Interactive Exploratory Data Analysis On Petabyte Scale Data Sets With Arkouda
Data Engineering Podcast
00:00
Arcuta - What's Next?
The arcuta project started off as a project in the department of defence. The goals were really to make a new framework that everyone one around the world can use for lordscale data analysis. Arcuda operates with a user on the front end, inter facing through their jupiter notebook or with python and they connect to a zero m c a message cu going to a super computer in the back end running the h b e cra chapel compiler. That's also open source. And so we as developers are developing all of the plumbing to go from python in the front end all the way through the super computer inthe backend.
Play episode from 16:39
Transcript
Transcript
Episode notes
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. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show!
- Data stacks are becoming more and more complex. This brings infinite possibilities for data pipelines to break and a host of other issues, severely deteriorating the quality of the data and causing teams to lose trust. Sifflet solves this problem by acting as an overseeing layer to the data stack – observing data and ensuring it’s reliable from ingestion all the way to consumption. Whether the data is in transit or at rest, Sifflet can detect data quality anomalies, assess business impact, identify the root cause, and alert data teams’ on their preferred channels. All thanks to 50+ quality checks, extensive column-level lineage, and 20+ connectors across the Data Stack. In addition, data discovery is made easy through Sifflet’s information-rich data catalog with a powerful search engine and real-time health statuses. Listeners of the podcast will get $2000 to use as platform credits when signing up to use Sifflet. Sifflet also offers a 2-week free trial. Find out more at dataengineeringpodcast.com/sifflet today!
- RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder.
- Data teams are increasingly under pressure to deliver. According to a recent survey by Ascend.io, 95% in fact reported being at or over capacity. With 72% of data experts reporting demands on their team going up faster than they can hire, it’s no surprise they are increasingly turning to automation. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. 85%!!! That’s where our friends at Ascend.io come in. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. 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
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
- NetworkX
- Chapel
- Massive Graph Analytics Book
- Ray
- Dask
- Bodo
- 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
The AI-powered Podcast Player
Save insights by tapping your headphones, chat with episodes, discover the best highlights - and more!
