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Time Series.
Build faster, gain insight, reduce cost.

Build and run data-intensive analytical applications by combining the flexibility of the document model with time series collections.

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Implementing Time Series

Time series data is truly industry-agnostic. It's created across use cases, from financial services to smart manufacturing. However, it can be challenging to work with due to its enormous storage footprint, which creates further challenges for querying and analyses to extract real-time insights. In this talk, we will cover the fundamentals of time series data and its usage.

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Build time series apps faster

Simplify and accelerate app development with native time series collections that automatically handle the complexities and challenges of time series data, without the need for extra instrumentation by developers. This means faster time to market and a better developer experience.

Fully managed with Atlas
Multi-cloud data distribution
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Analytical insights, simplified

Discovering insights and patterns from your time series data is easier with the unified Query API that includes rich window functions and temporal operators for common and complex analytical queries.

Learn about the Query API

A streamlined time series experience

Seamlessly manage the entire time series data lifecycle – ingest, storage, analysis, visualization, and archive. There's no need to worry about performance or scalability since columnar storage and compression optimize for query speed and cost efficiency, even as data grows over time.

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Reduce complexity and cost

Eliminate costly, specialized databases that lead to complex data silos, data movement, and operational overhead. Instead, efficiently and securely manage both time series and operational data within a single versatile, general-purpose database.

Developer data platform
“The specialized columnar storage format and efficient data handling process large volumes of time-stamped data with speed and accuracy. This streamlines operations and enables real-time insights, helping optimize our services and improve the customer experience.”
Andrés Murcia
Chief Technology Officer, Picap
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Feature overview

Get started with
time series

Effortlessly handle large volumes of data with a cost-effective solution designed to meet the most demanding requirements of time series data.
View Documentation
Time series collections
Automatically store time series data in a specialized columnar format optimized for high storage efficiency, reduced I/O and low latency queries.
Read the docs
JSON
Window functions
Unlock insights faster with the unified and expressive Query API, leveraging Window Functions and Temporal Operators.
Learn about the MongoDB Query API
CRUD
Data densification
Handle missing or uneven data with densification and gap-filling functions. Perform analytics and ensure correctness by eliminating gaps in time or filling in missing values with methods like linear interpolation.
View aggregation pipeline operators
Shell

Deliver insights from time series data

Find out how to leverage time series data to create great application experiences.

Get the most out of Atlas

Build and run applications like IoT and financial analytics with MongoDB native time series capabilities.
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Build time series applications on MongoDB

Natively support the entire time series data lifecycle – from ingestion, storage, querying, real-time analysis, and visualization to online archiving.
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MONGODB NATIVE TIME SERIES
  • Time series collections
  • Columnar compression
  • Time series queries & analytics
  • Automated data lifecycle
  • Support for updates & deletes
  • Sharding support