The best way to find out if Flink is right for you is to try it out. As one of our clients says, “Less supposition, more data.” We’re experts in Flink-processing for the financial industry. Our recent work includes large-scale, near-time equity order processing analytics, real-time trade compliance checks, and real-time position aggregations that join multiple trade-order and market-data streams.

We can work together to define and execute a quick Proof of Value that demonstrates the impact of building a stream-based solution. We’ll tailor the design to address the exact pain points you need resolved: we won’t deploy a canned word-counting example. We do this in a dedicated AWS account with mock data, so you can get answers quickly.

We’ll help you design a robust production architecture that:

• Ensures scalability and resiliency
• Considers the business-domain boundaries of your pipelines. This enables the creation of Flink pipelines that act as microservices that can be developed and evolve independently
• Enables easy traceability and operationalization of the system in production

Once you have an architecture, we’re here to help implement it. We have a number of accelerators that can help you quickly spin up a Flink environment in AWS and connect to sources like CSV, Parquet, and Kafka to start consuming data. For an enterprise-level deployment of the solution, we also provide:

• Environments-as-a-Service (EaaS): Create “flavors” of deployment topologies for different use cases and deploy them in an automated fashion (e.g., small development environments, load-testing environments, production, etc.).
• Monitoring: We provide Grafana dashboards to expose the systems performance of the hosts as well as individual pipelines and their custom metrics.
• Data Provenance: We can help you trace back a piece of data to the various transformations that have created it in upstream pipelines. This way, you can always see what pieces of market data and trade data resulted in a specific position at a point in time.
• Replay: In complex, distributed systems, the hardest thing about fixing a bug is reproducing it. We can setup the ability to replay a specific time-interval that happened in production into a separate cluster for analysis and debugging. This way, you’re able to replicate a bug as well as ensure that you’ve fixed it.
• Automated Resilience Testing: The only way to find out if your implementation is truly resilient is to test it. We have a suite of customizable, automated failure tests that emulate infrastructure failures and bring down various pipeline components so you can validate the impact on your overall system.