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© 2024 The Firebolt Data Bros
Description
The Data Engineering Show is a podcast for data engineering and BI practitioners to go beyond theory. Learn from the biggest influencers in tech about their practical day-to-day data challenges and solutions in a casual and fun setting.
SEASON 1 DATA BROS
Eldad and Boaz Farkash shared the same stuffed toys growing up as well as a big passion for data. After founding Sisense and building it to become a high-growth analytics unicorn, they moved on to their next venture, Firebolt, a leading high-performance cloud data warehouse.
SEASON 2 DATA BROS
In season 2 Eldad adopted a brilliant new little brother, and with their shared love for query processing, the connection was immediate. After excelling in his MS, Computer Science degree, Benjamin Wagner joined Firebolt to lead its query processing team and is a rising star in the data space.
For inquiries contact tamar@firebolt.io
SEASON 1 DATA BROS
Eldad and Boaz Farkash shared the same stuffed toys growing up as well as a big passion for data. After founding Sisense and building it to become a high-growth analytics unicorn, they moved on to their next venture, Firebolt, a leading high-performance cloud data warehouse.
SEASON 2 DATA BROS
In season 2 Eldad adopted a brilliant new little brother, and with their shared love for query processing, the connection was immediate. After excelling in his MS, Computer Science degree, Benjamin Wagner joined Firebolt to lead its query processing team and is a rising star in the data space.
For inquiries contact tamar@firebolt.io
39 Episodes
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This is a special episode of The Data Engineering Show, and joining the Bros is not one guest, nor even two – instead they’re revisiting the best bits from three different fascinating episodes. In each, they spotlight essential trends and lessons learned across the evolving data engineering landscape. From data observability to bridging academia with real-world practice, this episode covers perspectives on where data engineering is heading and why certain challenges persist.
In this episode of The Data Engineering Show, Ryanne Dolan from LinkedIn joins the Bros to discuss LinkedIn's Hoptimator project. Ryanne explains how they’re simplifying complex data workflows by automating them through SQL queries, integrating Kubernetes, Kafka, and Flink. The conversation highlights the shift towards a consumer-driven data model and the future of data engineering.
SQL’s slow. SQL’s stupid. We hear these claims every time a new shiny tool enters the market, only to realize five years later when the hype dies down that SQL is actually a good idea. In this super techie episode of the Data Engineering Show, Andy Pavlo, Associate Professor at Carnegie Mellon University, joins the bros to delve into database internals and optimization. Andy discusses leveraging ML for autonomous database optimization, using Postgres for practical applications, tuning production databases safely, and why SQL is here to stay.
Too often expensive resources and manhours are spent on dashboards no one uses, resulting in zero ROI. Philip Philip Zelitchenko, VP of Data & Analytics at ZoomInfo met the bros to talk about adopting product management principles to ensure data projects have value, and provide an unfiltered peak into ZoomInfo’s data stack and unique tech culture.
Matthew Weingarten, Lead Data Engineer at Disney Streaming, talks about principles essential for data quality, cost optimization, debugging, and data modeling, as adopted by the world's leading companies.
Data engineering should be less about the stack and more about best practices. While tools may change, foundational principles will remain constant. Joseph Mercado, Senior Data Engineer at LinkedIn, is on The Data Engineering Show to talk about principles that are key to success, leveraging AI for automation, and adopting software engineering methods.
Joe Hellerstein is the Jim Gray Professor of Computer Science at Berkeley and Joseph Gonzalez is an Associate Professor in the Electrical Engineering and Computer Science department. They’ve inspired generations of database enthusiasts (including Benji and Eldad) and have come on the show to talk about all things LLM and RunLLM which they co-founded.If you consider yourself a hardcore engineer, this episode is for you.
There are two types of data influencers on LinkedIn:1. Those who talk directly about the products and companies they work for2. Those that provide more general guidance, tips and opinions Can influencers actually be passionate about the products they’re developing and straightforwardly talk about them without sounding salesly? We’re kicking off 2024 with the amazing Megan Lieu on a new Data Engineering Show episode.Megan is one of those influencers that combine the two approaches, and with almost 100K followers, her content seems to be resonating with many data folks. She talked to the bros about her approach to data advocacy as well as the power of notebooks, especially when they become broader and enable collaboration.
Every data team should have at least one data engineer with a software engineering background. This time on The Data Engineering Show, Xiaoxu Gao is an inspiring Python and data engineering expert with 10.6K followers on Medium. She’s a data engineer at Adyen with a software engineering background, and she met the bros to talk about why both software and data engineering skills are so important.Without software engineering skills you’ll be limited to the rigid capabilities of your stack. But without data engineering skills you’ll find it hard to be cost effective and see the bigger picture.
