The Streaming Data Engine for Killer Applications and Analytics
Use Continuous SQL to materialize data with durable REST data APIs for real-time analytics, ETL, data science, and streaming application development.
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A podcast about all things streaming data
Connect to Kafka topics, and define schemas
Easily connect to Kafka clusters including Confluent Cloud, AWS MSK, or your own self-managed Kafka. Automatically detect and define schemas for your JSON or AVRO topics. Build input transformations to normalize messy data or foreign data sources.
Build and deploy jobs using Continuous SQL
Define and run stream processing jobs using ANSI standard SQL for streaming ETL, for filtering and joining for Machine Learning models, or for aggregating data for real-time dashboards.
Define Materialized Views on streams
Create a view of the current state of the data for easy consumption in your applications, configure maintenance policies, job restart strategies and more. Data is fully indexed and storage is automatically managed.
Query Materialized Views via RESTful endpoints
Create multiple RESTful endpoints to query materialized views by any column, define range scans, or pass in parameters. Creating dashboards, maps, and using data in notebooks has never been easier.
Get Started By Selecting Your Cloud Provider
Streaming data is core to our business. Eventador SQLStreamBuilder gave us the capability to ingest complicated feeds at massive scale and perform production-quality, continuous SQL jobs against them. This was something other competitors just couldn’t achieve—and it’s a complete game-changer for us.
Chris Ferraro, CTO and VP of Engineering
Learn how the Eventador Platform is a time, money and sanity saver for data engineering teams
Learn how the Eventador Platform enables extreme performance for real-time analytics without ETL.
Build Entire Streaming Pipelines Using Continuous SQL
Real-time Fraud & Anomaly Detection
|-- production fraud and alerting job|
|PARTITION BY card|
|ORDER BY eventTimestamp|
|F.amount AS first_amount,|
|E.amount AS last_amount|
|ONE ROW PER MATCH|
|AFTER MATCH SKIP PAST LAST ROW|
|PATTERN (F+ E) -- match 1 or more rows|
|F AS F.amount IS NOT NULL AND F.amount > 10, -- lower boundary|
|E AS E.amount IS NOT NULL AND F.amount < E.amount) -- starting value less than ending value|
|-- detect multiple auths in a short window and|
|-- send to lock account topic/microservice|
|MAX(amount) as theamount,|
|TUMBLE_END(eventTimestamp, interval '5' minute) as ts|
|WHERE lat IS NOT NULL|
|AND lon IS NOT NULL|
|GROUP BY card, TUMBLE(eventTimestamp, interval '5' minute)|
|HAVING COUNT(*) > 4 -- >4==fraud|
|-- production IoT application job|
|-- display range remaining every 15 seconds|
|tumble_end(eventTimestamp, interval '15' second) as TS,|
|CAST(ROUND(MIN(CAST(battery_level as numeric)),2) AS varchar)||'%' as state_of_charge,|
|MIN(CAST(trip_distance AS numeric)) AS distance_covered,|
|100-MIN(CAST(battery_level as numeric)) AS battery_pct_used,|
|MIN(CAST(trip_distance AS numeric))/(100-MIN(CAST(battery_level as numeric))) AS foot_per_battery_pct,|
|MIN(CAST(battery_level as numeric))*(MIN(CAST(trip_distance AS numeric))/(100-MIN(CAST(battery_level as numeric)))) as range_in_feet|
|GROUP by boardid, tumble(eventTimestamp, interval '15' second)|
|-- union two different virtual tables|
|SELECT * FROM clickstream|
|WHERE useragent = 'Chrome/62.0.3202.84 Mobile Safari/537.36'|
|SELECT * FROM clickstream|
|WHERE useragent = 'Version/4.0 Chrome/58.0.3029.83 Mobile Safari/537.36'|
The SQLStreamBuilder Continuous SQL Engine
The Eventador Platform’s SQLStreamBuilder enables you to continuously perform SQL on streams of data upstream of any database for massive performance and cost advantages.
The Fully Managed Runtime for Apache Flink
Whether you want to use SQL, Java, Scala or a combination of the three, the Eventador Platform’s Runtime for Apache Flink enables you to manage all your Flink jobs in a single place, while giving you robust team management and reporting features for a truly enterprise-grade streaming system.