Top 6 Most Popular Time Series Data Softwares | May 2024

Here are the top 6 most popular time series data softwares as derived from our TpSort Score which is a continually popular score, it denotes an estimated popularity of a software.

1. Librato

Librato Librato is a cloud-based monitoring platform for devops, development and operations teams who want the flexibility to monitor the metrics and events important to their application deployment, while leaving storage, analysis and alerting to a service that can scale with their operation.Librato lets you monitor all aspects of your operation,......

2. AWS CloudWatch

AWS CloudWatch Amazon CloudWatch offers cloud monitoring services for customers of AWS resources. Collect & track metrics & react immediately to keep your businesses running smoothly.......

3. OpenTSDB

OpenTSDB OpenTSDB is a distributed, scalable Time Series Database (TSDB) written on top of HBase. OpenTSDB was written to address a common need: store, index and serve metrics collected from computer systems (network gear, operating systems, applications) at a large scale, and make this data easily accessible and graphable.......

4. Cube

Cube Cube is a system for collecting timestamped events and deriving metrics. By collecting events rather than metrics, Cube lets you compute aggregate statistics post hoc. It also enables richer analysis, such as quantiles and histograms of arbitrary event sets. Cube is built on MongoDB and available under the Apache License......

5. Apache Mahout

Apache Mahout Apache Mahout is an Apache project to produce free implementations of distributed or otherwise scalable machine learning algorithms on the Hadoop platform. Mahout is a work in progress; the number of implemented algorithms has grown quickly, but there are still various algorithms missing.While Mahout's core algorithms for clustering, classification and......

6. Reconnoiter

Reconnoiter Reconnoiter is a monitoring and trend analysis system designed to cope with large architectures (thousands of machines and hundreds of thousands of metrics).Heavy focus is placed on decoupling the various components of the system to allow for disjoint evolution of each component as issues arise or new requirements are identified.......