Rapidly changing world events, such as geopolitical shifts, environmental and societal disruptions, and COVID-19, continue to drive significant changes in both consumer and customer needs and behaviors. These changes have caused buyers to use and accept more digital channels. Buyers also now expect a more B2C-style experience in their working lives. Access to information has never been easier, yet people struggle with the volume of data.
The key to the solution is having a web app that uses BIM data from Autodesk Forge to automate the creation of an Azure Digital Twins foundational dataset. The app provides both visual and relational context to support the instantiation of a DT in the Azure Digital Twins build process.
These things are embedded with sensors, software, and other technologies to connect and exchange data with devices and systems over a wireless network such as the internet.
For those in the know, industrial IoT is already delivering significant competitive advantages as our world becomes more connected, and just as COVID-19 created a quantum leap for many businesses, accelerating their acceptance and adoption of new technologies, it also paved the way for an IoT future.
Application architectures, internal processes and personal preferences mean organizations and teams set up the CI/CD pipeline differently, resulting in a diversity of infrastructure and tooling throughout software delivery pipelines.
Speedscale, the API test-automation software company, today launches Speedscale CLI, a free observability tool that inspects detects, and maps API calls on local applications or containers.
In recent years, the demand for business users to be able to consume, transform, model, and visualize large amounts of complex data from multiple heterogeneous sources has increased dramatically. To meet this demand in a cost-effective, scalable way, many large companies have benefitted from moving to cloud-based data platforms.
To show you how easy and quick it is to get started on AWS, we provide a one-click deployment for an extensible trading backtesting solution that uses Kinesis long-term retention for streaming data.
Businesses collect more and more data every day to drive processes like decision-making, reporting, and machine learning (ML). Before cleaning and transforming your data, you need to determine whether it’s fit for use. Incorrect, missing, or malformed data can have large impacts on downstream analytics and ML processes. Performing data quality checks helps identify issues earlier in your workflow so you can resolve them faster. Additionally, doing these checks using an event-based architecture helps you reduce manual touchpoints and scale with growing amounts of data.
With AWS Glue DataBrew, you can now easily transform and prepare datasets from Amazon Simple Storage Service (Amazon S3), an Amazon Redshift data warehouse, Amazon Aurora, and other Amazon Relational Database Service (Amazon RDS) databases and upload them into Amazon S3 to visualize the transformed data in a dashboard using Amazon QuickSight or other business intelligence (BI) tools like Tableau.
All raw data, plus the derived anomalies and failure patterns, are then ingested from Apache Flink to Amazon Timestream for further use in near real-time dashboards.
As we started architecting the deployment of multiple clusters to support our customers’ data residency requirements, we determined we also needed to explore approaches to reduce the total operational maintenance and costs of our multi-cluster environment.
Data replication across the AWS network can happen quickly, but we are still limited by the speed of light. For this reason, data consistency must be considered when building a multi-Region application. Genera
Azure Virtual Desktop service architecture is similar to Windows Server Remote Desktop Services. Microsoft manages the infrastructure and brokering components, while enterprise customers manage their own desktop host virtual machines (VMs), data, and clients.
