Data Engineering Podcast Simple And Scalable Encryption Of Data In Use For Analytics And Machine Learning With Opaque Systems
Dec 26, 2022
01:08:25
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Introduction
00:00 • 2min
How Do You Enable Computation on Confidential Data?
01:40 • 3min
How to Share Confidential Data in a Safe and Privacy Respecting Way?
04:14 • 2min
MC2 and OPIC Systems
06:18 • 3min
Using Confidential Computing to Accelerate Your Digital Transformation
09:48 • 3min
Data Collaboration
12:57 • 6min
Data Encryption - Performance and Scalability Issues
18:31 • 3min
The Performance Overhead of Confident Computing
21:15 • 6min
Uppsolver SQLake - Build Data Pipelines Not DAGs
26:48 • 6min
The Open Source Platform and the Challenges It Faces
32:41 • 5min
Is There a Constraint to Machine Learning?
37:13 • 4min
Pre-Production Versus Production?
41:03 • 2min
The Challenge of the Data Orchestrator
43:07 • 2min
Aspects of Data Segmentation
45:33 • 3min
MC2 - What Do You Keep in the Close Source?
48:41 • 3min
OPEC - What Are Some of the Most Interesting or Unexpected Ways That You've Seemed to Apply?
52:11 • 4min
MC2 - What Are Some of the Most Interesting Lessons You've Learned?
56:18 • 3min
Datafold
59:34 • 3min
The Next Frontier in Data Protection?
01:03:00 • 2min
The Biggest Gap to Bias in Data Management
01:05:20 • 3min
Summary
Encryption and security are critical elements in data analytics and machine learning applications. We have well developed protocols and practices around data that is at rest and in motion, but security around data in use is still severely lacking. Recognizing this shortcoming and the capabilities that could be unlocked by a robust solution Rishabh Poddar helped to create Opaque Systems as an outgrowth of his PhD studies. In this episode he shares the work that he and his team have done to simplify integration of secure enclaves and trusted computing environments into analytical workflows and how you can start using it without re-engineering your existing systems.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. By the time errors have made their way into production, it’s often too late and damage is done. Datafold built automated regression testing to help data and analytics engineers deal with data quality in their pull requests. Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. No more shipping and praying, you can now know exactly what will change in your database! Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold.
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- Build Data Pipelines. Not DAGs. That’s the spirit behind Upsolver SQLake, a new self-service data pipeline platform that lets you build batch and streaming pipelines without falling into the black hole of DAG-based orchestration. All you do is write a query in SQL to declare your transformation, and SQLake will turn it into a continuous pipeline that scales to petabytes and delivers up to the minute fresh data. SQLake supports a broad set of transformations, including high-cardinality joins, aggregations, upserts and window operations. Output data can be streamed into a data lake for query engines like Presto, Trino or Spark SQL, a data warehouse like Snowflake or Redshift., or any other destination you choose. Pricing for SQLake is simple. You pay $99 per terabyte ingested into your data lake using SQLake, and run unlimited transformation pipelines for free. That way data engineers and data users can process to their heart’s content without worrying about their cloud bill. For data engineering podcast listeners, we’re offering a 30 day trial with unlimited data, so go to dataengineeringpodcast.com/upsolver today and see for yourself how to avoid DAG hell.
- Your host is Tobias Macey and today I'm interviewing Rishabh Poddar about his work at Opaque Systems to enable secure analysis and machine learning on encrypted data
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what you are building at Opaque Systems and the story behind it?
- What are the core problems related to security/privacy in data analytics and ML that organizations are struggling with?
- What do you see as the balance of internal vs. cross-organization applications for the solutions you are creating?
- comparison with homomorphic encryption
- validation and ongoing testing of security/privacy guarantees
- performance impact of encryption overhead and how to mitigate it
- UX aspects of not being able to view the underlying data
- risks of information leakage from schema/meta information
- Can you describe how the Opaque Systems platform is implemented?
- How have the design and scope of the product changed since you started working on it?
- Can you describe a typical workflow for a team or teams building an analytical process or ML project with your platform?
- What are some of the constraints in terms of data format/volume/variety that are introduced by working with it in the Opaque platform?
- How are you approaching the balance of maintaining the MC2 project against the product needs of the Opaque platform?
- What are the most interesting, innovative, or unexpected ways that you have seen the Opaque platform used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Opaque Systems/MC2?
- When is Opaque the wrong choice?
- What do you have planned for the future of the Opaque platform?
Contact Info
- Website
- @Podcastinator 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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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Links
- Opaque Systems
- UC Berkeley RISE Lab
- TLS
- MC²
- Homomorphic Encryption
- Secure Multi-Party Computation
- Secure Enclaves
- Differential Privacy
- Data Obfuscation
- AES == Advanced Encryption Standard
- Intel SGX (Software Guard Extensions)
- Intel TDX (Trust Domain Extensions)
- TPC-H Benchmark
- Spark
- Trino
- PyTorch
- Tensorflow
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Upsolver:  Build Real-Time Pipelines. Not Endless DAGs! Creating real-time ETL pipelines is extremely time-consuming and engineering intensive. Why? Because when we attempt to shoehorn a 30-year old batch process into a real-time pipeline, we create an orchestration hell that makes every pipeline a data engineering project. Every pipeline is composed of transformation logic (the what) and orchestration (the how). If you run daily batches, orchestration is simple and there’s plenty of time to recover from failures. However, real-time pipelines with per-hour or per-minute batches make orchestration intricate and data engineers find themselves burdened with building Direct Acyclic Graphs (DAGs), in tools like Apache Airflow, with 10s to 100s of steps intended to address all success and failure modes, task dependencies and maintain temporary data copies. Ori Rafael, CEO and co-founder of Upsolver, will unpack this problem that bottlenecks real-time analytics delivery, and describe a new approach that completely eliminates the need for orchestration, so you can remove Airflow from your development critical path and deliver reliable production pipelines quickly. Go to [dataengineeringpodcast.com/upsolver](dataengineeringpodcast.com/upsolver) to start your 30 day trial with unlimited data, and see for yourself how to avoid DAG hell.
- Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener.
- Datafold:  Datafold helps you deal with data quality in your pull request. It provides automated regression testing throughout your schema and pipelines so you can address quality issues before they affect production. No more shipping and praying, you can now know exactly what will change in your database ahead of time. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI, so in a few minutes you can get from 0 to automated testing of your analytical code. Visit our site at [dataengineeringpodcast.com/datafold](https://www.dataengineeringpodcast.com/datafold) today to book a demo with Datafold.
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