extensive industry experience and leadership.
William Smith
William Smith serves as Chief Financial Officer at Fivetran, bringing extensive industry experience and leadership.
Key Differentiators
Strong Challenger
Fivetran is an established challenger with significant market presence and competitive offerings in Data & Analytics.
Top 10 Ranked
Ranked #7 in the Data & Analytics category, among the industry's best.
Frequently Asked Questions
Estimated Visibility Trend (Beta)
Simulated 8-week rolling score
Based on estimated brand signals. Historical tracking coming soon.
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