What is Fivetran?

Fivetran is a fully managed, cloud-native automated data integration platform. It fundamentally simplifies the process of extracting data from hundreds of disparate operational systems (like Salesforce, Zendesk, PostgreSQL, or Stripe) and loading it reliably into a central cloud data warehouse or open data lakehouse.

Historically, organizations employed massive teams of data engineers strictly to write custom API polling scripts. If an API endpoint changed, or if a database schema updated unexpectedly, the fragile custom scripts crashed, destroying the downstream analytical dashboards. Fivetran resolves this profound operational burden by providing pre-built, deeply maintained connectors that extract, normalize, and load data completely automatically, allowing data engineering teams to focus entirely on analytical transformations rather than pipeline maintenance.

Driving the ELT Paradigm

Fivetran was one of the primary catalysts for the industry-wide shift from traditional ETL (Extract, Transform, Load) to modern ELT (Extract, Load, Transform).

In the legacy ETL model, data was pulled from a source, heavily transformed on an intermediate integration server, and then loaded into a highly rigid data warehouse. This was necessary because legacy databases lacked the computational power to handle raw, messy data.

As incredibly powerful cloud data platforms (like Snowflake, BigQuery, and Dremio) emerged, this bottleneck became obsolete. Fivetran explicitly extracts the raw data and loads it directly into the destination storage identically to the source schema. Once the data lands securely in the cloud, tools like dbt (Data Build Tool) execute the complex SQL transformations entirely within the destination engine. Fivetran exclusively manages the “E” and the “L”.

Automated Schema Drift Handling

The most complex challenge in data ingestion is managing schema drift. In an active organization, software engineers constantly alter operational databases. They add new columns, delete old tables, and change data types (e.g., converting an Integer column to a String to accommodate a new tracking format).

When a rigid, custom-built pipeline encounters an unexpected column, it typically crashes. Fivetran is engineered to handle schema drift automatically and seamlessly. If an upstream application adds a new customer_tier column to a PostgreSQL table, Fivetran detects the modification during its next extraction cycle. It automatically issues the necessary DDL (Data Definition Language) command to add the corresponding customer_tier column to the destination Snowflake or Iceberg table, and populates the data without dropping a single record or requiring any manual engineering intervention.

Change Data Capture (CDC) and Incremental Updates

Executing a massive “Full Refresh” (extracting every single record from a massive database every night) places extreme load on operational systems and consumes vast amounts of network bandwidth.

To ensure minimal impact on source applications, Fivetran heavily utilizes log-based Change Data Capture (CDC). Instead of running massive SELECT * queries against a production PostgreSQL database, Fivetran connects directly to the database’s internal Write-Ahead Log (WAL). It continuously reads the exact binary log of INSERT, UPDATE, and DELETE operations. It processes these precise, incremental modifications and applies them identically to the destination lakehouse. This ensures high-velocity data replication with near-zero performance degradation on the critical production database.

System Normalization and Idempotency

When Fivetran extracts data from a highly complex, nested API (like the Jira API or the Stripe API), it does not simply dump a massive, unreadable JSON blob into the data warehouse.

Fivetran natively normalizes the data. It dissects complex JSON arrays and flattens them into clean, highly structured relational tables linked by explicit foreign keys. Furthermore, Fivetran pipelines are strictly idempotent. If a network failure occurs during a massive sync, Fivetran simply restarts the exact same batch. Because it explicitly tracks extraction cursors and uses deterministic loading logic, it never accidentally duplicates data or corrupts the destination table upon a retry.

Summary of Technical Value

Fivetran permanently eliminated the vast majority of custom data engineering pipeline work. By providing hundreds of fully managed, highly resilient connectors capable of handling automated schema drift and complex CDC logic natively, it allows organizations to centralize their data effortlessly. It is a critical foundational component of the modern data stack, ensuring that the open data lakehouse is constantly populated with high-fidelity, highly reliable operational data.

Learn More

To learn more about the Data Lakehouse, read the book “Lakehouse for Everyone” by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.