Building The DataDog Platform For Processing Timeseries Data At Massive Scale
Data Engineering Podcast
Data Dog
We have like some internal projects about like sparkly performance, money and touring. And we're working on it, but I'm not sure like how and when we're going to release it. The execution time of a job at one point in time isn't necessarily indicative of its overall performance because it's highly dependent on the data that it's working with. It's definitely difficult to be able to say with any high degree of certainty whether a particular tuning is having the desired impact without being able to measure it over an extended period.
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
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- 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
- @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.
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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
- Apache Pig
- Databricks
- JVM == Java Virtual Machine
- Kubernetes
- SSIS (SQL Server Integration Services)
- Pentaho
- JasperSoft
- Apache Airflow
- Apache NiFi
- Luigi
- Dagster
- Prefect
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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