Next generation performance and efficiency for your data pipelines without a big migration or new ETL. Haevek has the same developer ergonomics and integrations as Spark.
The problem
Regardless of cost and performance issues with the current infrastructure, a migration is costly and risky.
Falcon connects with existing data sources and lake houses. It operates using familiar syntax that is similar to Apache Spark. Adding it into your stack is fast and non-destructive.
Too often, the promise of a new technology came with the intrusion of throwing out a good amount of the stack that team members were familiar with.
Haevek’s Falcon platform is built differently. It operates next to solutions like Spark, Big Query, Snowflake, and Redshift without interrupting existing production processes.
As a Kubernetes-native, Falcon can be spun up anywhere Kubernetes runs. Haevek sends you a Helm chart and you are up and running on the compute infrastructure of your choice.
Your existing enterprise data systems. Your existing infrastructure. No rip and replace.
Developer ramp up does not need to stand in the way. Migrating workloads to Falcon is not too heavy of a lift.
Although using a Rust-based programming model, the syntax and logic will be very familiar to engineers who have been working with Spark.
use falcon_stdlib::prelude::*;
pub struct WordCount;
impl FalconJob for WordCount {
fn build(ctx: &JobBuildContext) -> impl Dataset {
ctx.read_prefix("s3://bucket/corpus/")
.lines()
.flat_map(|line| line.split_whitespace().map(str::to_owned).collect::<Vec<_>>())
.map(|word| (word, 1u64))
.reduce_by_key(|a, b| a + b)
.write_json("s3://bucket/wordcount-output/")
}
}Example Falcon workload, using syntax familiar to Spark engineers.
A data engineer familiar with Spark will recognize the flat_map / map / reduce_by_key pattern. Falcon’s IterDataset<item = T> aligns with Spark’s fundamental RDD[T].
Workload migration is not a massive code development project and does not require new staff. Most likely existing engineers can transform workloads for Falcon.
Why Rust
In addition to its performance advantages, the choice of Rust as Falcon’s implementation language provides a notable developer experience improvement; the necessary job compilation step means that type errors, missing dependencies, and API misuse are caught at build time rather than at job submission or execution time. For example, Spark users may run into serialization errors at runtime that Falcon would catch at compile-time.
Customer story
7 days
to a Falcon Test Flight pilot on live data
7 days
of testing
Live
Moved to production, no interruption in service
Haevek’s Falcon Platform revolutionizes data processing. Costs for infrastructure and licensing drop, often by 80 percent. Performance increases, often by 75 percent. Capabilities open up, projects evolve, new innovations come into view.
“Haevek is a very innovative capability. We don't think anyone else can do what they do.”
Chief Engineer · Raytheon
We offer the 2-4 week Falcon Test Flight for you to pilot Falcon in your environment with your data. No data moved, no lock-in, training included.