Today, we’re hearing from telematics solutions company Geotab about how Google BigQuery enables them to democratize data across their entire organization and reduce the complexity of their data pipelines.
Analytics and data visualizations have the power to elevate a software product, making it a powerful tool that helps each user fulfill their mission more effectively. To stand apart from the competition, today’s software applications need to deliver a lot more than just transaction processing. They must also provide insights that help drive better decisions, alert users to matters that require their attention, and deliver up-to-the-minute information about the things that matter most.
In the past, most software applications were all about “data processing.” In the parlance of old-school management information systems, that meant an almost exclusive focus on keeping accurate transactional records alongside any master data necessary to complete that mission. Transaction processing is important, of course, but in today’s world, applications are expected to deliver a lot more than that.
Information technology has been at the heart of governments around the world, enabling them to deliver vital citizen services, such as healthcare, transportation, employment, and national security. All of these functions rest on technology and share a valuable commodity: data. Data is produced and consumed in ever-increasing amounts and therefore must be protected. After all, we believe everything that we see on our computer screens to be true, don’t we?
Today's increasingly complex and unstable world, together with a continued focus from senior leaders on improving growth, use of technology and addressing workforce issues, means that many savvy organizations are looking towards Revenue Operations
Our mission at Google Cloud is to help our customers fuel data driven transformations. As a step towards this, BigQuery is removing its limit as a SQL-only interface and providing new developer extensions for workloads that require programming beyond SQL. These flexible programming extensions are all offered without the limitations of running virtual servers.
Most commonly, data teams have worked with structured data. Unstructured data, which includes images, documents, and videos, will account for up to 80 percent of data by 2025. However, organizations currently use only a small percentage of this data to derive useful insights. One of main ways to extract value from unstructured data is by applying ML to the data.
The telecommunications industry continues to develop hybrid data architectures to support data workload virtualization and cloud migration. However, while the promise of the cloud remains essential—not just for data workloads but also for network virtualisation and B2B offerings—the sheer volume and scale of data in the industry require careful management of the “journey to the cloud.”
In this post I will demonstrate how Kafka Connect is integrated in the Cloudera Data Platform (CDP), allowing users to manage and monitor their connectors in Streams Messaging Manager while also touching on security features such as role-based access control and sensitive information handling. If you are a developer moving data in or out of Kafka, an administrator, or a security expert this post is for you. But before I introduce the nitty-gritty first let’s start with the basics.
A key consideration when buying an embedded analytics solution is not only whether it supports embedding of charts and reports, but that it can integrate analytics in a way that is indistinguishable from the experience of your application. Learn what white-label BI is.
