Are you interested in performing time series forecasting or anomaly detection, but you don’t know where to start? If so, you’re not alone. There is an overwhelming variety of libraries, algorithms, and workflow recommendations for these tasks. As a Developer Advocate at InfluxDB, the leading time series database, I’ve researched time series data science methodologies and best practices for forecasting and anomaly detection.
In an expanded and commercial industry space new and innovative opportunities are presented to retail organizations from several novel sources of data, log files, and from transaction information to sensor data to achieve unmatched value and combative advantage in their respective fields. In order to attain customer satisfaction and to flourish their business, people across the organization must be empowered, so that swift and accurate decisions can be attained.
Indices are an important part of Elasticsearch. Each index keeps your data sets separated and organized, giving you the flexibility to treat each set differently, as well as make it simple to manage data through its lifecycle. And Elastic makes it easy to take full advantage of indices by offering ingest methods and management tools to simplify the process.
With InfluxDB you can use Tasks to process data on a schedule. You can also use tasks to write custom alerts. However, sometimes your task will fail. In this TLDR, we’ll learn how to debug your task with the InfluxDB UI and the InfluxDB CLI.
If you’re familiar with Telegraf, you know that you can easily configure this lightweight collection agent with a single TOML configuration file to gather metrics from over 180 inputs and write data to a wide variety of different outputs and/or platforms. You might also know that Telegraf can act as a processor, aggregator, parser, and serializer.
The telecom industry is in the midst of a massive shift to new service offerings enabled by 5G and edge computing technologies. With this digital transformation, networks and network services are becoming increasingly complex: RAN, Core and Transport are only a few of the network’s many layers and integrated components. Today’s telecom engineers are expected to handle, manage, optimize, monitor and troubleshoot multi-technology and multi-vendor networks.
An abundance of information can be daunting for any company. If internal teams do not know where the data is, it might hamper their efficiency at the cost of data quality and cleanliness. From a cost-effectiveness viewpoint, organizations are likely to waste excessively by hanging on to redundant data or storing varied data in one location irrespective of their sensitivity level.
5G is in the process of transforming communications technology, enabling never-before-seen data transfer speeds and high-performance remote computing capabilities.
