Precisely compute
only what changed.

10 to over 1,000× fresher inputs for your apps and agents, 10 to over 100× lower compute than your warehouse.

User access revoked

Feldera: Instant

Only 4 permissions updated

Batch jobs

Recalculate everything

Trusted by frontier teams at

  • Okta
  • Nubank
  • Procore
  • Zeta Global
  • Global 2000
    Enterprises
  • OSO
  • and more
Answers update instantly when input data changes.
Hours/days → seconds
Questions of any complexity stay just as fresh, at unparalleled efficiency.
Any SQL complexity
Ship fast, and get fresh answers for less than your batch jobs cost today.
Save millions

Results from customers in production, on tens of terabytes of data.

  • Intractable → milliseconds

    Agentic security. Permission checks for every user and AI agent, over deeply nested access graphs.

  • 48 hours → under 2 minutes

    Real-time pipelines. End-to-end freshness.

  • 1–3 hours → seconds

    Real-time pipelines. Gold layer latency.

Stats from one customer’s single pipeline, using under 16 GB of RAM.

Deeply nested views
100+
SELECTs
700+
Aggregate calls
500+
Subqueries
500+
JOINs
300+
Window functions
100+
GROUP BYs
90+
UNIONs
80+
CTEs (including recursion)
40+
SELECT DISTINCTs
25+
  • $2.5M → $2.4K per year

    Record so far. From a complex warehouse job running every few minutes to Feldera.

Batch compute is the wrong architecture for changing data. It leads to stale and wrong answers, huge compute bills, and millions in engineering waste for brittle workarounds.

Feldera: all you need is the SQL you already run.

Keep arbitrarily complex SQL queries instantly fresh over changing data.

Latency and compute depend on the size of the change, not the size of the data.

From the creators of DBSP, the award-winning breakthrough in incremental computation.

Agentic security infrastructure

Power fine-grained authorization for every user and AI agent. Each permission check is answered from current data, even over deeply nested access graphs.

Real-time pipelines

Gold tables update in seconds when the bronze layer changes, even with hundreds of thousands of lines of deeply nested SQL in between.

Fraud detection

Features for every card, fresh on every transaction, so models score on what just happened.

authorization.sql

DECLARE RECURSIVE VIEW group_can_read (
    group_id BIGINT NOT NULL,
    file_id BIGINT NOT NULL
);

-- A group can read a file it views, or anything inside a folder it can read
CREATE MATERIALIZED VIEW group_can_read AS
(SELECT group_id, file_id FROM group_file_viewer)
UNION ALL
(
  SELECT group_can_read.group_id, files.id AS file_id
  FROM group_can_read
  JOIN files ON group_can_read.file_id = files.parent_id
);

-- Users inherit their groups' access, unless banned
CREATE MATERIALIZED VIEW user_can_read AS
SELECT DISTINCT members.user_id, group_can_read.file_id
FROM members
JOIN group_can_read ON members.group_id = group_can_read.group_id
JOIN users ON users.id = members.user_id
WHERE NOT users.is_banned;

medallion.sql

-- Bronze: follow the raw Delta table as it changes
CREATE TABLE bronze_orders (
    order_id BIGINT NOT NULL PRIMARY KEY,
    user_id BIGINT,
    order_status VARCHAR,
    order_total DECIMAL(12, 2),
    created_at TIMESTAMP NOT NULL
) WITH ('connectors' = '[{"transport": {
    "name": "delta_table_input",
    "config": {"uri": "s3://lake/bronze_orders", "mode": "snapshot_and_follow"}}}]');

-- Gold: always current, recomputed only for orders that changed
CREATE MATERIALIZED VIEW gold_order_status_summary AS
SELECT
    order_status,
    COUNT(DISTINCT order_id) AS order_count,
    COALESCE(SUM(order_total), 0) AS total_revenue,
    COUNT(DISTINCT user_id) AS unique_customers
FROM bronze_orders
GROUP BY order_status;

fraud_features.sql

-- Spend features per card, over 1, 7 and 30 days
CREATE VIEW feature AS
SELECT
    t.cc_num,
    AVG(amt) OVER window_1_day AS avg_spend_pd,
    AVG(amt) OVER window_7_day AS avg_spend_pw,
    AVG(amt) OVER window_30_day AS avg_spend_pm,
    -- Number of transactions in the last 24 hours
    COUNT(*) OVER window_1_day AS trans_freq_24,
    amt, unix_time, zip, city_pop, is_fraud
FROM transaction AS t
JOIN demographics AS d ON t.cc_num = d.cc_num
WINDOW
  window_1_day AS (PARTITION BY t.cc_num ORDER BY unix_time
                   RANGE BETWEEN 86400 PRECEDING AND CURRENT ROW),
  window_7_day AS (PARTITION BY t.cc_num ORDER BY unix_time
                   RANGE BETWEEN 604800 PRECEDING AND CURRENT ROW),
  window_30_day AS (PARTITION BY t.cc_num ORDER BY unix_time
                    RANGE BETWEEN 2592000 PRECEDING AND CURRENT ROW);
  • Delta Lake
  • Iceberg
  • Postgres
  • Kafka
  • HTTP
  • NATS
  • Custom

Feldera

  • Delta Lake
  • Iceberg
  • Postgres
  • Kafka
  • HTTP
  • NATS
  • Custom

and moreand more

Change inRows out

    1B rows
    Batch recompute · O(data)
    1,000,000,000 rows
    Feldera · O(change)
    1,000 rows
    Feldera's work only depends on what changed.
    780K× less compute
    • “Securing AI agents at scale isn’t easy. Our customers include some of the most security-conscious organizations out there, and they expect rock-solid performance. Feldera helps us deliver on those expectations.”
      Graham NerayCEO & Co-Founder, OSO
    • “Feldera ensures we’re always working with the freshest data to power our real-time dashboards, produce new events from consolidated data, and support our analytics, all from a single pipeline per use case.”
      Kristoffer FøllesdalSolutions Architect, Eviny Fornybar
    • “Feldera’s IVM engine is best in class - it is, without any exaggeration, rock solid. It’ll blow your mind - DBSP incrementalizes ANY SQL query you throw at it - ANY query, even if you have a bazillion JOINs and a thousand GROUP BYs.”
      Raki RahmanPrincipal Software Engineer - SQL Server, Microsoft
    • “Feldera is a game changer for building the highest value real-time fraud and feature engineering pipelines. Easy to use (it’s SQL) and state of the art in performance.”
      Jim DowlingCo-Founder & CEO, Hopsworks

    Stop recomputing.

    Bring the SQL you already run. Questions? Ask us on Slack.