Remove Data Warehouse Remove Events Remove Reporting Remove Risk
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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits.

Risk 73
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Combine transactional, streaming, and third-party data on Amazon Redshift for financial services

AWS Big Data

The following are some of the key business use cases that highlight this need: Trade reporting – Since the global financial crisis of 2007–2008, regulators have increased their demands and scrutiny on regulatory reporting. Deploy the solution You can use the following AWS CloudFormation template to deploy the solution.

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Architectural Patterns for real-time analytics using Amazon Kinesis Data Streams, Part 2: AI Applications

AWS Big Data

Before we dive in, we recommend reviewing Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1 for the basic functionalities of Kinesis Data Streams. Part 1 also contains architectural examples for building real-time applications for time series data and event-sourcing microservices.

IoT 56
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Create, train, and deploy Amazon Redshift ML model integrating features from Amazon SageMaker Feature Store

AWS Big Data

Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. To get started, we need an Amazon Redshift Serverless data warehouse with the Redshift ML feature enabled and an Amazon SageMaker Studio environment with access to SageMaker Feature Store.

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Financial Intelligence vs. Business Intelligence: What’s the Difference?

Jet Global

A new paradigm in reporting and analysis is emerging. There was always a delay between the events being recorded in financial systems (for example, the purchase of a product or service) and the ability to put that information in context and draw useful conclusions from it (for example, a weekly sales report).

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Advancing Data Security With IBM Security Guardium Insights for IBM Cloud Pak for Security

CDW Research Hub

This provides near-real-time data activity monitoring and protection capabilities for Database as a Service (DBaaS) sources, such as AWS Kinesis and Azure Event Hubs. GDP is a leading data security platform for databases and data warehouses. Automated workflows and long-term data storage. Better together.

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Build a serverless analytics application with Amazon Redshift and Amazon API Gateway

AWS Big Data

Business teams can gain meaningful insights by simplifying their reporting through web applications and distributing it to a broader audience. Reporting and analysis – An application where you can trigger large analytical queries with dynamic inputs and then view or download the results. What are WebSockets and why do we need them?