Amazon Batch Use cases

Financial Services organizations, from fintech startups to longstanding enterprises, have been utilizing batch processing in areas such as high performance computing for risk management, end-of-day trade processing, and fraud surveillance. You can use Amazon Batch to minimize human error, increase speed and accuracy, and reduce costs with automation, so that you can refocus on evolving the business.
High performance computing

The Financial Services industry has advanced the use of high performance computing in areas such as pricing, market positions, and risk management. By taking these compute-intensive workloads onto Amazon Web Services, organizations have increased speed, scalability, and cost-savings. With Amazon Batch, organizations can automate the resourcing and scheduling these jobs to save costs and accelerate decision-making and go-to-market speeds.

Trading desks are constantly looking for opportunities to improve their positions by analyzing the day’s transaction costs, execution reporting, and market performance, among other areas. All of this requires require batch processing of large data sets from multiple sources after the trading day closes. Amazon Batch enables the automation of these workloads so that you can understand the pertinent risk going into the next day’s trading cycle and make better decisions based on data.

Fraud is an ongoing concern impacting all industries, especially Financial Services. Amazon Machine Learning enables more intelligent ways to analyze data using algorithms and models to combat this challenge. When used in conjunction with Amazon Batch, organizations can automate the data processing or analysis required to detect irregular patterns in your data that could be an indicator of fraudulent activity such as money laundering and payments fraud.

The scientific insight that allows Biopharmaceutical and Genomics companies to bring products to market demand high performance computing environments. Amazon Batch can be applied throughout your organization in applications such as computational chemistry, clinical modelling, molecular dynamics, and genomic sequencing testing and analysis.

Amazon Batch allows research scientists involved in drug discovery to more efficiently and rapidly search libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme. By doing this, scientists can capture better data to begin drug design and have a deeper understanding for the role of a particular biochemical process, which could potentially lead to the development of more efficacious drugs and therapies.

After bioinformaticians complete their primary analysis of a genomic sequence to produce the raw files, they can use Amazon Batch to complete their secondary analysis. With Amazon Batch, customers can simplify and automate the assembly of the raw DNA reads into a complete genomic sequence by comparing the multiple overlapping reads and the reference sequence, as well as potentially reduce data errors caused by incorrect alignment between the reference and the sample.

Media and Entertainment companies require highly scalable batch computing resources to enable accelerated and automated processing of data as well as the compilation and processing of files, graphics, and visual effects for high-resolution video content. Use Amazon Batch to accelerate content creation, dynamically scale media packaging, and automate asynchronous media supply chain workflows.

Amazon Batch provides content producers and post-production houses with tools to automate content rendering workloads and reduces the need for human intervention due to execution dependencies or resource scheduling. This includes the scaling of compute cores in a render farm, utilizing Spot Instances, and coordinating the execution of disparate steps in the process.

Amazon Batch accelerates batch and file based transcoding workloads by automating workflows, overcoming resource bottlenecks, and reducing the number of manual processes by scheduling and monitoring the executing of asynchronous processes, then triggering conditional responses to scale resources for a given workload when necessary.

Amazon Batch simplifies complex media supply chain workflows by coordinating the execution of disparate and dependent jobs at different stages of processing, and supports a common framework for managing content preparation for different contributors to the media supply chain.