SQL Materialized Views
The Cloud Operational Data Store Built for
Fraud Detection
Materialize is a cloud data warehouse built with streaming internals. It let's you deploy real-time fraud detection with the team, SQL, and models you already have.
Loan eligibility rule calculation time decreased by 98.15%, dropping from 27 seconds to half a second.
Wolf Randall Director, Data Products, Vontive
Fraud Detection with Materialize
User-facing notifications
Want to know when fraud is happening? Materialize's unique real-time notifications make it easy to subscribe via SQL and ensure everyone knows when there is a problem.
Fraud and anomaly detection
Materialize enables you to use the SQL and models you already have to implement real-time fraud detection at scale. It's cloud architecture enables you to affordably run fraud detection in real-time, all the time.
Risk Modeling
Instantly analyze a series of transactions and model their likelihood to be fraudulent. Materialize can connect to real-time data in Kafka or Postgres so you can put the models you create to work at millisecond latency.
Monitoring and maintenance
If you are going to analyze transactions for fraud, you also need to ensure your pipeline of data is intact and operational. With Materialize, you can instantly flag broken pipelines and ensure you have all the data to detect fraud.
Implement Real-time Fraud Detection
Implementing real-time fraud detection can appear to be a daunting task. With numerous data sources and complicated models to run, your project plan can start to get overwhelming. Luckily, we've created a few resources to help you and your organization get started on the right path with fraud detection. Take a look today and see how easy it can be to get started with Materialize.
Fraud Detection with Ease, Scale, and Trust
Deploy real-time fraud detection with the team you already have.
Traditional Warehouses: Too Slow
Cloud Warehouses are easy to use, but get expensive when run continuously, and hit hard limits on latency and concurrency.
Stream Processors: Too Complicated
Stream Processors are fast, but they're a low-level tool, using them has high engineering costs, creates complex architectures.
Materialize packages the speed of stream processors into a cloud data warehouse
Use the same SQL workflows from traditional warehouses but get results
updated continuously to power customer-facing use-cases in production.
Streaming Engine
Work is done at the moment of data arrival, rather than query time, so that maintained results are available almost instantly.
Event-Driven Primitives
Sink data out of Materialize to Kafka, or Subscribe to updates via any PostgreSQL driver.
Access via standard SQL
Incrementally Maintained Views
Write complex SQL transformations as materialized views that efficiently update themselves as inputs change.
Learn MoreBuilt for JOINs
Multi-way, complex join support, even across databases - all in standard SQL.
Learn MoreSliding Windows
Write queries that filter to a window of time anchored to the present, Materialize will update results as time advances.
Learn MoreSQL Subscriptions
Write alerts as SQL queries with filters and subscribe to new rows as they appear.
Learn MoreCREATE MATERIALIZED VIEW my_view AS
SELECT userid, COUNT(api.id), COUNT(pageviews.id)
FROM users
JOIN pageviews on users.id = pageviews.userid
JOIN api ON users.id = api.userId
GROUP BY userid;| userID | api_calls | pageviews |
|---|---|---|
| VPLaKV | 400 | 20 |
| MN37Mt | 60 | 9 |
| 1fT4KY | 72 | 42 |
| sT4QY | 10 | 342 |
Incrementally Maintained Views
Write complex SQL transformations as materialized views that efficiently update themselves as inputs change.
Learn MoreCREATE MATERIALIZED VIEW my_view AS
SELECT userid, COUNT(api.id), COUNT(pageviews.id)
FROM users
JOIN pageviews on users.id = pageviews.userid
JOIN api ON users.id = api.userId
GROUP BY userid;| userID | api_calls | pageviews |
|---|---|---|
| VPLaKV | 400 | 20 |
| MN37Mt | 60 | 9 |
| 1fT4KY | 72 | 42 |
| sT4QY | 10 | 342 |
Built for JOINs
Multi-way, complex join support, even across databases - all in standard SQL.
Learn MoreSELECT DISTINCT ON (auctions.id)
bids.amount,
auctions.item,
auctions.seller
FROM auctions, bids
WHERE auctions.id = bids.auction_id
ORDER BY auctions.id,
bids.amount DESC,
bids.buyer;
| amount | item | seller |
|---|
Sliding Windows
Write queries that filter to a window of time anchored to the present, Materialize will update results as time advances.
Learn MoreCREATE MATERIALIZED VIEW my_window AS
SELECT date_trunc('minute', received_at),
COUNT(*) as order_ct, SUM(amount) as revenue
FROM orders
WHERE mz_now() < received_at + interval '5 minutes'
GROUP BY 1;| minute | order_ct | revenue |
|---|
SQL Subscriptions
Write alerts as SQL queries with filters and subscribe to new rows as they appear.
Learn MoreSELECT userID, email, MAX(orders.id) as last_order
FROM users
JOIN orders ON orders.userID = users.id
GROUP BY userId, email
-- Use a filter to surface users with a high % of fraud
HAVING SUM(is_fraud) / COUNT(orders.id)::FLOAT > 0.5;| userID | last_order | |
|---|---|---|
| REOtIb | [email protected] | 13/12/2022 |
| Y5KBE8 | [email protected] | 9/12/2022 |
| Wj7JQ0 | [email protected] | 13/12/2022 |
| tPCQ0 | [email protected] | 13/11/2022 |
Presents as PostgreSQL
Manage and query Materialize using any Postgres driver or tool.
Built for JOINs
Multi-way, complex join support across real-time streams - all in standard SQL.
Active Replication
Use replication to increase availability, reduce downtime, scale seamlessly.
Low-Latency Serving Layer
Results can be maintained in memory, making read latency similar to Redis.
Secure and Compliant
SOC 2 Type 2 compliant, encrypted at rest, secure connectivity to your infra.
The Warehouse-Native Approach
to Fraud Detection.
You don't need to compromise on speed, data quality, control, or simplicity.
“Our data warehouse alerts run too slowly”
Data warehouses power many user-facing notifications and data models - but can only work in batches. Materialize incrementally maintains the results of SQL queries in real-time so alerts never run off of old data.
“Creating and updating notification logic is a hassle”
Hard-coded notification logic requires a ton of effort to update and maintain as business requirements shift. Materialize allows you to adjust and test using standard SQL, saving time both in the short and long term.
“We don’t have the data engineers for real-time alerts”
Anyone who knows standard SQL - including full-stack engineers, data scientists, or PMs - can build notification logic within Materialize, eliminating the need for long back-and-forth review cycles with data engineering.
“We can’t risk automation if our system gives bad data”
Don’t risk weird failure cases caused by eventual consistency. All results from Materialize reflect correct answers, meaning alerts and automated processes are never falsely triggered by late-arriving data.
“We use other systems for our notifications”
Materialize is postgres wire-compatible and acts like a standard postgres database. Keep your existing alerting and notification systems - but power them with real-time data.
“We need to run alerts at massive scale”
Materialize is designed for scale, and is powered by a stack of stream processors - Timely Dataflow and Differential Dataflow - that have been battle-tested by Fortune 100 companies in global deployments.
More Use Cases
Automation and Alerting
→Eliminate manual work, remove delays, move faster by actioning automatically on your data.
Real-Time & User-Facing Analytics
→Dashboards and data products need to be reactive to up-to-the-minute changes in your business.
Segmentation and Personalization
→Value of personalization, recommendations, dynamic pricing increases as latency of data aggregations approaches zero.
ML in Production
→Online feature stores need continually updated data, operators need to monitor and react to changes in ML effectiveness.
