Displaying 1 to 13 from 13 results

m3 - M3 monorepo - Distributed TSDB and Query Engine, Prometheus Sidecar, Metrics Platform

  •    Go

Distributed TSDB and Query Engine, Prometheus Sidecar, Metrics Aggregator, and more. (For a fully comprehsensive getting started guide, see our single node how-to).

Marketstore - DataFrame Server for Financial Timeseries Data

  •    Go

MarketStore is a database server optimized for financial timeseries data. You can think of it as an extensible DataFrame service that is accessible from anywhere in your system, at higher scalability. It is designed from the ground up to address scalability issues around handling large amounts of financial market data used in algorithmic trading backtesting, charting, and analyzing price history with data spanning many years, including tick-level for the all US equities or the exploding crypto currencies space. If you are struggling with managing lots of HDF5 files, this is perfect solution to your problem.

Griddb - High performance, High scalability and High reliability database for big data

  •    C++

GridDB is an In-Memory NoSQL Database for highly scalable IoT applications . It has a KVS (Key-Value Store)-type data model that is suitable for sensor data stored in a timeseries. It is a database that can be easily scaled-out according to the number of sensors. High Reliability It is equipped with a structure to spread out the replication of key value data among fellow nodes so that in the event of a node failure, automatic failover can be carried out in a matter of seconds by using the replication function of other nodes.

Argus - Time series monitoring and alerting platform.

  •    Java

A pom for all Argus projects. It provides standards for plugin versions, configuration and a number of other things.

Gnocchi - Time series database

  •    Python

Gnocchi is an open-source |time series| database. The problem that Gnocchi solves is the storage and indexing of |time series| data and resources at a large scale. This is useful in modern cloud platforms which are not only huge but also are dynamic and potentially multi-tenant. Gnocchi takes all of that into account. Gnocchi has been designed to handle large amounts of aggregates being stored while being performant, scalable and fault-tolerant. While doing this, the goal was to be sure to not build any hard dependency on any complex storage system.

kairosdb-client - Java Client for KairosDB

  •    Java

Java client for pushing and querying data to/from KairosDB

btrdb-python - BTrDB bindings for Python

  •    Python

These are BTrDB Bindings for Python allowing you painless and productive access to the Berkeley Tree Database (BTrDB). BTrDB is a time series database focusing on blazing speed with respect to univariate time series data at the nanosecond scale. Our goal is to make BTrDB as easy to use as possible, focusing on integration with other tools and the productivity of our users. In keeping with this we continue to add new features such as easy transformation to numpy arrays, pandas Series, etc. See the sample code below and then checkout our documentation for more in depth instructions.

timbala - Durable time-series database that's API-compatible with Prometheus.

  •    Go

Timbala is in a very early stage of development and is not yet production-ready. Please do not use it yet for any data that you care about. There are several known issues that prevent any serious use of Timbala at this stage; please see the MVP milestone for details.

iot-analytics-at-the-edge - GTM Stack: IoT Data Analytics at the Edge

  •    Python

Source code for the post, GTM Stack: IoT Data Analytics at the Edge. In the post, we explore the integration of several open-source software applications to build an IoT edge analytics stack, designed to operate on ARM-based edge nodes. We use the stack to collect, analyze, and visualize IoT data, without first shipping the data to Cloud or other external systems. See the post for complete instructions on using the source code.

tutorials.Time-Series-Data - :blue_book: FIWARE 304: Querying Time Series Data (QuantumLeap)

  •    Shell

This tutorial is an introduction to FIWARE QuantumLeap - a generic enabler which is used to persist context data into a CrateDB database. The tutorial activates the IoT sensors connected in the previous tutorial and persists measurements from those sensors into the database. To retrieve time-based aggregations of such data, users can either use QuantumLeap query API or connect directly to the CrateDB HTTP endpoint. Results are visualised on a graph or via the Grafana time series analytics tool. Previous tutorials have shown how to persist historic context data into a range of databases such as MySQL and PostgreSQL. using Apache Flume and Apache NIFI Furthermore, the Short Term Historic tutorial has introduced the STH-Comet generic enabler for persisting and querying historic context data using a MongoDB database.

summarydb - Approximate time-series database using sliding window aggregation

  •    Go

This is an implementation of SummaryStore in Golang. By using window sliding aggregations, SummaryDB achieves much lower disk usage and lower time-based range query latencies compared to other TSDBs. These benefits come at the cost of higher error bounds of the query results.

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