Category: Industries

In this post, we discuss how Phreesia used AWS DMS to replicate their on-premises database to AWS in an effective and cost-optimized manner. Because of the database’s large size and complex data structure, properly tuning the AWS DMS configuration was critical to minimize the migration duration and cost. We outline the fine-tuning techniques that were applied to optimize the AWS DMS task settings, instance size, IOPS provisioning, and table mappings. Applying these performance optimizations allowed Phreesia to develop a migration strategy to move this 30 TB database to Amazon S3 in just 2 days without disruption to production workloads.

As industrial and manufacturing companies embark on their digital transformation journey, they are looking to capture and process large volumes of near real-time data for optimizing production, reducing downtime, and improving overall efficiency. As part of this, they’re looking to store data locally at the plant floor or on-premises data center for real-time low-latency reporting […]

Schneider Electric is a leader in digital transformation in energy management and industrial automation. To effectively manage customer account hierarchies in its CRM at scale, Schneider Electric started leveraging advances in generative artificial intelligence (AI) large language models (LLMs) in April 2023. They created a solution to make timely updates to their customer account hierarchies in their CRM by linking customer account information to the correct parent company based on the latest information retrieved from the Internet and proprietary datasets. In this post, we explore further iterations of this project and how the team applied what they learned to the Salesforce CRM system using Amazon Aurora and Amazon Bedrock.

Ola Money is a financial service provided by Ola Financial Services (OFS), which is part of the Ola group of companies. In this post, we share the modernization journey of Ola Money’s MySQL workloads using Amazon Aurora, a relational database management system built for the cloud with MySQL and PostgreSQL compatibility that gives the performance and availability of commercial-grade databases at one-tenth the cost.

Financial services customers choose DynamoDB for security, resilience, performance, and scale. In this post, we discuss how Amazon DynamoDB helps financial services customers overcome these challenges for common industry use cases. We also share customer examples, such as Fidelity Investments, Experian, Moody’s, and other financial services applications that are built with DynamoDB.

Blockchain technology has the potential to revolutionize how personal digital records are managed, stored, and shared, because it offers unique features such as immutability, transparency, security, and decentralization. The application possibilities of blockchain technology in the context of personal digital records encompass various potential use cases, including but not limited to: Create decentralized digital identity […]

In this post, we discuss an automotive manufacturing information management use case where we store information about components within a vehicle as well as the hierarchy between each of the components. For our automotive use case, we use Amazon DynamoDB to deliver transactional queries, such as component attribute lookups. We will also show you how to use DynamoDB for larger responses such as a recursive query for all the components in a vehicle. While recursive object relationships can be represented in graph databases and possibly traditional RDBMS (with complex joins), these deeper queries can also be represented in DynamoDB.

The Amazon Finance Technologies (FinTech) payment transmission team manages products for the Accounts Payable (AP) team, from invoices to the pay process. Their suite of services handles the disbursement process, from invoice generation to payment creation, to make sure that payment beneficiaries receive their payments. Amazon Business makes payments to a very diverse range of […]

This is the first post in a series of posts covering digital asset tokenization in financial services, a topic which is seeing tremendous interest in the sector. The series aims to be a guide for financial services customers looking to learn more about the topic, and who may be considering building a digital asset capability for their […]

In this two-part series, we demonstrated how to use Amazon Timestream database and its built-in time series functionalities to identify the harmonic issues at scale through correlating metrics for millions of customers and automate the process for large-scale data handling. An electricity utility normally engages in electricity generation and distribution of electricity to end users, […]