Interactive Exploratory Data Analysis On Petabyte Scale Data Sets With Arkouda
Summary Exploratory data analysis works best when the feedback loop is fast and iterative. This is easy to achieve when you are working on small datasets, but as they scale up beyond what can fit on a single machine those short iterations quickly become long and tedious. The Arkouda project is a Python interface built on top of the Chapel compiler to bring back those interactive speeds for exploratory analysis on horizontally scalable compute that parallelizes operations on large volumes of data. In this episode David Bader explains how the framework operates, the algorithms that are built into it to support complex analyses, and how you can start using it today. 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. 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Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to dataengineeringpodcast.com/ascend and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer. Your host is Tobias Macey and today I’m interviewing David Bader about Arkouda, a horizontally scalable parallel compute library for exploratory data analysis in Python Interview Introduction How did you get involved in the area of data management? Can you describe what Arkouda is and the story behind it? What are the main goals of the project? How does it address those goals? Who is the primary audience for Arkouda? What are some of the main points of friction that engineers and scientists encounter while conducting exploratory data analysis (EDA)? What kinds of behaviors are they engaging in during these exploration cycles? When data scientists run up against the limitations of their tools and environments how does that impact the work of data engineers/data platform owners? There have been a number of libraries/frameworks/utilities/etc. built to improve the experience and outcomes for EDA. What was missing that made Arkouda necessary/useful? Can you describe how Arkouda is implemented? What are some of the novel algorithms that you have had to design to support Arkouda’s objectives? How have the design/goals/scope of the project changed since you started working on it? How has the evolution of hardware capabilities impacted the set of processing algorithms that are viable for addressing considerations of scale? What are the relative factors of scale along space/time axes that you are optimizing for? What are some opportunities that are still unrealized for algorithmic optimizations to expand horizons for large-scale data manipulation? For teams/individuals who are working with Arkouda can you describe the implementation process and what the end-user workflow looks like? What are the most interesting, innovative, or unexpected ways that you have seen Arkouda used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Arkouda? When is Arkouda the wrong choice? What do you have planned for the future of Arkouda? Contact Info Website LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Arkouda NJIT == New Jersey Institute of Technology NumPy Pandas Podcast.__init__ Episode NetworkX Chapel Massive Graph Analytics Book Ray Podcast.__init__ Episode Dask Podcast Episode Bodo Podcast Episode Stinger Graph Analytics Bears-R-Us 0MQ Triangle Centrality Degree Centrality The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast