Scalding
Hail
Our great sponsors
| Scalding | Hail | |
|---|---|---|
| - | 5 | |
| 3,469 | 934 | |
| 0.1% | 1.4% | |
| 2.5 | 9.8 | |
| 11 months ago | 2 days ago | |
| Scala | Python | |
| Apache License 2.0 | 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.
Scalding
We haven't tracked posts mentioning Scalding yet.
Tracking mentions began in Dec 2020.
Hail
- We're wasting money by only supporting gzip for raw DNA files
-
Software engineers: consider working on genomics
I don't have any funding to hire right now, but I'm always happy to chat about the industry and my experience building Hail (https://hail.is, https://github.com/hail-is/hail), a tool widely used by folks with large collections of human sequences.
The other posters are not wrong about compensation. Total compensation is off by a factor of two to three.
However, it is absolutely possible to work with a group of top-notch engineers on serious distributed systems & compilers in service of an excellent scientific-user experience. I know because I do. We are lucky to have a PI who respects and hires and diversity of expertise within his lab.
I enjoy being deeply embedded with our users. I do not have to guess what they need or want because I help them do it every day.
I also enjoy enmeshing engineering with statistics, mathematics, and biology. Work is more interesting when so many disciplines conspire towards the end of improved human health.
-
AWS doesn't make sense for scientific computing
I think this post is identifying scientific computing with simulation studies and legacy workflows, to a fault. Scientific computing includes those things, but it also includes interactive analysis of very large datasets as well as workflows designed around cloud computing.
Interactive analysis of large datasets (e.g. genome & exome sequencing studies with 100s of 1000s of samples) is well suited to low-latency, server-less, & horizontally scalable systems (like Dremel/BigQuery, or Hail [1], which we build and is inspired by Dremel, among other systems). The load profile is unpredictable because after a scientist runs an analysis they need an unpredictable amount of time to think about their next step.
As for productionized workflows, if we redesign the tools used within these workflows to directly read and write data to cloud storage as well as to tolerate VM-preemption, then we can exploit the ~1/5 cost of preemptible/spot instances.
One last point: for the subset of scientific computing I highlighted above, speed is key. I want the scientist to stay in a flow state, receiving feedback from their experiments as fast as possible, ideally within 300 ms. The only way to achieve that on huge datasets is through rapid and substantial scale-out followed by equally rapid and substantial scale-in (to control cost).
[1] https://hail.is
-
Ask HN: Who is hiring? (July 2021)
Broad Institute of MIT and Harvard | Cambridge, MA | Associate Software Engineer | Onsite
We are seeking an associate software engineer interested in contributing to an open-source data visualization library for analyzing the biological impact human genetic variation. You will contribute to projects like gnomAD (https://gnomad.broadinstitute.org), the world's largest catalogue of human genetic variation used by hundreds of thousands of researchers and help us scale towards millions of genomes in the coming years. We are also developing next-generation tools for enabling genetic analyses of large biobanks across richly phenotyped individuals (https://genebass.org). In this role you will gain experience developing data-intensive web applications with Typescript, React, Python, Terraform, Google Cloud Platform, and will make use of the scalable data analysis library Hail (https://hail.is). Key to our success is growing a strong team with a diverse membership who foster a culture of continual learning, and who support the growth and success of one another. Towards this end, we are committed to seeking applications from women and from underrepresented groups. We know that many excellent candidates choose not to apply despite their capabilities; please allow us to enthusiastically counter this tendency.
Please provide a CV and links previous work or projects, ideally with contributions visible on Github.
email: [email protected]
What are some alternatives?
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
GridScale - Scala library for accessing various file, batch systems, job schedulers and grid middlewares.
Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.
Vegas - The missing MatPlotLib for Scala + Spark
Scrunch - Mirror of Apache Crunch (Incubating)
metorikku - A simplified, lightweight ETL Framework based on Apache Spark
spark-deployer - Deploy Spark cluster in an easy way.
Scoozie - Scala DSL on top of Oozie XML
Apache Flink - Apache Flink
Jupyter Scala - A Scala kernel for Jupyter
Scio - A Scala API for Apache Beam and Google Cloud Dataflow.
Scoobi - A Scala productivity framework for Hadoop.