Hail
Apache Spark
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Hail | Apache Spark | |
---|---|---|
5 | 101 | |
934 | 38,320 | |
1.4% | 1.1% | |
9.8 | 10.0 | |
2 days ago | 7 days ago | |
Python | Scala | |
MIT License | Apache License 2.0 |
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.
Hail
- We're wasting money by only supporting gzip for raw DNA files
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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.
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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
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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]
Apache Spark
- "xAI will open source Grok"
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Groovy 🎷 Cheat Sheet - 01 Say "Hello" from Groovy
Recently I had to revisit the "JVM languages universe" again. Yes, language(s), plural! Java isn't the only language that uses the JVM. I previously used Scala, which is a JVM language, to use Apache Spark for Data Engineering workloads, but this is for another post 😉.
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🦿🛴Smarcity garbage reporting automation w/ ollama
Consume data into third party software (then let Open Search or Apache Spark or Apache Pinot) for analysis/datascience, GIS systems (so you can put reports on a map) or any ticket management system
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Go concurrency simplified. Part 4: Post office as a data pipeline
also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.
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Five Apache projects you probably didn't know about
Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features.
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Apache Spark VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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Integrate Pyspark Structured Streaming with confluent-kafka
Apache Spark - https://spark.apache.org/
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Spark – A micro framework for creating web applications in Kotlin and Java
A JVM based framework named "Spark", when https://spark.apache.org exists?
- Rest in Peas: The Unrecognized Death of Speech Recognition (2010)
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PySpark SparkSession Builder with Kubernetes Master
I recently saw a pull request that was merged to the Apache/Spark repository that apparently adds initial Python bindings for PySpark on K8s. I posted a comment to the PR asking a question about how to use spark-on-k8s in a Python Jupyter notebook, and was told to ask my question here.
What are some alternatives?
GridScale - Scala library for accessing various file, batch systems, job schedulers and grid middlewares.
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
Vegas - The missing MatPlotLib for Scala + Spark
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
metorikku - A simplified, lightweight ETL Framework based on Apache Spark
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Scoozie - Scala DSL on top of Oozie XML
Scalding - A Scala API for Cascading
Jupyter Scala - A Scala kernel for Jupyter
mrjob - Run MapReduce jobs on Hadoop or Amazon Web Services
Summingbird - Streaming MapReduce with Scalding and Storm
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.