Our team led Cloud Strategy and hand-on-solution architecture at McGraw-Hill Financial circa 2011 delivering critical production applications on AWS. Our seasoned team has over eighty years combined experience in financial markets.

We began early based on our belief that in the Cloud information would increase in value, that by designing applications to run on public infrastructure,
compute intensive tasks could effectively shrink time while keeping costs constant, that infinite storage would result in incredible new opportunities to process and gain insight into data.

Hire a Winning Team

Our core team is forged from working together closely in the early days of Cloud. We enjoy working together. By hiring us, you’re able to tap the experience and success of people that never answer the phone or respond to email from recruiters.

Our new hires we’re able to recruit based on our reputation as leaders in the industry, and our robust network of the world’s most talented AWS architects and developers.

Domain Expertise
We understand risk, derivatives, financial markets data. We’ve developed AWS Lambda and API integrations with market data providers. A member of our team even figured out how to transcompile QuantLib to Node.js. Over the years we’ve worked with AWS on key product innovations based on requirements for our customer projects.

We understand what our customers need, and work closely with you to achieve the highest business impact.

Builders
In the Cloud the real business value can be derived from you don’t build. Architecture and API level master of AWS Services means we can build an End-to-End DevOps Pipeline that reduces the time Data Scientists spend wrangling with code and data. Businesses gain the agility and rapid time-to-market, the competitive advantage they hire us to deliver. In financial services New York firms hire us for our financial services domain expertise, and for our deep perspective on what works and how to get results fast.

Please read some of our public case studies and we encourage you to ask questions.

Get Results

  • Connector.

    Competitive Advantage

    Gain competitive advantages by transforming raw data into valuable information that helps the business

  • Connector.

    Adopt Advanced Technology

    Evaluate the tools, techniques, and technologies required to work with data at terabyte/petabyte scale

  • Connector.

    Cost Savings

    Reduce costs of analyzing massive volumes of heterogeneous data types

Know the Terrain



Choose an AWS Partner with Experience in finance — like Bronze Drum

  • Connector.

    Advanced Capabilities

    Architect a Data Lake on AWS

  • Connector.

    Rapid Delivery

    We deliver a 1000 node deep learning platform in less than six weeks.

  • Connector.

    Guaranteed Results

    We commit to your success, and your satisfaction and business results drive our success.

Our Services

We work exclusively on AWS and no other Cloud Service Provider. Our deep AWS platform expertise translates into well-architected solutions optimized for security, cost, availability, and rapid-time to market. We build solutions that perform well, but also increase the tempo of work within your organization. We reduce friction, automate, and design in such as way as to make your business work better.

On AWS we maximize the value of Advanced Technologies like DynamoDB, RedShift Spectrum, AWS Lambda, Kinesis not simply as infrastructure components, but rather as services integrated into a total customer solution. We serve capital markets firms by innovating with our customer to yield competitive advantage and immediate business value from the Advanced Technologies available on AWS.

Timely analysis at scale translates into concrete benefits because your core value creation process accelerates even as the number of opportunities increase by a significant multiplier
While capital markets move in real-time, a great deal of data and analysis within firms can be accelerated by thinking of data as a continuous stream of processing. Further, this concept of event driven, continuous processing can accelerate each of the activities, while amplifying the amount of information analyzed, as well as the diversity and quality of such information. Timely analysis at scale translates into concrete benefits because your core value creation process accelerates even as the number of opportunities increase by a significant multiplier. Deep Learning, for example, requires advanced capabilities running clusters, but also and end-to-end process from code to training to model deployment. To remain competitive we recommend firms begin the transformation from batch to real-time, event driven analysis. By reducing time-to-insight across a much greater number of opportunities, you can do what you do successfully, and do it at a greater scale, with lower risk.

Cloud technologies will alter the way firms ingest, process and analyze, and access data within firms. We focus on on-boarding, governance, data ingestion, transformation, and custom applications built on AWS services such as WorkDocs, Glue, Kinesis, Lambda, API Gateway.

Popular Services Include
Batch to Real-time business transformation
Monolithic to Microservices
Serverless Data Acquisition and Integration from firms like Bloomberg, Xignite
Code to 1000-node Deep Learning Container Solutions
Simplify fund and market data acquisition for both data center and Cloud operations


  • READ MORE
    Developing AWS Lambda Functions for Analyzing Market Data
    2015/02/19
    Case Study, Strategy
    [I wrote this in 2015 as we worked with a hedge fund to leverage...
  • READ MORE
    Case Study: Carbon09 Voice Trading for Amazon Alexa
    2016/09/30
    Case Study, Internet of Things
    AboutBronze Drum Consulting is an AWS Advanced Consulting Partner. Tradier is a brokerage API...
  • READ MORE
    eClectic for Artivia
    2016/09/30
    Case Study, Internet of Things
    AboutArtivia, LLC began by building a simple application to manage the works. A prolific...
  • READ MORE
    Fidelity Life Associates Next Generation Platform
    2016/10/07
    Case Study, Financial Markets
    Building on AWS Bronze Drum and Fidelity Life Associates enabled rapid provisioning of the...
  • READ MORE
    JD Power Next Gen Auto Finance Analytics
    2016/10/07
    Case Study, Financial Markets
    AboutJ.D. Power and Associates amplifies the voice of the consumer, and help brands improve...
  • READ MORE
    Assured Guaranty High Performance MatLab Risk Modeling on AWS
    2016/10/28
    Case Study, Financial Markets
    AboutAssured Guaranty insures municipal bonds against default and helps investors to mitigate risk and...
  • READ MORE
    .NET Lambda for Capital Markets: Acquiring and Processing Bloomberg Data for a Customer's Greenfield Deep Learning Platform 
    2017/06/17
    Big Data, Case Study, Lambda
    The recent option to run .NET in an AWS Lambda functions enables Cloud Native...

Demand Advanced Capabilities

Strategy

Real-time data from a multitude of diverse data source
Real-time adaptive systems and models aka Deep Learning
Accept no limits
Scale infinitely

Real-time data from a multitude of diverse data source
Real-time adaptive systems and models aka Deep Learning
Accept no limits
Scale infinitely

Transform from Batch to Real-Time Processing


run


Select Customers

Ask Questions

Your Question Here

How do we acquire and manage Financial Market Data in the Cloud?

  • Data acquisition via APIs
  • Direct Connect and Market Data feeds on AWS
  • Create and buffer custom data streams from “firehose” data feeds
  • How do we ingest and process and aggregate real-time on-premises data streams?
Design and build real-time, event driven solution from diverse sets of customer, public, and subscriber data sets.

Tweetworthy Events

 

Applications


C++ to Microservices. While much of your code base may be written in C++, did you know it can run on AWS as Serverless Lambda Functions? Learn how we can take a micro services approach to decomposing existing software and enabling parallel execution in the Cloud.

This example runs as an AWS Node.js Lambda function, Python function, or as native C++ code. Our work enables these function to run on any device in any architecture.

Bonds
run
Fitted Bond Curve
run
Swap Valuation
run
For Monte Carlo simulations that drive much of the pricing of instruments, GPU combined with a scale-out architecture creates an undeniable value processing. Process workloads faster, and then tear-down the cluster.