Building Real-time Data Pipelines for Capital Markets Firms

Building Real-time Data Pipelines for Capital Markets Firms

Building Real-time Data Pipelines for Capital Markets Firms. A data pipeline built from serverless components allows capital markets firms to focus on developing valuable insights and offerings. The end users' data access patterns can strongly influence the data pipeline architecture and schema design.

Editor’s note: This is the second part in our special series on working with market data in Google Cloud. This post highlights how we leveraged serverless components to build a flexible data ingestion pipeline. Check out the first post on visualizing real-time market data in the cloud.

Capital markets firms must rapidly extract insights from unbounded real-time datasets. A firm's market data pipelines should weigh the end user's data access requirements as a central design consideration, but oftentimes the rigidity of delivery mechanisms and analytical tools block this goal. Serverless data offerings can solve this problem by removing operational friction when introducing a new, well-suited tool. This makes it simple for one data pipeline to serve separate user goals—say, one for training a real-time machine learning model and another for analyzing historical data. 

With a serverless data pipeline, capital markets firms can focus on insights, not infrastructure, and stay ahead in a fast-moving industry. In this post, you’ll see best practices for real-time data ingestion using CME Group’s  Smart Stream top-of-book (ToB) JSON feed. The reference architecture considers data usage patterns and schema when selecting storage and transport options. It also encapsulates business logic in Cloud Functions to increase the speed of development and to offload operational complexities. These patterns and designs can be applied to a wide variety of use cases.

google cloud platform financial services serverless

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