We stream the podcasts on YouTube, where each session is also recorded and published on our channel, complete with timestamps, a transcript, and important links.

You can access all the podcast episodes here - https://datatalks.club/podcast.html

📚Check our free online courses ML Engineering course - http://mlzoomcamp.com Data Engineering course - https://github.com/DataTalksClub/data-engineering-zoomcamp MLOps course - https://github.com/DataTalksClub/mlops-zoomcamp Analytics in Stock Markets - https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp LLM course - https://github.com/DataTalksClub/llm-zoomcamp Read about all our courses in one place - https://datatalks.club/blog/guide-to-free-online-courses-at-datatalks-club.html

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Dante joins the podcast to break down the June employment report. While everyone agreed that the report was mostly good, concerns remain around the concentration of job gains and the slowdown in hiring. With job growth moderating and the unemployment rate edging higher, the team argues that the time is now for the Fed to start cutting rates.  Link to survey Guest Hosts: Dante DeAntonio - Senior Director, Moody's Analytics Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X' @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

Neste episódio, mergulhamos profundamente no tema automação de pipelines de dados e seu impacto na eficiência operacional. Descubra como as tecnologias de automação estão revolucionando a gestão de dados e impulsionando a produtividade das equipes.

Neste episódio do Data Hackers — a maior comunidade de AI e Data Science do Brasil-, conheçam Murilo Viveiros — Gerente de produto na BMC Software; Fabiana Delfino — Sr. Solution Engineer na BMC Software e o Luiz Pereira — Data Architecture Manager na Gerdau

Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Caso queira, você também pode ouvir o episódio aqui no post mesmo!

Falamos no episódio

Nossa Bancada Data Hackers:

Monique Femme — Head of Community Management na Data Hackers Gabriel Lages — Co-founder da Data Hackers e Data & Analytics Sr. Director na Hotmart.

Referências:

Data4all: https://ada.tech/sou-aluno/plataforma/gerdau-data4all Conheça a BMC : https://www.bmcsoftware.pt/

Lindsay Murphy joins me to chat about whether companies are ready for AI in their data and analytics workflows. The verdict - listen and find out more :)

Lindsay's LinkedIn: https://www.linkedin.com/in/lindsaymurphy4/

The Data Product Management In Action podcast, brought to you by Soda and executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. In Season 01, Episode 06, host Frannie Helforoush (Senior Digital Product Manager at RBC Global Asset Management) and guest Nathan Worrell (Senior Product Manager, Data Analytics at Cortland) explore areas that are crucial to successfully realizing data product management and delivering value. With Nathan's experience and passion, he shares his thoughts on applying product thinking to data products and emphasizes the often-forgotten core soft skills necessary to augment success. They leave no stone unturned as they dive into the detail of product thinking. Nathan provides practical, concrete examples that are easy for anyone to take away and implement, including the strategic use of Generative AI.  About our host Frannie Helforoush: Frannie's journey began as a software engineer and evolved into a strategic product manager. Now, as a data product manager, she leverages her expertise in both fields to create impactful solutions. Frannie thrives on making data accessible and actionable, driving product innovation, and ensuring product thinking is integral to data management. Connect with Frannie on LinkedIn. About our guest Nathan Worrell: Nathan is a dynamic product manager with a passion for AI, data, and process optimization. He has a proven track record of success across multiple industries, leading complex initiatives and building products from the ground up. Nathan thrives on working with diverse teams with the goal of driving businesses to become more data-driven. Connect with Nathan on LinkedIn. All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else. Join the conversation on LinkedIn. Apply to be a guest or nominate someone that you know.

President Joe Biden and former President Donald Trump will pursue very different economic policies if reelected. Both have put forward a wide range of proposals to change the tax code, government spending, and trade, immigration and regulatory policies that if implemented could have significant consequences on the economy’s performance for years to come. In this bonus episode of Inside Economics, the team considers what policies would likely be implemented under different election scenarios and their economic impact. Read the full analysis by Mark Zandi, Brendan La Cerda and Justin Begley: Assessing the Macroeconomic Consequences of Biden vs. Trump Guest Hosts: Justin Begley - Economist, Moody's Analytics, Brendan La Cerda - Director/Senior Economist, Moody's Analytics Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X' @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

When developing Machine Learning (ML) models, the biggest challenges are often infrastructural. How do we deploy our model and expose an inference API? How can we retrain? Can we continuously evaluate performance and monitor model drift? In this talk, we will present how we are tackling these problems at the Philadelphia Phillies by developing a suite of tools that enable our software engineering and analytics teams to train, test, evaluate, and deploy ML models - that can be entirely orchestrated in Airflow. This framework abstracts away the infrastructural complexities that productionizing ML Pipelines presents and allows our analysts to focus on developing robust baseball research for baseball operations stakeholders across player evaluation, acquisition, and development. We’ll also look at how we use Airflow, MLflow, MLServer, cloud services, and GitHub Actions to architect a platform that supports our framework for all points of the ML Lifecycle.

Our Idea to platformize Ingestion pipelines is driven via Airflow in the background and streamline the entire ingestion process for Self Service. With customer experience on top of it and making data ingestion fool proof as part of Analytics data team, Airflow is just complementing for our vision.

NCR Voyix Retail Analytics AI team offers ML products for retailers while embracing Airflow as its MLOps Platform. As the team is small and there have been twice as many data scientists as engineers, we encountered challenges in making Airflow accessible to the scientists: As they come from diverse programming backgrounds, we needed an architecture enabling them to develop production-ready ML workflows without prior knowledge of Airflow. Due to dynamic product demands, we had to implement a mechanism to interchange Airflow operators effortlessly. As workflows serve multiple customers, they should be easily configurable and simultaneously deployable. We came up with the following architecture to deal with the above: Enabling our data scientists to formulate ML workflows as structured Python files. Seamlessly converting the workflows into Airflow DAGs while aggregating their steps to be executed on different Airflow operators. Deploying DAGs via CI/CD’s UI to the DAGs folder for all customers while considering definitions for each in their configuration files. In this session, we will cover Airflow’s evolution in our team and review the concepts of our architecture.

This talk is presented by Broadcom. Airflow’s “workflow as code” approach has many benefits, including enabling dynamic pipeline generation and flexibility and extensibility in a seamless development environment. However, what challenges do you face as you expand your Airflow footprint in your organization? What if you could enhance Airflow’s monitoring capabilities, forecast DAG and task executions, obtain predictive alerting, visualize trends, and get more robust logging? Broadcom’s Automation Analytics & Intelligence (AAI) offers advanced analytics for workload automation for cloud and on-premises automation. It connects easily with Airflow to offer improved visibility into dependencies between tasks in Airflow DAGs along with the workload’s critical path, dynamic SLA management, and more. Join our presentation to hear more about how AAI can help you improve service delivery. We will also lead a workshop that will allow you to dive deeper into how easy it is to install our Airflow Connector and get started visualizing your Airflow DAGs to optimize your workload and identify issues before they impact your business.

Data engineers have shifted from delivering data for internal analytics applications to customer-facing data products. And with that shift comes a whole new level of operational rigor necessary to instill trust and confidence in the data. How do you hold data pipelines to the same standards as traditional software applications? Can you apply principles learned from the field of SRE to the world of data? In this talk, we’ll explore how we’ve seen this evolve in Astronomer’s customer base and highlight best practices learned from the most critical data product applications we’ve seen. We’ll hear from Astronomer’s own data team as they went through the transformation from analytics to data products. And we’ll showcase a new product we’re building to help data teams around the world solve exactly this problem!

Using various operators to perform daily routines. Integration with Technologies: Redis: Acts as a caching mechanism to optimize data retrieval and processing speed, enhancing overall pipeline performance. MySQL: Utilized for storing metadata and managing task state information within Airflow’s backend database. Tableau: Integrates with Airflow to generate interactive visualizations and dashboards, providing valuable insights into the processed data. Amazon Redshift: Panasonic leverages Redshift for scalable data warehousing, seamlessly integrating it with Airflow for data loading and analytics. Foundry: Integrated with Airflow to access and process data stored within Foundry’s data platform, ensuring data consistency and reliability. Plotly Dashboards: Employed for creating custom, interactive web-based dashboards to visualize and analyze data processed through Airflow pipelines. GitLab CI/CD Pipelines: Utilized for version control and continuous integration/continuous deployment (CI/CD) of Airflow DAGs (Directed Acyclic Graphs), ensuring efficient development and deployment of workflows.

The Apache Airflow community is so large and active that it’s tempting to take the view that “if it ain’t broke don’t fix it.” In a community as in a codebase, however, improvement and attention are essential to sustaining growth. And bugs are just as inevitable in community management as they are in software development. If only the fixes were, too! Airflow is large and growing because users love Airflow and our community. But what steps could be taken to enhance the typical user’s and developer’s experience of the community? This talk will provide an overview of potential learnings for Airflow community management efforts, such as project governance and analytics, derived from the speaker’s experience managing the OpenLineage and Marquez open-source communities. The talk will answer questions such as: What can we learn from other open-source communities when it comes to supporting users and developers and learning from them? For example, what options exist for getting historical data out of Slack despite the limitations of the free tier? What tools can be used to make adoption metrics more reliable? What are some effective supplements to asynchronous governance?

Airflow is widely used within Robinhood. In addition to traditional offline analytics use cases (to schedule ingestion and analytics workloads that populate our data lake), we also use Airflow in our backend services to orchestrate various workflows that are highly critical for the business, e.g: compliance and regulatory reporting, user facing reports and more. As part of this, we have evolved what we believe is a unique deployment architecture for Airflow. We have central schedulers that are responsible for workloads from multiple different teams, but the workflow tasks themselves run on workers owned by respective teams that are highly coupled with their backend services and codebase. Furthermore, Robinhood augmented Airflow with a bunch of customizations — airflow worker template for Kubernetes, enhanced observability, enhanced SLA detection, and a collection of operators, sensors, and plugins to tailor Airflow to their exact needs. This session is going to walk through how we grew our architecture and adapted Airflow to fit Robinhood’s variety of needs and use cases.

The team gives their impressions of Thursday night’s Presidential debate (hint: it involves existential dread). Then, the focus switches to the latest inflation data. Matt Colyar joins to give an overview of May’s encouraging PCE deflator report and what it implies for Fed policy. The episode closes with a roundtable discussion about the long and variable lags in which Fed policy effects the economy, and whether that means the central bank should start loosening policy now.   Guest Hosts: Matt Colyar - Assistant Director, Moody's Analytics Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X' @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

A data career is an amazing path. Once you're inside, you have good job prospects, fun and challenging work, constant opportunities to learn, solid comp, and can often work from anywhere.    But it can be hard to break in. Today, in 2024, there seems to be more competition than ever for entry level roles. So how can you get started?    In this episode, we'll be sharing some of the best strategies, actionable advice, and personal anecdotes from two recent career transitioners, Annie Nelson and Ian Klosowicz, who have each inspired and helped countless others.   You'll leave with a concrete path to landing your first role, and succeeding on the job once you're there. What You'll Learn: The skills you should be focusing on and showcasing Practical advice for networking, finding jobs, applying, and nailing the interview Tips for using your first 90 days in role to set up your career trajectory   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   About our guests: Annie Nelson (Annie's Analytics) is a leading data analytics and business intelligence expert. She excels in transforming raw data into actionable insights, making complex data concepts accessible. Annie shares her expertise through speaking engagements, online content, and consulting, helping organizations leverage data to achieve their goals. Check out Annie's book: How to Become a Data Analyst Follow Annie on LinkedIn  

Ian Klosowicz is a seasoned data analyst dedicated to shaping the next generation of data analysts, with a proven track record of teaching people a roadmap that works for them. He specializes in guiding aspiring data analysts on their journey to securing their first roles in the dynamic world of data analytics. Subscribe to Ian's newsletter Follow Ian on LinkedIn   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

Unlock the potential of the Elastic Stack with the "Elastic Stack 8.x Cookbook." This book provides over 80 hands-on recipes, guiding you through ingesting, processing, and visualizing data using Elasticsearch, Logstash, Kibana, and more. You'll also explore advanced features like machine learning and observability to create data-driven applications with ease. What this Book will help me do Implement a robust workflow for ingesting, transforming, and visualizing diverse datasets. Utilize Kibana to create insightful dashboards and visual analytics. Leverage Elastic Stack's AI capabilities, such as natural language processing and machine learning. Develop search solutions and integrate advanced features like vector search. Monitor and optimize your Elastic Stack deployments for performance and security. Author(s) Huage Chen and Yazid Akadiri are experienced professionals in the field of Elastic Stack. They bring years of practical experience in data engineering, observability, and software development. Huage and Yazid aim to provide a clear, practical pathway for both beginners and experienced users to get the most out of the Elastic Stack's capabilities. Who is it for? This book is perfect for developers, data engineers, and observability practitioners looking to harness the power of Elastic Stack. It caters to both beginners and experts, providing clear instructions to help readers understand and implement powerful data solutions. If you're working with search applications, data analysis, or system observability, this book is an ideal resource.

The Data Product Management In Action podcast, brought to you by Soda and executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. In Season 01, Episode 005, host Nadiem von Heydebrand (CEO and Co-founder at Mindfuel) sits down with Clemence Chee (VP of Data and Analytics at Babbel). Clemence shares his journey and the unique challenges of data product managment, and the critical role of creating tangible business value and Return On Investment.  About our host Nadiem von Heydebrand: Nadiem is the CEO and Co-Founder of Mindfuel. In 2019, he merged his passion for data science with product management, becoming a thought leader in data product management. Nadiem is dedicated to demonstrating the true value contribution of data. With over a decade of experience in the data industry, Nadiem leverages his expertise to scale data platforms, implement data mesh concepts, and transform AI performance into business performance, delighting consumers at global organizations that include Volkswagen, Munich Re, Allianz, Red Bull, and Vorwerk. Connect with Nadiem on LinkedIn.

About our guest Clemence Chee: With over 10 years as a data and technology enthusiast, Clemence has extensive experience in Venture Development, Operations, and Business Intelligence. Prior to his current role at VP Data & Analytics at Babbel, he spent 7 years at HelloFresh as Global Senior Director of Data and has been fortunate to contribute to and build companies from ideation through pre-seed, Series A-D, IPO, and DAX40. Connect with Clemence on LinkedIn. All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else.  Join the conversation on LinkedIn  #dataproductmanagementwednesday

Whether big or small, one of the biggest challenges organizations face when they want to work with data effectively is often lack of access to it. This is where building a data platform comes in. But building a data platform is no easy feat. It's not just about centralizing data in the data warehouse, it’s also about making sure that data is actionable, trustable and usable. So, how do you make sure your data platform is up to par? Shuang Li is Group Product Manager at Box. With experience of building data, analytics, ML, and observability platform products for both external and internal customers, Shuang is always passionate about the insights, optimizations, and predictions that big data and AI/ML make possible. Throughout her career, she transitioned from academia to engineering, from engineering to product management, and then from an individual contributor to an emerging product executive. In the episode, Adel and Shuang explore her career journey, including transitioning from academia to engineering and helping to work on Google Fiber, how to build a data platform, ingestion pipelines, processing pipelines, challenges and milestones in building a data platform, data observability and quality, developer experience, data democratization, future trends and a lot more.  Links Mentioned in the Show: BoxConnect with Shuang on Linkedin[Course] Understanding Modern Data ArchitectureRelated Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of Alteryx New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Avery chats with Trevor Maxwell, who transformed his career from an industrial printer technician and construction worker to a remote technical business analyst.

Learn how Trevor leveraged the Data Career Podcast and a data bootcamp to break into the data industry despite having no prior tech experience or degree.

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(06:55) The Importance of Networking and Consistency (11:23) The Value of Hands-On Projects and Writing (19:32) The Power of Small Steps (25:06) Interview Preparation and Techniques

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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