How Fivetran, dbt, and Databricks Lakeflow work together

At a glance, Databricks Lakeflow appears to directly compete with Fivetran and dbt in data integration and transformation, respectively. In reality, Lakeflow offers powerful complementary capabilities to both — used alongside Fivetran to ingest and process different sources into the same Databricks environment, and dbt to process raw streaming data and end-to-end orchestration.

Together, Fivetran, dbt, and Databricks Lakeflow form the basis for a modern, open data platform, enabling all operational and analytical uses of data. Use Fivetran to bring enterprise data into Databricks, dbt to turn data into trusted business models, and Databricks Lakeflow to ingest and process event streams through custom pipelines and coordinate downstream work.

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What each piece brings to the table

Fivetran provides managed data integration across a broad range of applications and databases. Prebuilt connectors for over 770 distinct data sources automate data integration for general analytical and operational purposes, while providing advanced functionality such as incremental synchronization and schema migration. Fivetran delivers data into Delta Lake and other open table formats, where downstream workloads can access it.

dbt lets analytics teams define transformations as code, with tests, version control, and documentation. Teams can preserve established models and extend them as new data arrives. Fivetran additionally provides prebuilt dbt-compatible data models for supported sources (check each package for Databricks compatibility).

Lakeflow offers ingestion, transformation, and orchestration. Lakeflow pipelines, built on Spark Declarative Pipelines, support batch and streaming processing in SQL and Python. Lakeflow Jobs coordinates downstream tasks, including dbt projects.

A company might choose Fivetran for its SaaS and database connections, Lakeflow for event ingestion and processing, and dbt for business modeling. 

A practical example using Fivetran, dbt, and Lakeflow

Suppose you need commercial context and behavioral signals in one place to build a real-time account health table. Here is how you could build a stack:

1. Batch SaaS and database data integration by Fivetran: Fivetran replicates Salesforce accounts, billing records, support tickets, and application-database tables into Databricks. These datasets establish who the customers are, what they pay for, and which product workspaces belong to them. Fivetran manages the supported connections as the source systems change.
   
2. Streaming product data integration by Lakeflow: A Lakeflow pipeline ingests product events from a message bus such as Kafka. These events record logins, feature usage, and failed transactions. The engineering team writes processing logic to remove duplicates, handle late arrivals, and calculate account-level usage over relevant time windows. This stream arrives alongside the business data Fivetran supplies.
   
3. Transformations by dbt: The analytics team uses dbt to build account, subscription, and support models. It defines recurring revenue consistently, relates tickets to accounts, and maintains the mapping between product workspaces and commercial customers. Tests identify missing account identifiers or unexpected duplicate subscriptions. Supported Fivetran packages can provide a starting point, with company-specific logic added as needed.
   
4. Orchestration with Lakeflow: Lakeflow then combines the processed usage signals with transformed business models in a downstream account health table. The team can define an initial rule that flags accounts with declining activity, growing support demand, and an approaching renewal date. Lakeflow Jobs can coordinate the downstream pipeline and dbt tasks.

With this stack, your data team builds an account health table that updates in real time via streaming, suitable for decision support, business process automation, and AI applications.

Why Fivetran, dbt, and Lakeflow, in particular?

The case for a tech stack built specifically from Fivetran, dbt, and Databricks rests on how each technology serves three respective needs: varied business sources, a data team that relies heavily on SQL, and custom data engineering.

Fivetran is especially suitable for companies using many SaaS applications and databases, allowing data teams to outsource connector building and maintenance while ensuring key capabilities like change data capture, historical loads, schema change support, and recovery. Fivetran makes ongoing integration a managed service, freeing engineers to work on account matching, useful signals, and other business-specific problems.

dbt perfectly complements teams that already transform raw data into usable models using SQL, tests, and documentation. Keeping those assets lets the team extend established business definitions into new use cases.

Databricks is a strong fit for engineering-intensive workloads involving machine learning, artificial intelligence, and unstructured data processing. Lakeflow lets engineers develop batch and streaming pipelines in the same environment where business data lands. 

Start with a concrete data product, such as the account health example from earlier. By working through your data sources, data freshness requirements, ingestion, and transformations front to back, you will have a practical benchmark to evaluate how Fivetran, dbt, and Lakeflow can work together for you.

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