DataStax and the Future of Real-Time Data Applications with Jonathan Ellis

Summary

Podcast Summary: Software Engineering Daily

Episode: DataStax and the Future of Real-Time Data Applications with Jonathan Ellis
Release Date: November 19, 2024
Host: Sean Falconer
Guest: Jonathan Ellis, Co-Founder and Technical Lead at DataStax

1. Introduction to DataStax and Jonathan Ellis

In this episode of Software Engineering Daily, host Sean Falconer welcomes Jonathan Ellis, co-founder of DataStax, to discuss the company's evolution and its pivot towards AI-driven applications. Jonathan, with nearly 15 years in the data stack arena, shares his passion for coding and the technical challenges he tackles at DataStax.

Notable Quote:

Jonathan Ellis [01:14]: “Writing code... looking forward to taking code and then at the end of the day it does something that it couldn't do before.”

2. Vector Search and AI Integration in Data Stacks

DataStax has been enhancing its platform to support AI-driven applications, particularly focusing on vector search capabilities. Jonathan explains how DataStax aims to be a comprehensive stack for building generative AI applications by integrating components like their Vector Search for Cassandra, the acquisition of Langflow, and partnerships with NVIDIA for embeddings computation.

Notable Quotes:

Jonathan Ellis [02:49]: “We're really trying to remove the complexity as much as possible and let you focus on building your application.”
Sean Falconer [04:03]: “Yes, it's more of like a platform approach than being essentially like a point solution for vector storage.”

3. Overcoming Complexity in AI Application Development

Jonathan highlights the challenges developers face when stitching together various tools for AI applications, such as embedding models and databases. DataStax's approach simplifies this by allowing seamless integration with services like OpenAI and NVIDIA, handling complexities behind the scenes.

Notable Quote:

Jonathan Ellis [04:18]: “There's a lot of unspoken knowledge or it's not necessarily clear what the best practices are... we're trying to bring those into the mix as well through the Langflow platform.”

4. AI-Assisted Coding: Tools, Benefits, and Challenges

The conversation shifts to Jonathan's experiences with AI-assisted coding tools, including GitHub Copilot, Claude, and others. He discusses the initial skepticism he had towards using AI for coding, which transformed into enthusiasm as tools like ChatGPT and GPT-4 demonstrated significant productivity gains.

Notable Quotes:

Jonathan Ellis [06:36]: “For me, it's just so useful that it's worth putting up with all the sharp corners and rough edges.”
Sean Falconer [09:04]: “...ChatGPT and, you know, throwing prompts at it for what I needed... It does that with like a reasonable output.”

5. Enhancing Developer Productivity with AI

Jonathan elaborates on how AI tools have not only increased his productivity but also made coding more enjoyable by handling repetitive and boilerplate tasks. However, he acknowledges limitations, such as instances where AI falls short, requiring manual intervention.

Notable Quotes:

Jonathan Ellis [08:16]: “...I'm having more fun because I've got this AI intern to do kind of the boring parts and I can concentrate on the interesting parts.”
Jonathan Ellis [10:53]: “It's a good mix. I'm really, really happy with the challenge and the intellectual puzzles that programming in 2024 with AI looks like.”

6. Psychological Safety and Speed in Learning

Jonathan points out that AI tools provide a non-judgmental environment for developers to ask questions, enhancing learning and problem-solving without the fear of judgment. This rapid access to information accelerates the development process.

Notable Quote:

Jonathan Ellis [11:19]: “...having that assistant to answer questions like the non judgmental thing, that's great. Absolutely. But also just like the speed, the latency of getting your questions answered now.”

7. Challenges in Integrating Vector Search into Cassandra

Delving deeper into technical aspects, Jonathan discusses the challenges DataStax faced while integrating vector search into Apache Cassandra. This included adding a new vector type, developing efficient vector indexing algorithms like HNSW and Disk Ann, and building a query execution engine capable of handling multiple predicates.

