# Key concepts

## Storage types

Marple DB is a highly optimised data storage solution. It mainly targets high-frequency time series data obtained with measurement files. To optimise cost and performance, it combines three types of storage in a seamless way:

1. **Hot storage** (PostgreSQL)
2. **Cold storage** (Parquet on Apache Iceberg)
3. **Archive storage** (Object storage)

<figure><img src="/files/p0WerWl99AndlRHZIb40" alt=""><figcaption></figcaption></figure>

Marple DB automatically handles loading individual signals from cold storage to hot when needed. Lifecycle rules can be used to transition from cold to archive.

## Datastream

Marple DB can consume data from different sources. A datastream allows you to configure

* The file plugin (see below)
* [Preprocessing rules](/docs/marple-db/datastreams/preprocessing-rules.md) rules
* A unified name space (UNS)
* Automations

Read more in [Datastreams](/docs/marple-db/datastreams.md)

## Plugin

Data files exist in a lot of different file types (CSV, MDF, MAT, HDF5, ...). A plugin is a software module that reads out the data & metadata for a specific file type.

Read more in [Supported file types](/docs/marple-db/datastreams/supported-file-types.md)

## Datapool

A datastream writes its data into a datapool, which defines the Postgres tables where data will be written to.

By default, all datastreams write into the same `default` datapool. Changing this is for advanced use cases.

## Discover Marple DB in 1 minute

{% embed url="<https://www.youtube.com/watch?index=27&list=PLtVuqpI9QpJAFtUEvuS23Z47XGHrRc_P9&v=m0msNW6tSHU>" %}

Want to discover other Marple features in 1 minute? Check out other [1 Minute Marple videos](/docs/other-resources/1-minute-marple-videos.md)


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