A hands-on roadmap to using Python for artificial intelligence programming In Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history. The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simple and plain language and with illustrative code examples. Divided into three parts, the author explains artificial intelligence generally, machine learning, and deep learning. It tackles a wide variety of useful topics, from classification and regression in machine learning to generative adversarial networks. He also includes: Fulsome introductions to MATLAB, Python, AI, machine learning, and deep learning Expansive discussions on supervised and unsupervised machine learning, as well as semi-supervised learning Practical AI and Python “cheat sheet” quick references This hands-on AI programming guide is perfect for anyone with a basic knowledge of programming—including familiarity with variables, arrays, loops, if-else statements, and file input and output—who seeks to understand foundational concepts in AI and AI development.

Summary Data governance is a practice that requires a high degree of flexibility and collaboration at the organizational and technical levels. The growing prominence of cloud and hybrid environments in data management adds additional stress to an already complex endeavor. Privacera is an enterprise grade solution for cloud and hybrid data governance built on top of the robust and battle tested Apache Ranger project. In this episode Balaji Ganesan shares how his experiences building and maintaining Ranger in previous roles helped him understand the needs of organizations and engineers as they define and evolve their data governance policies and practices.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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 $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! This episode is brought to you by Acryl Data, the company behind DataHub, the leading developer-friendly data catalog for the modern data stack. Open Source DataHub is running in production at several companies like Peloton, Optum, Udemy, Zynga and others. Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product at dataengineeringpodcast.com/acryl RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. The most important piece of any data project is the data itself, which is why it is critical that your data source is high quality. PostHog is your all-in-one product analytics suite including product analysis, user funnels, feature flags, experimentation, and it’s open source so you can host it yourself or let them do it for you! You have full control over your data and their plugin system lets you integrate with all of your other data tools, including data warehouses and SaaS platforms. Give it a try today with their generous free tier at dataengineeringpodcast.com/posthog Your host is Tobias Macey and today I’m interviewing Balaji Ganesan about his work at Privacera and his view on the state of data governance, access control, and security in the cloud

Interview

Introduction How did you get involved in the area of data management? Can you describe what Privacera is and the story behind it? What is your working definition of "data governance" and how does that influence your product focus and priorities? What are some of the lessons that you learned from your work on Apache Ranger that helped with your efforts at Privacera? How would you characterize your position in the market for data governance/data security tools? What are the unique constraints and challenges that come into play when managing data in cloud platforms? Can you explain how the Privacera platform is architected?

How have the design and goals of the system changed or evolved since you started working on it?

What is the workflow for an operator integrating Privacera into a data platform?

How do you provide feedback to users about the level of coverage for discovered data assets?

How does Privacera fit into the workflow of the different personas working with data?

What are some of the security and privacy controls that Privacera introduces?

How do you mitigate the potential for anyone to bypass Privacera’s controls by interacting directly with the underlying systems? What are the most interesting, innovative, or unexpected ways that you have seen Privacera used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Privacera? When is Privacera the wrong choice? What do you have planned for the future of Privacera?

Contact Info

LinkedIn @Balaji_Blog on Twitter

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 show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. 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 iTunes and tell your friends and co-workers

Links

Privacera Hadoop Hortonworks Apache Ranger Oracle Teradata Presto/Trino Starburst

Podcast Episode

Ahana

Podcast Episode

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Sponsored By: Acryl:

The modern data stack needs a reimagined metadata management platform. Acryl Data’s vision is to bring clarity to your data through its next generation multi-cloud metadata management platform. Founded by the leaders that created projects like LinkedIn DataHub and Airbnb Dataportal, Acryl Data enables delightful search and discovery, data observability, and federated governance across data ecosystems. Signup for the SaaS product today at dataengineeringpodcast.com/acrylSupport Data Engineering Podcast

Simplify Big Data Analytics with Amazon EMR is a thorough guide to harnessing Amazon's EMR service for big data processing and analytics. From distributed computation pipelines to real-time streaming analytics, this book provides hands-on knowledge and actionable steps for implementing data solutions efficiently. What this Book will help me do Understand the architecture and key components of Amazon EMR and how to deploy it effectively. Learn to configure and manage distributed data processing pipelines using Amazon EMR. Implement security and data governance best practices within the Amazon EMR ecosystem. Master batch ETL and real-time analytics techniques using technologies like Apache Spark. Apply optimization and cost-saving strategies to scalable data solutions. Author(s) Sakti Mishra is a seasoned data professional with extensive expertise in deploying scalable analytics solutions on cloud platforms like AWS. With a background in big data technologies and a passion for teaching, Sakti ensures practical insights accompany every concept. Readers will find his approach thorough, hands-on, and highly informative. Who is it for? This book is perfect for data engineers, data scientists, and other professionals looking to leverage Amazon EMR for scalable analytics. If you are familiar with Python, Scala, or Java and have some exposure to Hadoop or AWS ecosystems, this book will empower you to design and implement robust data pipelines efficiently.

