With the rapid development of financial technology, more and more investment institutions are leveraging open source big data platforms to build their own high performance investment metrics systems. ELK stack and Kafka have become popular choices. In this article, we will explore how to use ELK stack and Kafka to build a scalable, real-time investment metrics pipeline.
ELK stack overview
ELK stack consists of three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a highly scalable search and analytics engine. Logstash is a data processing pipeline for collecting, transforming and shipping logs and metrics data. Kibana provides visualization and analytics UI on top of Elasticsearch. Together they provide a powerful platform for aggregating and analyzing large volumes of data in real-time.
Why ELK stack for investment metrics
ELK stack is a great fit for building investment metrics systems. Elasticsearch provides fast search and analytics on time series data like stock prices, index levels, etc. Its scalability allows ingesting and querying large amounts of market data. Logstash enables collecting metrics and logs from different systems like trading platforms, risk engines, middle offices, etc. Kibana offers rich dashboards and visualizations for traders, quants, risk managers to monitor positions, risk exposures, P&L in real-time.
Leveraging Kafka for data transfer
A key challenge is the high throughput data transfer between metrics producers like trading systems and the ELK stack. Kafka offers a scalable, low latency message bus to buffer and stream large volumes of metrics data to ELK. Its partitioning model allows data streams to be scaled horizontally. Logstash can directly integrate with Kafka topics as data source. With Kafka, the metrics pipeline can handle much higher throughput without overwhelming Elasticsearch.
Achieving high availability
For an investment metrics system, high availability is critical. ELK and Kafka clusters can be run in containers orchestrated by Kubernetes, which provides automation around availability and scalability. Kubernetes manages failover and replication of Elasticsearch nodes. It also automatically rebalances Kafka partitions on failures. The overall system can self-heal and scale based on monitoring metrics.
ELK stack and Kafka provide a scalable, real-time platform for building investment metrics systems. Their open source nature, large user base and cloud hosted options make them easy to deploy. With thoughtful architecture and infrastructure, they can meet the data ingestion, processing and monitoring needs of most investment firms.