talk-data.com

Try Keboola 👉 https://www.keboola.com/mcp?utm_campaign=FY25_Q2_RoW_Marketing_Events_Webinar_Keboola_MCP_Server_Launch_June&utm_source=Youtube&utm_medium=Avery Today, we'll create an entire data pipeline from scratch without writing a single line of code! Using Keboola MCP server and ClaudeAI, we’ll extract data from my FindADataJob.com RSS feed, transform it, load it into Google BigQuery, and visualize it with Streamlit. This is the future of data engineering! Keboola MCP Integration: https://mcp.connection.us-east4.gcp.keboola.com/sse I Analyzed Data Analyst Jobs to Find Out What Skills You ACTUALLY Need https://www.youtube.com/watch?v=lo3VU1srV1E&t=212s 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator ⌚ TIMESTAMPS 00:00 - Introduction 00:54 - Definition of Basic Data Engineering Terms 02:26 - Keboola MCP and Its Capabilities 07:48 - Extracting Data from RSS Feed 12:43 - Transforming and Cleaning the Data 19:19 - Aggregating and Analyzing Data 23:19 - Scheduling and Automating the Pipeline 25:04 - Visualizing Data with Streamlit

🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

Martin Fiser, Field CTO at Keboola with 8 years at Google, joined Yuliia and Scott to challenge modern data stack complexity and costs. He advocates for all-in-one platforms over fragmented solutions, highlighting how companies waste up to 40% of time on tool integration. Martin shared insights on US-European cultural differences in data approaches and warned against "development by resume" culture where engineers prioritize trendy tools over business outcomes.

Buckle up and join Tomas Trnka, Chief Data Officer at Carvago, as he unveils the journey behind Carvago’s rapid rise as a leading online marketplace for used cars. Discover how Carvago built a production-ready solution in just 3 months using the Keboola platform. Tomas will dive into the architecture “under the hood”, showing how they process over 5 million car classifieds daily and iteratively develop new data products. Part of the presentation will be dedicated to Data Mesh, with real-world examples of its implementation. Finally, learn about Carvago’s unique approach to AI, blending classic machine learning with AI enhancements. Attendees will gain practical insights and recommendations for accelerating data initiatives and building scalable, cutting-edge solutions.

Discover how Carvago built a production-ready solution in just 3 months using the Keboola platform.

Hello Prague dwellers and Duck typists!

The 21st Prague PyData meetup will take place at Keboola. As usual, the talks will start at 18:30 but we encourage you to come as soon as 18:00 to enjoy the opportunity to socialise and refresh yourselves (which you can continue doing during the break and after the talks).

Our main goal is to build the community around Python and data and make it welcoming to people of various skills and experience levels.

⚡ If you are interested in giving a lightning talk (up to 5 minutes to present an idea, tool or results related at least to some degree to Python and/or data), please contact us before or during the event.

📢How Data Improves Your Life in Prague (František Kaláb, OICT/Golemio)

At Prague Data Platform, we collect, process, and evaluate data from the city to enhance decision-making and improve the lives of its citizens. From waste management to public transport, this talk will provide an overview of the city's data landscape and showcase specific areas where data is leveraged to the city's advantage. I will also discuss how we utilize open-source technologies and adhere to open standards to make our work transparent and accessible to clients and citizens.

📢DuckDB Intro with Case Study from Keboola (David Ešner, Keboola)

DuckDB is a powerful tool for data analysts and data engineers. It is an embedded, in-process database that provides connectors and capabilities comparable to complex query engines like Spark or Presto, yet it's as simple to set up as SQLite. In this talk, I will provide a brief introduction to the technology and how we leverage it as a simple magic wand for easy, in-process data crunching within the Keboola container architecture. I will walk you through our rather unusual use cases

Summary

The data ecosystem has been building momentum for several years now. As a venture capital investor Matt Turck has been trying to keep track of the main trends and has compiled his findings into the MAD (ML, AI, and Data) landscape reports each year. In this episode he shares his experiences building those reports and the perspective he has gained from the exercise.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Businesses that adapt well to change grow 3 times faster than the industry average. As your business adapts, so should your data. RudderStack Transformations lets you customize your event data in real-time with your own JavaScript or Python code. Join The RudderStack Transformation Challenge today for a chance to win a $1,000 cash prize just by submitting a Transformation to the open-source RudderStack Transformation library. Visit dataengineeringpodcast.com/rudderstack today to learn more Your host is Tobias Macey and today I'm interviewing Matt Turck about his annual report on the Machine Learning, AI, & Data landscape and the insights around data infrastructure that he has gained in the process

Interview

Introduction How did you get involved in the area of data management? Can you describe what the MAD landscape report is and the story behind it?

At a high level, what is your goal in the compilation and maintenance of your landscape document? What are your guidelines for what to include in the landscape?

As the data landscape matures, how have you seen that influence the types of projects/companies that are founded?

What are the product categories that were only viable when capital was plentiful and easy to obtain? What are the product categories that you think will be swallowed by adjacent concerns, and which are likely to consolidate to remain competitive?

The rapid growth and proliferation of data tools helped establish the "Modern Data Stack" as a de-facto architectural paradigm. As we move into this phase of contraction, what are your predictions for how the "Modern Data Stack" will evolve?

Is there a different architectural paradigm that you see as growing to take its place?

How has your presentation and the types of information that you collate in the MAD landscape evolved since you first started it?~~ What are the most interesting, innovative, or unexpected product and positioning approaches that you have seen while tracking data infrastructure as a VC and maintainer of the MAD landscape? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the MAD landscape over the years? What do you have planned for future iterations of the MAD landscape?

Contact Info

Website @mattturck on Twitter MAD Landscape Comments Email

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. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

MAD Landscape First Mark Capital Bayesian Learning AI Winter Databricks Cloud Native Landscape LUMA Scape Hadoop Ecosystem Modern Data Stack Reverse ETL Generative AI dbt Transform

Podcast Episode

Snowflake IPO Dataiku Iceberg

Podcast Episode

Hudi

Podcast Episode

DuckDB

Podcast Episode

Trino Y42

Podcast Episode

Mozart Data

Podcast Episode

Keboola MPP Database

The intro and outro music is f