Dive into the world of reproducible data science with Pachyderm, a specialized platform designed for version-controlled data pipelines. By following this book, 'Reproducible Data Science with Pachyderm,' you'll gain the skills to implement robust, scalable machine learning workflows with Pachyderm 2.0, covering setup, integration, and advanced use cases. What this Book will help me do Build scalable, version-controlled data pipelines with Pachyderm's unique features. Understand the principles behind reproducible data science and implement them effectively. Deploy Pachyderm on AWS, Google Cloud, and Azure while integrating with popular tools. Create and manage end-to-end machine learning workflows, including hyperparameter tuning. Leverage advanced integrations, such as Pachyderm Notebooks and language clients like Python and Go. Author(s) Svetlana Karslioglu is a seasoned data scientist with extensive experience in constructing scalable machine learning and data processing systems. With years in both practical implementation and educational endeavors, she has a talent for breaking down complex concepts into accessible learning paths. Her approach is hands-on and results-oriented, aimed at empowering professionals to excel in the field of data science. Who is it for? This book is intended for data scientists, machine learning engineers, and data engineers who are keen to ensure reproducibility in their workflows. Ideal readers may have familiarity with data science basics and some exposure to Kubernetes and programming languages like Python. By studying the book, learners will establish confidence in implementing Pachyderm for scalable and reliable data pipelines.

Think big about your data! PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines. In Data Analysis with Python and PySpark you will learn how to: Manage your data as it scales across multiple machines Scale up your data programs with full confidence Read and write data to and from a variety of sources and formats Deal with messy data with PySpark’s data manipulation functionality Discover new data sets and perform exploratory data analysis Build automated data pipelines that transform, summarize, and get insights from data Troubleshoot common PySpark errors Creating reliable long-running jobs Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you’ve learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required. About the Technology The Spark data processing engine is an amazing analytics factory: raw data comes in, insight comes out. PySpark wraps Spark’s core engine with a Python-based API. It helps simplify Spark’s steep learning curve and makes this powerful tool available to anyone working in the Python data ecosystem. About the Book Data Analysis with Python and PySpark helps you solve the daily challenges of data science with PySpark. You’ll learn how to scale your processing capabilities across multiple machines while ingesting data from any source—whether that’s Hadoop clusters, cloud data storage, or local data files. Once you’ve covered the fundamentals, you’ll explore the full versatility of PySpark by building machine learning pipelines, and blending Python, pandas, and PySpark code. What's Inside Organizing your PySpark code Managing your data, no matter the size Scale up your data programs with full confidence Troubleshooting common data pipeline problems Creating reliable long-running jobs About the Reader Written for data scientists and data engineers comfortable with Python. About the Author As a ML director for a data-driven software company, Jonathan Rioux uses PySpark daily. He teaches the software to data scientists, engineers, and data-savvy business analysts. Quotes A clear and in-depth introduction for truly tackling big data with Python. - Gustavo Patino, Oakland University William Beaumont School of Medicine The perfect way to learn how to analyze and master huge datasets. - Gary Bake, Brambles Covers both basic and more advanced topics of PySpark, with a good balance between theory and hands-on. - Philippe Van Bergenl, P² Consulting For beginner to pro, a well-written book to help understand PySpark. - Raushan Kumar Jha, Microsoft

Summary When you think about selecting a database engine for your project you typically consider options focused on serving multiple concurrent users. Sometimes what you really need is an embedded database that is blazing fast for single user workloads. DuckDB is an in-process database engine optimized for OLAP applications to speed up your analytical queries that meets you where you are, whether that’s Python, R, Java, even the web. In this episode, Hannes Mühleisen, co-creator and CEO of DuckDB Labs, shares the motivations for creating the project, the myriad ways that it can be used to speed up your data projects, and the detailed engineering efforts that go into making it adaptable to any environment. This is a fascinating and humorous exploration of a truly useful piece of technology.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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 $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. Your host is Tobias Macey and today I’m interviewing Hannes Mühleisen about DuckDB, an in-process embedded database engine for columnar analytics

Interview

Introduction How did you get involved in the area of data management? Can you describe what DuckDB is and the story behind it? Where did the name come from? What are some of the use cases that DuckDB is designed to support? The interface for DuckDB is similar (at least in spirit) to SQLite. What are the deciding factors for when to use one vs. the other?

