TileDB VS grouparoo

- 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.

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 !

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.]

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

TileDB, Inc. is the company behind TileDB, the first universal data engine. TileDB allows analytics professionals and data scientists to access, analyze, and share complex data sets with any tool at extreme scale. TileDB overcomes the constraints of columnar tables, flat files, and SQL-only tools, handling all data with a multi-dimensional array engine and extreme interoperability across the data science ecosystem. TileDB Cloud is a totally serverless offering of TileDB, which delivers access control and enables distributed computing at planet-scale, eliminating all cluster management and minimizing cost. TileDB, Inc. was spun out of MIT and Intel Labs in May 2017 and closed a $15M Series A in July 2020, following a previous $4M Seed Round.

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, but the candidates must reside either in the US or in Greece. US candidates must be US citizens, whereas Greek candidates must be Greek or EU citizens.

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. A few features on the roadmap include enhancing our TileDB Cloud offering, optimizing our serverless framework, improving integration with JupyterLab, and expanding our marketplace functionality.

We are primarily seeking:

- Senior Golang Engineer

Apply today at https://tiledb.workable.com !

Reverse ETL is on AirByte's roadmap under the "Future / Not prioritized" section. I wanted to use Grouparoo as a short term solution, but the repo was archived and I think they stopped taking new cloud customers (unknown if this is wrong/outdated).

There are other tools that we will have to adopt in the future but haven’t yet due to lack of necessity. Specifically, one category that is popular in modern data stacks is Reverse ETL (Hightouch, Census, or Grouparoo). We currently don’t have a usecase for piping data back into 3rd party tools but it will definitely come up in the future.

Reverse ETL: Grouparoo, Castled

Grouparoo, the open source Reverse ETL tool we are building, does all of these things. https://www.grouparoo.com

Ingestion / ETL: Airbyte, Singer, Jitsu Transformation: dbt Orchestration: Airflow, Dagster Testing: GreatExpectations Observability: Monosi Reverse ETL: Grouparoo, Castled Visualization: Lightdash, Superset

Grouparoo | Remote (US) | Remote-OK | https://www.grouparoo.com

Grouparoo is a venture-backed software company building open source data tools that make data reliable, accessible, and actionable. We’re empowering teams to make great customer experiences, driven by data. While engineering teams have gotten good at storing and generating data about their customers, it’s rare that this data is used to its full potential in external applications. Grouparoo makes these integrations easy by providing a framework for defining your customer data and reliably syncing it to external tools.

To learn more about who we are, our engineering culture, and whether this is the right place for you, read our Key Values profile: https://www.keyvalues.com/grouparoo

Here are our open roles:

- Senior Backend / Lead Engineer: https://jobs.lever.co/grouparoo/6ba485d1-a5a4-41f0-9fa5-920a...

- Developer Advocate: https://jobs.lever.co/grouparoo/5e1531b4-7ec8-4c10-8e52-fc23...

Tech Stack: TypeScript / Javascript / Node.js, ActionHero, React + Next.js, Postgres & Redis, and whole lot of third-party APIs!

Congrats on the launch! Hightouch looks great and this need is real. Things seem to be going well, so I don't think I'm taking too much away by mentioning that we have been been working on Grouparoo, an open source alternative that solves similar pain points.

A few differences: git developer workflow focused (branches, CI, PRs, etc), ability to self host, segmentation in destinations (tagging people in mailchimp based on rules, for example)

https://www.grouparoo.com

We are building Grouparoo. Obviously, it's a biased sample but we are seeing a few trends at play.

Has anyone heard or used of reverse etl or Hightouch or open sourced Grouparoo?