An Exploration Of The Data Engineering Requirements For Bioinformatics

Summary Biology has been gaining a lot of attention in recent years, even before the pandemic. As an outgrowth of that popularity, a new field has grown up that pairs statistics and compuational analysis with scientific research, namely bioinformatics. This brings with it a unique set of challenges for data collection, data management, and analytical capabilities. In this episode Jillian Rowe shares her experience of working in the field and supporting teams of scientists and analysts with the data infrastructure that they need to get their work done. This is a fascinating exploration of the collaboration between data professionals and scientists. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the world’s first end-to-end, fully automated Data Observability Platform! In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence, reducing time to detection and resolution from weeks or days to just minutes. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/impact today to save your spot at IMPACT: The Data Observability Summit a half-day virtual event featuring the first U.S. Chief Data Scientist, founder of the Data Mesh, Creator of Apache Airflow, and more data pioneers spearheading some of the biggest movements in data. The first 50 to RSVP with this link will be entered to win an Oculus Quest 2 — Advanced All-In-One Virtual Reality Headset. RSVP today – you don’t want to miss it! Your host is Tobias Macey and today I’m interviewing Jillian Rowe about data engineering practices for bioinformatics projects Interview Introduction How did you get involved in the area of data management? How did you get into the field of bioinformatics? Can you describe what is unique about data needs in bioinformatics? What are some of the problems that you have found yourself regularly solving for your clients? When building data engineering stacks for bioinformatics, what are the attributes that you are optimizing for? (e.g. speed, UX, scale, correctness, etc.) Can you describe a typical set of technologies that you implement when working on a new project? What kinds of systems do you need to integrate with? What are the data formats that are widely used for bioinformatics? What are some details that a data engineer would need to know to work effectively with those formats while preparing data for analysis? What amount of domain expertise is necessary for a data engineer to work in life sciences? What are the most interesting, innovative, or unexpected solutions that you have seen for manipulating bioinformatics data? What are the most interesting, unexpected, or challenging lessons that you have learned while working on bioinformatics projects? What are some of the industry/academic trends or upcoming technologies that you are tracking for bioinformatics? Contact Info LinkedIn jerowe on GitHub Website Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Bioinformatics How Perl Saved The Human Genome Project Neo4J AWS Parallel Cluster Datashader R Shiny Plotly Dash Apache Parquet Dask Podcast Episode HDF5 Spark Superset Data Engineering Podcast Episode Podcast.__init__ Episode FastQ file format BAM (Binary Alignment Map) File Variant Call Format (VCF) HIPAA DVC Podcast Episode LakeFS BioThings API The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast