https://www.bigdataldn.com/en-gb/conference/session-details.4500.251781.the-high-performance-data-and-ai-debate.html

The data landscape is fickle, and once-coveted roles like 'DBA' and 'Data Scientist' have faced challenges. Now, the spotlight shines on Data Engineers, but will they suffer the same fate? This talk dives into historical trends.

In the early 2010’s, DBA/data warehouse was the sexiest job. Data Warehouse became the “No Team.”

In the mid-2010’s, data scientist was the sexiest job. Data Science became the “mistaken for” team.

Now, data engineering is the sexiest job. Data Engineering became the “confused team”. The confusion run rampant with questions about the industry: What is a data engineer? What do they do? Should we have all kinds of nuanced titles for variations? Just how technical should they be?

Together, let’s go back to history and look for ways on how data engineering can avoid the same fate as data warehousing and data science. This talk provides a thought-provoking discussion on navigating the exciting yet challenging world of data engineering. Let's avoid the pitfalls of the past and shape a future where data engineers thrive as essential drivers of innovation and success.

🌟 Session Overview 🌟

Session Name: The State of Data Engineering Speaker: Jesse Anderson Session Description: The data landscape is fickle, and once-coveted roles like 'DBA' and 'Data Scientist' have faced challenges. Now, the spotlight shines on Data Engineers, but will they suffer the same fate? This talk dives into historical trends.

In the early 2010s, DBA/Data Warehouse was the sexiest job. Data Warehouse became the 'No Team.'

In the mid-2010s, Data Scientist was the sexiest job. Data Science became the 'mistaken for' team.

Now, Data Engineering is the sexiest job. Data Engineering has become the 'confused team.' The confusion runs rampant with questions about the industry: What is a data engineer? What do they do? Should we have all kinds of nuanced titles for variations? Just how technical should they be?

Together, let's go back in history and look for ways that data engineering can avoid the same fate as data warehousing and data science. This talk provides a thought-provoking discussion on navigating the exciting yet challenging world of data engineering. Let's avoid the pitfalls of the past and shape a future where data engineers thrive as essential drivers of innovation and success.

🚀 About Big Data and RPA 2024 🚀

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The data landscape is fickle, and once-coveted roles like "DBA" and "Data Scientist" have faced challenges. Now, the spotlight shines on Data Engineers, but will they suffer the same fate? 

Thistalk dives into historical trends.

In the early 2010’s, DBA/data warehouse was the sexiest job. Data Warehouse became the “No Team.”

In the mid-2010’s, data scientist was the sexiest job. Data Science became the “mistaken for” team.

Now, data engineering is the sexiest job. Data Engineering became the “confused team”. The confusion run rampant with questions about the industry: What is a data engineer? What do they do? Should we have all kinds of nuanced titles for variations? Just how technical should they be?

Together, let’s go back to history and look for ways on how data engineering can avoid the same fate as data warehousing and data science. 

This talk provides a thought-provoking discussion on navigating the exciting yet challenging world of data engineering. Let's avoid the pitfalls of the past and shape a future where data engineers thrive as essential drivers of innovation and success.

Main Takeaways:

● We need to look back on the history of data teams to avoid their mistakes

● Data Engineering is following the same mistakes as Data Science and Data Warehousing

● Learn the actionable insights to help data engineering avoid similar fates

Unlock the secrets of successful data projects with Jesse Anderson's keynote, 'Why Most Data Projects Fail and How to Avoid It.' 📊🚀 Learn the critical factors behind project success, including the 'who, what, when, where, and how' of data projects, and discover the keys to achieving tangible business value in a structured and efficient manner. 🗝️💡 #DataProjects #successtips

✨ H I G H L I G H T S ✨

🙌 A huge shoutout to all the incredible participants who made Big Data Conference Europe 2023 in Vilnius, Lithuania, from November 21-24, an absolute triumph! 🎉 Your attendance and active participation were instrumental in making this event so special. 🌍

Don't forget to check out the session recordings from the conference to relive the valuable insights and knowledge shared! 📽️

Once again, THANK YOU for playing a pivotal role in the success of Big Data Conference Europe 2023. 🚀 See you next year for another unforgettable conference! 📅 #BigDataConference #SeeYouNextYear

Summary Building data products are complicated by the fact that there are so many different stakeholders with competing goals and priorities. It is also challenging because of the number of roles and capabilities that are necessary to go from idea to delivery. Different organizations have tried a multitude of organizational strategies to improve the success rate of these data teams with varying levels of success. In this episode Jesse Anderson shares the lessons that he has learned while working with dozens of businesses across industries to determine the team structures and communication styles that have generated the best results. If you are struggling to deliver value from big data, or just starting down the path of building the organizational capacity to turn raw information into valuable products then this is a conversation that you don’t want to miss.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. 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 managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Are you bogged down by having to manually manage data access controls, repeatedly move and copy data, and create audit reports to prove compliance? How much time could you save if those tasks were automated across your cloud platforms? Immuta is an automated data governance solution that enables safe and easy data analytics in the cloud. Our comprehensive data-level security, auditing and de-identification features eliminate the need for time-consuming manual processes and our focus on data and compliance team collaboration empowers you to deliver quick and valuable data analytics on the most sensitive data to unlock the full potential of your cloud data platforms. Learn how we streamline and accelerate manual processes to help you derive real results from your data at dataengineeringpodcast.com/immuta. Today’s episode of the Data Engineering Podcast is sponsored by Datadog, a SaaS-based monitoring and analytics platform for cloud-scale infrastructure, applications, logs, and more. Datadog uses machine-learning based algorithms to detect errors and anomalies across your entire stack—which reduces the time it takes to detect and address outages and helps promote collaboration between Data Engineering, Operations, and the rest of the company. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial. If you start a trial and install Datadog’s agent, Datadog will send you a free T-shirt. Your host is Tobias Macey and today I’m interviewing Jesse Anderson about best practices for organizing and managing data teams

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of how you view the mission and responsibilities of a data team?

What are the critical elements of a successful data team? Beyond the core pillars of data science, data engineering, and operations, what other specialized roles do you find hel

Data engineers and data scientists are not interchangeable—and misperceptions of their roles can hurt teams and compromise productivity. This article clears up the differences of each role and how to best optimize these roles.