MongoDB optimizes for the demands of time series workloads – streaming data ingestion, indexing, fast query processing, and compressed storage footprint. Your teams get time series apps to market faster, with less effort and at lower cost.
Time series collections, queries and analytics
Automated data lifecycle
Simplify app development
Build apps for the unique performance and scale demands of time series data. Eliminate lengthy development cycles. Quickly bring new apps to market with native time series collections that automatically optimize your schema for high storage efficiency, low latency queries, and real time analytics.
Surface insights and anticipate outcomes
Collect data at the edge and process, query, and aggregate it in place with a single, unified Query API. Scale and gain faster insights with automatic indexing, rich window functions, and data densification.
Reduce complexity and cost
Eliminate the time and complexity required to stitch together multiple technologies. Seamlessly and economically manage the entire time series data lifecycle in MongoDB, from ingestion, storage, querying and analyzing data, and visualization through to archival as data ages.
Simplify your data estate
Overcome legacy trade-offs. Eliminate specialized data stores that lead to more data silos, data movement, and operational overhead. Efficiently and securely blend time series and enterprise data within a single versatile, flexible database and use a single query API to power almost any workload.
Native time series collections
Quickly get started with a flexible schema optimized for high storage efficiency.
Low latency queries
Scale queries with automatic clustered indexes on time and secondary indexes on any metadata field.
Uncover patterns with window functions and calculate moving averages and sums over flexible time windows.
Full data lifecycle management
Support the entire lifecycle of time series data, from ingestion, storage, analysis, and visualization to data archiving.
Reduce latency and comply with data sovereignty regulations by partitioning datasets and co-locating nodes with data producers.
Dramatically reduce your database storage footprint by as much as 70% with best-in-class compression algorithms.
Gap filling and densification
Missing data points? Gap filling and densification make it easier to handle missing data.
Support for delete operations
Delete data when needed to easily comply with modern data privacy regulations.
Get started with time series
Eliminate lengthy development cycles and quickly build schemas, queries, and analytics tuned for the unique performance demands of time series workloads.