TileDB-VCF VS TileDB

Perhaps of interest https://github.com/TileDB-Inc/TileDB-VCF

Here are the docs to the open-source TileDB-VCF storage engine: https://docs.tiledb.com/main/integrations-and-extensions/population-genomics

TileDB, Inc. | Full-time | REMOTE | USA, Greece | https://tiledb.com/

TileDB is the database designed for discovery, built to organize, structure, and analyze any data. Our solutions for single-cell and population genomics are used by major pharmaceutical companies and research institutes, and power large public data collections such as the Cellxgene Discover Census. We are actively hiring for several roles building our unified data catalog, scalable computation, and interactive analysis platform.

- Infrastructure Engineer: Kubernetes, Terraform, Argo, Grafana, Prometheus, CloudWatch, GitOps; Golang, Python, C++, or Rust (GMT -8/+4).

- Frontend/UI developer: Typescript, React; experience with high-performance/high-volume data and visualization applications. GMT -8/+1

We are fully-remote, with optional co-working hubs in Cambridge, MA, New York, NY, and Athens, Greece. Apply today at https://ats.rippling.com/tiledb-careers/jobs or reach out directly (email in profile).

TileDB, Inc. | Full-Time | REMOTE | USA, Greece/EU | [https://tiledb.com](https://tiledb.com/)

TileDB has recently announced a $34 million Series B fund-raise and is actively hiring for engineers across a range of roles (SRE, backend/distributed systems, database internals, and more). You will have the opportunity to work on innovative technology that creates impact for challenging problems in genomics, geospatial, machine learning, distributed systems, and many other areas.

TileDB Cloud is the modern database, allowing developers and scientists to capture, analyze, and share any data with any tool. We build on a broad foundation of open source, maintaining the TileDB storage engine, libraries for genomics (single-cell and population), geospatial (raster, point clouds, and more), a TileDB visualization engine extending Babylon.js, and much more ([github.com/TileDB-Inc/TileDB](http://github.com/TileDB-Inc/TileDB))

With TileDB, all data — tables, genomics, images, videos, location, time-series — is captured as multi-dimensional arrays. To supercharge this data, TileDB Cloud implements a serverless infrastructure delivering query execution, access control, data and code sharing, and distributed computing at global scale — eliminating cluster management, minimizing TCO, and promoting scientific collaboration and reproducibility.

Website: [https://tiledb.com](https://tiledb.com/) | GitHub: https://github.com/TileDB-Inc/TileDB | Blog: https://tiledb.com/blog

We are actively hiring for several roles including:

- Site Reliability Engineer (k8s, Terraform, automation, Prometheus, CloudWatch, GitOps; Golang, Python)

- single cell genomics: in collaboration with the Chan-Zuckerberg Initiative, we recently released TileDB-SOMA for single cell data, with APIs for both Python and R built around a common storage specification: https://tiledb.com/blog/tiledb-101-single-cell

With TileDB, all data — tables, genomics, images, videos, location, time-series — across multiple domains is captured as multi-dimensional arrays. TileDB Cloud implements a totally serverless infrastructure and delivers access control, easier data and code sharing and distributed computing at global scale, eliminating cluster management, minimizing TCO and promoting scientific collaboration and reproducibility.

Website: https://tiledb.com

GitHub: https://github.com/TileDB-Inc/TileDB

Stavros from TileDB here (Founder and CEO). I thought of requesting some feedback from the community on this blog. It was only natural for a multi-dimensional array database like TileDB to offer vector (i.e., 1D array) search capabilities. But the team managed to do it very well and the results surprised us. We are just getting started in this domain and a lot of new algorithms and features are coming up, but the sooner we get feedback the better.

TileDB-Vector-Search Github repo: https://github.com/TileDB-Inc/TileDB-Vector-Search

TileDB-Embedded (core array engine) Github repo: https://github.com/TileDB-Inc/TileDB

TileDB 101: Vector Search (blog to get kickstarted): https://tiledb.com/blog/tiledb-101-vector-search/

TileDB, Inc. | Full-Time | REMOTE | USA | Greece | https://tiledb.com

TileDB is the database for complex data, allowing data scientists, researchers, and analysts to access, analyze, and share any data with any tool at global scale. We have just launched a vector search library leveraging TileDB and TileDB Cloud for powerful local search and seamless scaling to multi-modal organizational datasets and batched computation: https://tiledb.com/blog/why-tiledb-as-a-vector-database

With TileDB, all data — tables, genomics, images, videos, location, time-series — across multiple domains is captured as multi-dimensional arrays. Our vector search library and other offerings are designed to empower these datasets with extreme interoperability via numerous APIs and tool integrations across the data science ecosystem, eliminating the hassles and inefficiencies of data conversion. TileDB Cloud implements a totally serverless infrastructure and delivers access control, easier data and code sharing and distributed computing at global scale, eliminating cluster management, minimizing TCO and promoting scientific collaboration and reproducibility.

Interesting. Have you come across TileDB before?

https://tiledb.com/

Hi folks, Stavros from TileDB here. Here are my two cents on tabular data. TileDB (Embedded) is a very serious competitor to Parquet, the only other sane choice IMO when it comes to storing large volumes of tabular data (especially when combined with Arrow). Admittedly, we haven’t been advertising TileDB’s tabular capabilities, but that’s only because we were busy with much more challenging applications, such as genomics (population and single-cell), LiDAR, imaging and other very convoluted (from a data format perspective) domains.

Similar to Parquet:

* TileDB is columnar and comes with a lot of compressors, checksum and encryption filters.

* TileDB is built in C++ with multi-threading and vectorization in mind

* TileDB integrates with Arrow, using zero-copy techniques

* TileDB has numerous optimized APIs (C, C++, C#, Python, R, Java, Go)

* TileDB pushes compute down to storage, similar to what Arrow does

Better than Parquet:

* TileDB is multi-dimensional, allowing rapid multi-column conditions

* TileDB builds versioning and time-traveling into the format (no need for Delta Lake, Iceberg, etc)

* TileDB allows for lock-free parallel writes / parallel reads with ACID properties (no need for Delta Lake, Iceberg, etc)

* TileDB can handle more than tables, for example n-dimensional dense arrays (e.g., for imaging, video, etc)

Useful links:

* Github repo (https://github.com/TileDB-Inc/TileDB)

* TileDB Embedded overview (https://tiledb.com/products/tiledb-embedded/)

* Docs (https://docs.tiledb.com/)

* Webinar on why arrays as a universal data model (https://tiledb.com/blog/why-arrays-as-a-universal-data-model)

Happy to hear everyone’s thoughts.

It could be worth checking out TileDB: https://github.com/TileDB-Inc/TileDB The entire system, down to the data format itself, is optimized around storing multi-dimensional arrays. It also supports timestamps and real numbers as dimensions, which could be handy given your example data. [Full disclosure: I currently work for TileDB.]