Some Atoti features require large additional libraries and might not be useful in every projects. To keep the core library as light as possible, these features are packaged into separate plugins that can be installed when needed.
Plugin to load real time Kafka streams into Atoti tables.
Plugin to load the results of SQL queries into Atoti tables.
Plugin to load CSV and Parquet files from AWS S3 into Atoti tables.
Plugin to load CSV and parquet files from Azure Blob Storage into Atoti tables.
Plugin to load CSV and parquet files from Google Cloud Storage into Atoti tables.
These connectors open tens of HTTP connections to the cloud storage in order to transfer the data in parallel. They then transparently reassemble the blocks directly in memory. They can load up to 300 GB in about 5 minutes. Some parameters can impact the overall download speed:
Bandwidth of the network interface.
Speed of the CPU cores since HTTPS connections and client side-encryption consume CPU resources.
File size: small files will not have good download speed (< 60 MB/s).
Type (hot/cold) of the storage: hot storage is faster.
Data locality: best when the host running Atoti and the data are in the same cloud region.
See how to Use DirectQuery.
Plugin to use DirectQuery on Google BigQuery.
Plugin to use DirectQuery on ClickHouse.
Plugin to use DirectQuery with Databricks.
Plugin to use DirectQuery with Microsoft SQL Server.
Plugin to use DirectQuery on Amazon Redshift.
Plugin to use DirectQuery on Snowflake.
Plugin to use DirectQuery on Azure Synapse Analytics.
Plugin to create interactive Atoti widgets in JupyterLab.
A plugin can be installed as a Python package or as a Conda package.
For instance, to install the JupyterLab plugin:
pip install atoti-jupyterlab
Multiple plugins can be installed with the “extras” syntax:
pip install atoti[jupyterlab,sql]
conda install atoti-jupyterlab