Notable Quotes:

Jonathan Ellis [14:10]: “How do you wire that into the rest of the database?... building a cost-based query optimizer.”
Jonathan Ellis [15:59]: “We can push the compression up to 64x... a much more tractable problem.”

8. The Future of AI in Software Engineering

Jonathan shares his optimistic perspective on AI's role in software engineering, predicting that AI will continue to enhance productivity while also raising concerns about over-reliance. He emphasizes the importance of balancing AI assistance with personal coding skills to maintain a deep understanding of the codebase.

Notable Quotes:

Jonathan Ellis [22:22]: “If you are overusing it, then it's self-limiting... it's a good balance.”
Jonathan Ellis [24:03]: “Unit tests are the first things on the chopping block for me... if you give it a little bit of direction beyond just write tests.”

9. Introducing Colbert Live: Smarter Vector Searches

Jonathan introduces Colbert Live, an open-source library inspired by Stanford's Colbert project. This tool enhances vector searches by creating semantic vectors for each token in a document, enabling more accurate and efficient search results without the need for expensive re-ranking models.

Notable Quotes:

Jonathan Ellis [29:38]: “Instead of representing our documents or our passages with a single vector, let's create a semantic vector for each token...”
Jonathan Ellis [34:38]: “...Colbert style search has the potential to replace those... more relevant results.”

10. Integration and Future Prospects of Colbert Live

Jonathan explains how Colbert Live integrates with various vector databases by abstracting the database layer, allowing developers to implement custom search functionalities across different platforms. He anticipates that Colbert Live will become a standard for vector searches, especially for multimodal data.

Notable Quotes:

Jonathan Ellis [35:27]: “Even more than the traditional vector search and getting better results, I'm more excited for the image search side of things.”
Jonathan Ellis [37:55]: “It's an Apache licensed project... the intent really is for it to be more than just data stacks.”

11. Future Directions for DataStax and AI Applications

Looking ahead, Jonathan envisions 2025 as a pivotal year where AI enables the development of entirely new applications and workflows that were previously unattainable. DataStax aims to support this transition by providing robust infrastructure and tools to harness AI's full potential.

Notable Quotes:

Jonathan Ellis [40:38]: “2023 being a year of experimentation... 2025 is where people are going to go from automating and enhancing their existing products and existing workflows to addressing things that weren't possible before.”
Jonathan Ellis [42:11]: “DataStax wants to help people make that transition.”

12. Concluding Thoughts

Jonathan and Sean conclude the episode by reflecting on the rapid advancements in AI and its transformative impact on software engineering. Jonathan expresses enthusiasm for the future, highlighting DataStax's commitment to leading the charge in AI-powered data solutions.

Notable Quote:

Jonathan Ellis [43:08]: “All right, thanks again, Sean. Cheers.”

Key Takeaways

Comprehensive AI Integration: DataStax is evolving into a full-stack platform for generative AI applications, simplifying the development process by integrating various AI tools and services.
Enhanced Developer Productivity: AI-assisted coding tools like GitHub Copilot and Claude significantly boost productivity and make coding more enjoyable, despite some limitations.
Innovative Vector Search Solutions: Colbert Live represents a significant advancement in vector search technology, offering more accurate and efficient results without the need for costly re-ranking models.
Future Outlook: The AI landscape in software engineering is poised for transformative growth, with companies like DataStax leading the way in enabling new applications and workflows.

This episode offers valuable insights into the intersection of AI and software engineering, showcasing how companies like DataStax are pioneering efforts to streamline and enhance real-time data applications. Jonathan Ellis provides a candid look at both the opportunities and challenges presented by AI, emphasizing the importance of balancing automation with foundational coding skills.

Summary

Podcast Summary: Software Engineering Daily

1. Introduction to DataStax and Jonathan Ellis

Notable Quote:

Jonathan Ellis [01:14]: “Writing code... looking forward to taking code and then at the end of the day it does something that it couldn't do before.”

