Discover fasterA database for data scientists

Data Science transformed

Files made obsolete

  • Efficiently store all data as multi-dimensional arrays
  • Keep your metadata with the data
  • Push all update and partitioning logic to storage

Sharing made simple

  • Share data with anyone in seconds
  • Define access policies with array semantics
  • Monitor all activity via logging

Processing made serverless

  • Perform serverless SQL and Python UDFs
  • Experience tremendous scale from your laptop
  • Pay as you go to reduce your costs

Genomics made practical

  • Store population variant data with linear scalability
  • Reduce storage to 40% over sample BCFs
  • Take advantage of the Data Science ecosystem

Geospatial made cloud-ready

  • Store point clouds & gridded data in a single cloud format
  • Analyze your data with popular geospatial libraries
  • Expand your processing with Data Science tools

Files made obsolete

  • Efficiently store all data as multi-dimensional arrays
  • Keep your metadata with the data
  • Push all update and partitioning logic to storage

Sharing made simple

  • Share data with anyone in seconds
  • Define access policies with array semantics
  • Monitor all activity via logging

Processing made serverless

  • Perform serverless SQL and Python UDFs
  • Experience tremendous scale from your laptop
  • Pay as you go to reduce your costs

Genomics made practical

  • Store population variant data with linear scalability
  • Reduce storage to 40% over sample BCFs
  • Take advantage of the Data Science ecosystem

Geospatial made cloud-ready

  • Store point clouds & gridded data in a single cloud format
  • Analyze your data with popular geospatial libraries
  • Expand your processing with Data Science tools

  • Files made obsolete
  • Sharing made simple
  • Processing made serverless
  • Genomics made practical
  • Geospatial made cloud-ready

A more natural approach

Powerful Format

Store everything as multi-dimensional arrays, the currency of Data Science

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Native Compute

Analyze your data at scale with familiar Data Science tooling

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Easy Sharing

Share diverse data sets with anyone using array access policies and logging

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The TileDB experience

  • Store
  • Analyze
  • Scale
  • Manage

Model your data as dense or sparse multi-dimensional arrays and store them on premises or on the cloud

Python
C++
Java
SQL
Context ctx;
Array array(ctx, "s3://tiledb-test/2d-sparse", TILEDB_READ);
std::vector data(16);
std::vector coords(32);
Query query(ctx, array, TILEDB_READ);
query.set_subarray({1, 4, 1, 4})
     .set_layout(TILEDB_ROW_MAJOR)
     .set_buffer("a", data)
     .set_coordinates(coords);
query.submit();
array.close();
Context ctx = new Context();
Array array = new Array(ctx, "s3://tiledb-test/2d-sparse");
Query query = new Query(array, TILEDB_READ);
query.setSubarray(new NativeArray(ctx, new long[] {1l, 4l, 1l, 4l}, Long.class));
query.setBuffer("a1", new NativeArray(ctx, 16, Integer.class));
query.setCoordinates(new NativeArray(ctx, 32, Long.class));
query.submit();
int[] a1_buff = (int[]) query.getBuffer("a1");
long[] coords = (long[]) query.getBuffer(TILEDB_COORDS);
SELECT AVG(attribute)
FROM `s3://tiledb-test/2d-sparse`;

SELECT dim1, dim2, AVG(attribute1)
OVER (PARTITION BY dim2)
FROM `s3://tiledb-test/2d-sparse`;
>>> import tiledb
>>> array = tiledb.open("s3://tiledb-test/test-array-100x100x30")
>>> array.shape
(100, 100, 30)

>>> array.dtype
dtype("float64")

>>> np.mean(array[:,:,1:10])
0.49943713803540135

Analyze your data natively by intergrating with familiar Data Science tools and a variety of programming language APIs

Scale storage and compute separately and leverage the distributed power of Spark, Dask and PrestoDB

Browse your arrays and share them with anyone on the cloud, monitoring all activity with detailed logs

Products

Pay-as-you-go

Cloud

Manage and compute on your data on the cloud, elastically and without deployment hassle

  • All TileDB Developer features 
  • Access control
  • Logging
  • Serverless SQL
  • Serverless Python UDFs
  • $1.80 / hour of compute
  • $0.14 / GB of data retrieved
Licence

Enterprise

Deploy TileDB Cloud in your private cluster, leveraging LDAP authentication

  • Deploy TileDB Cloud on premises
  • LDAP & SAML support
  • 24/7 support

Use Cases

Genomics

  • Excellent for population genetic studies
  • Store huge collections of gVCF data in a TileDB sparse array
  • Interface in C/C++ or Python, and scale with Dask and Spark
  • Save 40% in space, enjoy parallel IO, and process cost efficiently on the cloud
  • Solve the N+1 problem with rapid updates
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Geospatial

  • Store satellite images, LiDAR, weather data and more as dense or sparse arrays
  • Use familiar geospatial tooling like PDAL, GDAL and rasterio directly on TileDB array data
  • Enjoy cloud-optimized storage and parallel IO
  • Utilize the versatility and power of TileDB’s array model, and create multi-dimensional cubes with arbitrary attributes and metadata
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Dataframes

  • Store dataframes as sparse multi-dimensional sparse arrays
  • Take advantage of multi-dimensional indexing for rapid OLAP queries
  • Integrate with Pandas and scale slicing and dicing with Dask
  • Process fast SQL queries with Spark, PrestoDB and MariaDB
  • Enjoy serverless SQL queries on TileDB Cloud
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