In today’s world, digital transformation has become crucial for all types and sizes of organizations. It is no longer just a buzzword but a necessary step towards becoming impressive, customer-centric, and productive. This transition involves using technology to change the way people work, leading to improved customer and user experience, streamlined operations, and better business models for optimal outcomes.
This article was originally published in The New Stack and is reposted here with permission. Selecting the tools that best fit your IoT data and workloads at the outset will make your job easier and faster in the long run. Today, Internet of Things (IoT) data or sensor data is all around us. Industry analysts project the number of connected devices worldwide to be a total of 30.9 billion units by 2025, up from 12.7 billion units in 2021.
As the complexity of modern applications continues to increase, so too does the demand for comprehensive observability solutions. Organizations looking to enhance their applications’ performance, reliability, and scalability need powerful tools that allow them to monitor, analyze, and visualize their infrastructure. One such tool is InfluxDB 3.0, a time series database designed to handle large-scale monitoring and analytics workloads.
In modern business environments, where everything is fast-paced and data-centric, companies need to be able to track and analyze data quickly and efficiently to stay competitive. Metrics play a crucial role in this, providing valuable insights into product performance, user behavior, and system health. By tracking metrics, companies can make data-driven decisions to improve their product and grow their business.
When managing queues and services in streaming data pipelines that use technologies like Kafka and RabbitMQ, SREs and application developers often struggle to determine if these pipelines are performing as expected. Visibility into the performance of a streaming data pipeline, after all, requires visibility into every component of that pipeline.
