How to build a use case

Every Feldera use case has the same shape. Input connectors feed changes into SQL tables. SQL views compute the answers, and Feldera keeps them current on every change. Output connectors, queries or a change stream deliver the results. To build your use case, start from the example closest to it, then change the sources, the views and the outputs.

Steps

1. Run Feldera

Run Feldera locally with Docker, or use the online sandbox. Install the fda CLI to manage pipelines from a terminal, a script or an agent.

2. Declare tables and connect sources

A table declares the shape of an input. Its connectors property says where the data comes from. This table loads a Delta Lake table, then follows new versions of it:

CREATE TABLE orders (
    order_id BIGINT NOT NULL PRIMARY KEY,
    customer_id BIGINT,
    status VARCHAR,
    total DECIMAL(12, 2)
) WITH ('connectors' = '[{
    "transport": {
        "name": "delta_table_input",
        "config": {"uri": "s3://lake/orders", "mode": "snapshot_and_follow"}
    }
}]');

See all input connectors: Delta Lake, Iceberg, Kafka, PostgreSQL CDC, Debezium, S3, HTTP and more.

3. Write the logic as views

Write the answers you need as SQL views. Views can use full SQL, build on each other, and be recursive. Declare a view MATERIALIZED to query it at any time:

CREATE MATERIALIZED VIEW revenue_by_status AS
SELECT status, COUNT(*) AS orders, SUM(total) AS revenue
FROM orders
GROUP BY status;

4. Send results out

Attach output connectors to a view to write its changes to Kafka, Delta Lake, PostgreSQL, Redis and other destinations:

CREATE VIEW large_orders
WITH ('connectors' = '[{
    "transport": {
        "name": "kafka_output",
        "config": {"bootstrap.servers": "broker:9092", "topic": "large-orders"}
    },
    "format": {"name": "json"}
}]')
AS SELECT * FROM orders WHERE total > 10000;

5. Deploy and start

fda create my-pipeline program.sql
fda start my-pipeline

You can also use the web console, the Python SDK or the REST API (OpenAPI spec).

6. Read the results

Query a materialized view:

fda exec my-pipeline "SELECT * FROM revenue_by_status"

Or subscribe to a view's changes over HTTP. With send_snapshot=true, you get the current contents first, then every change:

curl -X POST 'http://localhost:8080/v0/pipelines/my-pipeline/egress/revenue_by_status?format=json&send_snapshot=true'

Pick a starting point

Each example below is a complete pipeline. Copy its SQL, then change the connectors and the views.

Use case	Sources	Views	Results	Start from
Agent context	Any mix of historical and live data	Views that the agent defines and deploys with fda	Ad-hoc queries, HTTP change stream	Agentic fraud detection
Fine-grained authorization (a graph use case)	Users, groups, objects and relationship tuples from PostgreSQL CDC or Kafka	Recursive views that resolve nested permissions	Redis, PostgreSQL or Kafka for fast lookups	Static FGA, dynamic FGA, FGA guide
Other graphs: reachability, hierarchies, transitive closure	Node and edge tables from any source	Recursive views	Any output	The FGA examples, recursive queries
Real-time medallion	Bronze Delta Lake or Iceberg tables	Silver and gold views	Delta Lake	Medallion SQL, guide
Lakehouse batch jobs	The batch job's Delta Lake, Iceberg or S3 tables	The job's SQL as views	Back to the lakehouse	Batch analytics SQL, guide
Streaming and CDC	Kafka, Debezium, PostgreSQL CDC	Views of any complexity	Kafka, PostgreSQL, Redis, HTTP	Debezium PostgreSQL, Debezium MySQL
Fraud detection	Transactions and reference data from Kafka and Delta Lake	Rolling-window features	A model, or a feature store	Fraud SQL, feature engineering SQL, guide

More complete pipelines: example SQL programs and runnable demos.

Coming from Spark SQL

The felderize skill in Feldera Agent Skills translates Spark SQL to Feldera SQL. The batch jobs guide shows a full conversion.