Remove Big Data Remove Events Remove Internet of Things Remove Reporting
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Delivering Low-latency Analytics Products for Business Success

Rocket-Powered Data Science

When data science was in its “early days” within businesses, the data scientists mostly worked offline with static sources (like databases or web-based reports) to build and test analytics models for potential deployment in the enterprise.

Analytics 166
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My top learning moments at Splunk.conf23

Rocket-Powered Data Science

When I reviewed highlights from last year’s Splunk.conf22 conference in my summary report at that time, I focused a lot on the Splunk Observability Cloud and its incredible suite of Observability and Monitoring products and services. This reflected my strong interest in observability at that time. Leaders are 7.9x

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Three Types of Actionable Business Analytics Not Called Predictive or Prescriptive

Rocket-Powered Data Science

There are other dimensions of analytics that tend to focus on hindsight for business reporting and causal analysis – these are descriptive and diagnostic analytics, respectively, which are primarily reactive applications, mostly explanatory and investigatory, not necessarily actionable.

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Glossary of Digital Terminology for Career Relevance

Rocket-Powered Data Science

7) Security (airports, shopping malls, entertainment & sport events). Autonomous Vehicles: Self-driving (guided without a human), informed by data streaming from many sensors (cameras, radar, LIDAR), and makes decisions and actions based on computer vision algorithms (ML and AI models for people, things, traffic signs,…).

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Otis takes the smart elevator to new heights

CIO Business Intelligence

The last thing [our customers] want is an elevator out of service,” says Rina Leonard, vice president and CIO at Otis. “We Otis One’s cloud-native platform is built on Microsoft Azure and taps into a Snowflake data lake. based company’s elevators smarter.

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Ingest and analyze your data using Amazon OpenSearch Service with Amazon OpenSearch Ingestion

AWS Big Data

In today’s data-driven world, organizations are continually confronted with the task of managing extensive volumes of data securely and efficiently. Whether it’s customer information, sales records, or sensor data from Internet of Things (IoT) devices, the importance of handling and storing data at scale with ease of use is paramount.

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

AWS Big Data

Part 1 also contains architectural examples for building real-time applications for time series data and event-sourcing microservices. With this capability, you can use SQL (Structured Query Language) to connect to and directly ingest the data stream from Kinesis Data Streams to analyze and run complex analytical queries.

IoT 91