Columnar tabular formats are inefficient for storing and handling the immense amount of metadata and multi-dimensional data generated across businesses such as genomics, geospatial, finance, and retail.
Don’t constrain your computations to domain-specific tools. For modern applications, forcing-fitting data into database tables and accessing that data via SQL and ODBC/JDBC is inefficient.
It is an intensive engineering feat to share data and code outside your organization, involving setting up cloud bucket policies, handling key management, and exchanging Jupyter notebooks.
Deploying scalable computations requires spinning up and maintaining clusters. Overprovisioning becomes unaffordable over time, whereas underprovisioning leads to unacceptable performance.
Is it for you?
Store any data and metadata, and access it with various APIs and tools
Share data and code with anyone and scale out compute without hassles
|Dense / Sparse Arrays & Dataframes|
|APIs (Python, R, C, C++, Java, Go)|
|Integrations (MariaDB, PrestoDB, Spark, Dask, ...)|
|Data Versioning & Time Travelling|
|Serverless UDFs and Task Graphs|
|Sharing (within and beyond organizations)|
Listen to Stavros’s podcast to understand why the industry needs a universal data engine
and how TileDB is putting “data back into data management”