engineering-principles
TileDB
engineering-principles | TileDB | |
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1 | 14 | |
129 | 1,764 | |
0.0% | 1.0% | |
0.0 | 9.7 | |
over 2 years ago | 1 day ago | |
C++ | ||
Creative Commons Attribution 4.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
engineering-principles
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Ask HN: Who is hiring? (January 2021)
Skyscanner | Onsite - London (UK remote for 2021) | Mobile Software Engineers (iOS/Android) | https://www.skyscanner.net/jobs/current-jobs/
Skyscanner are building the best travel marketplace on the small screen. We are building three new teams including mid-level / senior / principal engineers with native app experience.
We love solving complex problems at scale with a very customer first mindset. Our culture is very established and forward thinking with a clear set of engineering principals - https://github.com/Skyscanner/engineering-principles
This is a truly exciting moment for us to influence the way millions of travellers globally will interact with travel in a post Covid world
TileDB
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Ask HN: Who is hiring? (May 2024)
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)
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Ask HN: Who is hiring? (September 2023)
- 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
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Why TileDB as a Vector Database
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/
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Ask HN: Who is hiring? (August 2023)
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.
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Ask HN: Who is hiring? (December 2022)
TileDB, Inc. | Full-Time | REMOTE | USA | Greece | https://tiledb.com
TileDB transforms the lives of analytics professionals and data scientists with a universal database, allowing them to access, analyze, and share any data with any tool at global scale. TileDB unifies the way we think about data, delivering superior performance and foundational data management capabilities. All data — tables, genomics, images, videos, location, time-series — across multiple domains is captured as multi-dimensional arrays. TileDB offers 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.
TileDB, Inc. was spun out of MIT and Intel Labs in May 2017 and is backed by Two Bear Capital, Nexus Venture Partners, Uncorrelated Ventures, Intel Capital and Big Pi.
Recent HN article: https://news.ycombinator.com/item?id=23896131
Website: https://tiledb.com
GitHub: https://github.com/TileDB-Inc/TileDB
Docs: https://docs.tiledb.com
Blog: https://tiledb.com/blog
Our headquarters are located in Cambridge, MA and we have a subsidiary in Athens, Greece. We offer the ability to work remotely. If you are located outside of the USA and Greece we have options to accommodate this, don't hesitate to apply!
We have several open positions aimed at increasing TileDB’s feature set, growth and adoption. You will have the opportunity to work on innovative technology that creates impact on challenging and exciting problems in Genomics, Geospatial, Time Series, and more. Immediate features on the roadmap for TileDB Cloud include, advanced distributed computations, advanced computation pushdown, improved multi-cloud deployments and more.
We are actively seeking:
- Senior Golang Engineer
- Senior Python Engineer
- Site Reliability Engineer
- React Frontend Engineer
Apply today at https://tiledb.workable.com !
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Historical weather data API for machine learning, free for non-commercial
Interesting. Have you come across TileDB before?
https://tiledb.com/
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Why isn’t there a decent file format for tabular data?
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.
- Genomics data management reimagined. Analyze and share enormous variant datasets with TileDB Cloud.
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TileDB VS Activeloop hub - a user suggested alternative
2 projects | 20 Oct 2021
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Seeking options for multidimensional data storage
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.]
What are some alternatives?
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