2. Vector Search and AI Integration in Data Stacks

Notable Quotes:

Jonathan Ellis [02:49]: “We're really trying to remove the complexity as much as possible and let you focus on building your application.”
Sean Falconer [04:03]: “Yes, it's more of like a platform approach than being essentially like a point solution for vector storage.”

3. Overcoming Complexity in AI Application Development

Notable Quote:

Jonathan Ellis [04:18]: “There's a lot of unspoken knowledge or it's not necessarily clear what the best practices are... we're trying to bring those into the mix as well through the Langflow platform.”

4. AI-Assisted Coding: Tools, Benefits, and Challenges

Notable Quotes:

Jonathan Ellis [06:36]: “For me, it's just so useful that it's worth putting up with all the sharp corners and rough edges.”
Sean Falconer [09:04]: “...ChatGPT and, you know, throwing prompts at it for what I needed... It does that with like a reasonable output.”

5. Enhancing Developer Productivity with AI

Notable Quotes:

Jonathan Ellis [08:16]: “...I'm having more fun because I've got this AI intern to do kind of the boring parts and I can concentrate on the interesting parts.”
Jonathan Ellis [10:53]: “It's a good mix. I'm really, really happy with the challenge and the intellectual puzzles that programming in 2024 with AI looks like.”

6. Psychological Safety and Speed in Learning

Notable Quote:

Jonathan Ellis [11:19]: “...having that assistant to answer questions like the non judgmental thing, that's great. Absolutely. But also just like the speed, the latency of getting your questions answered now.”

7. Challenges in Integrating Vector Search into Cassandra

Notable Quotes:

Jonathan Ellis [14:10]: “How do you wire that into the rest of the database?... building a cost-based query optimizer.”
Jonathan Ellis [15:59]: “We can push the compression up to 64x... a much more tractable problem.”

8. The Future of AI in Software Engineering

Notable Quotes:

Jonathan Ellis [22:22]: “If you are overusing it, then it's self-limiting... it's a good balance.”
Jonathan Ellis [24:03]: “Unit tests are the first things on the chopping block for me... if you give it a little bit of direction beyond just write tests.”

9. Introducing Colbert Live: Smarter Vector Searches

Notable Quotes:

Jonathan Ellis [29:38]: “Instead of representing our documents or our passages with a single vector, let's create a semantic vector for each token...”
Jonathan Ellis [34:38]: “...Colbert style search has the potential to replace those... more relevant results.”

10. Integration and Future Prospects of Colbert Live

Notable Quotes:

Jonathan Ellis [35:27]: “Even more than the traditional vector search and getting better results, I'm more excited for the image search side of things.”
Jonathan Ellis [37:55]: “It's an Apache licensed project... the intent really is for it to be more than just data stacks.”

11. Future Directions for DataStax and AI Applications

Notable Quotes:

Jonathan Ellis [40:38]: “2023 being a year of experimentation... 2025 is where people are going to go from automating and enhancing their existing products and existing workflows to addressing things that weren't possible before.”
Jonathan Ellis [42:11]: “DataStax wants to help people make that transition.”

12. Concluding Thoughts

Notable Quote:

Jonathan Ellis [43:08]: “All right, thanks again, Sean. Cheers.”

Key Takeaways

Comprehensive AI Integration: DataStax is evolving into a full-stack platform for generative AI applications, simplifying the development process by integrating various AI tools and services.
Enhanced Developer Productivity: AI-assisted coding tools like GitHub Copilot and Claude significantly boost productivity and make coding more enjoyable, despite some limitations.
Innovative Vector Search Solutions: Colbert Live represents a significant advancement in vector search technology, offering more accurate and efficient results without the need for costly re-ranking models.
Future Outlook: The AI landscape in software engineering is poised for transformative growth, with companies like DataStax leading the way in enabling new applications and workflows.