In this episode, we explore how microgravity affects muscle structure and function, using Caenorhabditis elegans as a model organism. Spaceflight-induced muscle atrophy is a major challenge for astronauts, and understanding the molecular and genetic mechanisms behind these changes is key to developing countermeasures.

This discussion is based on the review article: “Advancing Insights into Microgravity-Induced Muscle Changes Using Caenorhabditis elegans as a Model Organism” Beckett LJ, Williams PM, Toh LS, Hessel V, Gerstweiler L, Fisk I, Toronjo-Urquiza L, Chauhan VM. Published in npj Microgravity (2024). 📖 Read the full paper: ⁠https://doi.org/10.1038/s41526-024-00418-z⁠

🔬 Learn how C. elegans provides unique insights into metabolic changes, gene expression, and protein regulation during spaceflight, offering potential strategies to counteract muscle degradation.

🌍 Follow for more research-based discussions on nematodes, space biology, and biomedical science.

This podcast is generated with artificial intelligence and curated by Veeren. If you’d like your publication featured on the show, please get in touch.

📩 More info: 🔗 www.veerenchauhan.com 📧 [email protected]

Summary DataDog is one of the most successful companies in the space of metrics and monitoring for servers and cloud infrastructure. In order to support their customers, they need to capture, process, and analyze massive amounts of timeseries data with a high degree of uptime and reliability. Vadim Semenov works on their data engineering team and joins the podcast in this episode to discuss the challenges that he works through, the systems that DataDog has built to power their business, and how their teams are organized to allow for rapid growth and massive scale. Getting an inside look at the companies behind the services we use is always useful, and this conversation was no exception.

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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Vadim Semenov about how data engineers work at DataDog

Interview

Introduction How did you get involved in the area of data management? For anyone who isn’t familiar with DataDog, can you start by describing the types and volumes of data that you’re dealing with? What are the main components of your platform for managing that information? How are the data teams at DataDog organized and what are your primary responsibilities in the organization? What are some of the complexities and challenges that you face in your work as a result of the volume of data that you are processing?

What are some of the strategies which have proven to be most useful in overcoming those challenges?

Who are the main consumers of your work and how do you build in feedback cycles to ensure that their needs are being met? Given that the majority of the data being ingested by DataDog is timeseries, what are your lifecycle and retention policies for that information? Most of the data that you are working with is customer generated from your deployed agents and API integrations. How do you manage cleanliness and schema enforcement for the events as they are being delivered? What are some of the upcoming projects that you have planned for the upcoming months and years? What are some of the technologies, patterns, or practices that you are hoping to adopt?

Contact Info

LinkedIn @databuryat on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

DataDog Hadoop Hive Yarn Chef SRE == Site Reliability Engineer Application Performance Management (APM) Apache Kafka RocksDB Cassandra Apache Parquet data serialization format SLA == Service Level Agreement WatchDog Apache Spark

Podcast Episode

Apache Pig Databricks JVM == Java Virtual Machine Kubernetes SSIS (SQL Server Integration Services) Pentaho JasperSoft Apache Airflow

Podcast.init Episode

Apache NiFi

Podcast Episode

Luigi Dagster

Podcast Episode

Prefect

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

This week's episode explores the possibilities of extracting novel insights from the many great social web APIs available. Matthew Russell's Mining the Social Web is a fantastic exploration of the tools and methods, and we explore a few related topics. One helpful feature of the book is it's use of a Vagrant virtual machine. Using it, readers can easily reproduce the examples from the book, and there's a short video available that will walk you through setting up the Mining the Social Web virtual machine. The book also has an accompanying github repository which can be found here. A quote from Matthew that particularly reasonates for me was "The first commandment of Data Science is to 'Know thy data'." Take a listen for a little more context around this sage advice. In addition to the book, we also discuss some of the work done by Digital Reasoning where Matthew serves as CTO. One of their products we spend some time discussing is Synthesys, a service that processes unstructured data and delivers knowledge and insight extracted from the data. Some listeners might already be familiar with Digital Reasoning from recent coverage in Fortune Magazine on their cognitive computing efforts. For his benevolent recommendation, Matthew recommends the Hardcore History Podcast, and for his self-serving recommendation, Matthew mentioned that they are currently hiring for Data Science job opportunities at Digital Reasoning if any listeners are looking for new opportunities.