Vin Vashista, the guy we all love to follow, has never seen a dashboard with positive ROI. This time on The Data Engineering Show, he met the bros to talk about the difference between BI dashboards and analytics that actually introduce knowledge. It’s no longer just about the data volume, it’s about quality and relevance.
After co-writing the best-selling book ‘Fundamentals of Data Engineering’, Joe Reis and Matt Housely joined the bros for some much-needed ranting, priceless data advice, and good laughs. So why are we still talking about providing business value and dashboards, even though we don’t really have anything new to say? If there are so many great tools in the data stack, why are we still so troubled? How can we focus more on things like data governance and data quality that’ll actually push the industry forward?
As people in the data industry go, Bill Inmon is among the top, often seen as the godfather of the data warehouse. In this Data Engineering Show episode, Bill Inmon talks about surviving rabbit holes throughout the evolution of data, the data modeling renaissance, and why ChatGPT is not Textual ETL.
As companies scale, data gets messy. The data team says one thing, the business team says something completely different. Meenal Iyer, VP Data at Momentive.ai, Met the Data Bros to talk about enforcing collaboration in large organizations to ensure what she considers the three most important data factors: Adoption, Trust, and Value.
When it comes to data management, have we come a long way since the early 2000s? Or has it simply taken us 20 years to finally realize that you can’t scale properly without data modeling. With over 20 years of experience in the data space, leading engineering teams at Cisco, Oracle, Greenplum, and now as Sr. Director of Engineering at BlackRock, Krishnan Viswanathan talks about the data engineering challenges that existed two decades ago and still exist today.
How good you are at Spark or Flink ≠ how good you are at data engineering. After years of data engineering experience at Airbnb, Netflix, and Facebook, Zach Wilson is now focused on spreading the knowledge in EcZachly and all over social media. He met Benjamin Wagner to explain why data modeling and storytelling are more important than the actual tech, why data engineering is going to see more job growth than data science, and what brought him to start creating content, reaching over 250K followers on LinkedIn.
Data engineers are not paid to do support. Liran Yogev, Director of Engineering at ZipRecruiter, and Doron Porat, Director of Infrastructure at Yotpo talk about building resilient self-service products that keep customers happy and engineers calm. They walked the bros through their data stacks and explained how ZipRecruiter is completely rebuilding its data layer from scratch.
Barr Moses, CEO of Monte Carlo explains the difference between data quality and data observability, and how to make sure your data is accurate in a world where so many different teams are accessing it.
Weichen Wang, Senior Engineering Manager at Amplitude, came to meet the bros to talk about Amplitude's cutting-edge data stack and how it processes 5 Trillion real-time events while dealing with mutable data and massive scale.
80% of the code that you write doesn’t work on the first try. And that’s fine. But knowing which 80% is not working and which 20% is working is the actual challenge. After 10 years at Facebook, managing and scaling the Seattle site to over 6000 engineers(!) Vijaye Raji founded Statsig to make observability automated and real-time. How is the semantic layer managed? How was the Statsig team able to build an observability product that handles real-time ever-changing metadata? What are Vijaye’s main takeaways from engineering at Facebook? Tune in.
Sudeep Kumar, Prinipal Engineer at Salesforce is a ClickHouse fan. He considers the shift to ClickHouse as one of his biggest accomplishments during his eBay days and walks Boaz through his experience with the platform. How on one hand it handled 2B events per minute, but also how it required rollups which compromised granularity when extending time windows.
Besides a ClickHouse review from a practitioner’s point of view, Sudeep tells us about interesting use-cases he’s working on at Salesforce.
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In the ever-expanding universe of data management, two giants have emerged - ClickHouse and Snowflake. It's like comparing a speedy starship to a cozy rocket - both designed for different galactic quests. Want to know more about their cosmic clash? 🚀 Enter ClickHouse, the lean, mean, real-time data processing machine. It's like the Millennium Falcon of data warehouses - compact, lightning-fast, and open-source! 🌨️ On the other side, there's Snowflake, the blizzard of data warehousing - cool, flexible, and cloud-native. But wait, there's more! In this epic data duel, there's a wildcard - a fully managed Apache Kafka® service! It's the interstellar courier, delivering data to these titans. 📚 Dig deeper into this celestial showdown https://double.cloud/blog/posts/2023/05/clickhouse-vs-snowflake/. Discover which data behemoth rules the galaxies, and may the data force be with you!