How might they be used in concert to take advantage of their relative strengths?

What are some of the ways that DuckDB can be used to better effect than options provided by different language ecosystems? Can you describe how DuckDB is implemented?

How has the design and goals of the project changed or evolved since you began working on it? What are some of the optimizations that you have had to make in order to support performant access to data that exceeds available memory?

Can you describe a typical workflow of incorporating DuckDB into an analytical project? What are some of the libraries/tools/systems that DuckDB might replace in the scope of a project or team? What are some of the

Enterprise networks are large and rely on numerous connected endpoints to ensure smooth operational efficiency. However, they also present a challenge from a security perspective. The focus of this Blueprint is to demonstrate an early threat detection against the network fabric that is powered by Brocade that uses IBM® QRadar®. It also protects the same if a cyberattack or an internal threat by rouge user within the organization occurs. The publication also describes how to configure the syslog that is forwarding on Brocade SAN FOS. Finally, it explains how the forwarded audit events are used for detecting the threat and runs the custom action to mitigate the threat. The focus of this publication is to proactively start a cyber resilience workflow from IBM QRadar to block an IP address when multiple failed logins on Brocade switch are detected. As part of early threat detection, a sample rule that us used by IBM QRadar is shown. A Python script that also is used as a response to block the user's IP address in the switch is provided. Customers are encouraged to create control path or data path use cases, customized IBM QRadar rules, and custom response scripts that are best-suited to their environment. The use cases, QRadar rules, and Python script that are presented here are templates only and cannot be used as-is in an environment.

Machine Learning with PyTorch and Scikit-Learn is a comprehensive resource for developers looking to dive deep into the world of machine learning. It introduces foundational concepts alongside practical implementations using Python and leading libraries such as PyTorch and Scikit-Learn. With well-explained techniques and real-world examples, you'll gain the knowledge needed to design, build, and optimize machine learning systems. What this Book will help me do Understand and apply core concepts in machine learning using Scikit-Learn. Develop and deploy deep learning models using PyTorch efficiently. Configure and optimize neural networks, transformers, and GANs for various applications. Handle and preprocess data effectively for building robust models. Follow best practices for model evaluation, tuning, and deployment. Author(s) Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili are experienced professionals in the field of machine learning with extensive teaching and writing backgrounds. They bring their expertise in Python and machine learning frameworks like PyTorch to provide both theoretical and practical insights helpful for learners. Their combined knowledge ensures a thorough and engaging learning experience suited for aspiring data scientists. Who is it for? This book is tailored for Python developers and data scientists eager to master machine learning and deep learning techniques. If you're familiar with Python programming and possess fundamental knowledge of calculus and linear algebra, you will find this book incredibly insightful. Whether you're entering the field or seeking to enhance your expertise, this resource caters to your professional growth in building advanced machine learning systems.

Summary Python has grown to be one of the top languages used for all aspects of data, from collection and cleaning, to analysis and machine learning. Along with that growth has come an explosion of tools and engines that help power these workflows, which introduces a great deal of complexity when scaling from single machines and exploratory development to massively parallel distributed computation. In answer to that challenge the Fugue project offers an interface to automatically translate across Pandas, Spark, and Dask execution environments without having to modify your logic. In this episode core contributor Kevin Kho explains how the slight differences in the underlying engines can lead to big problems, how Fugue works to hide those differences from the developer, and how you can start using it in your own work today.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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 $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription The only thing worse than having bad data is not knowing that you have it. With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders. With complete API access, a user-friendly interface, and automated yet flexible alerting, you’ve got everything you need to establish and maintain trust in your data. Go to dataengineeringpodcast.com/bigeye today to sign up and start trusting your analyses. Every data project starts with collecting the information that will provide answers to your questions or inputs to your models. The web is the largest trove of information on the planet and Oxylabs helps you unlock its potential. With the Oxylabs scraper APIs you can extract data from even javascript heavy websites. Combined with their residential proxies you can be sure that you’ll have reliable and high quality data whenever you need it. Go to dataengineeringpodcast.com/oxylabs today and use code DEP25 to get your special discount on residential proxies. Your host is Tobias Macey and today I’m interviewing Kevin Kho about Fugue, a library that offers a unified interface for distributed computing that lets users execute Python, pandas, and SQL code on Spark and Dask without rewrites

Interview

Introduction How did you get involved in the area of data management? Can you describe what Fugue is and the story behind it? What are the core goals of the Fugue project? Who are the target users for Fugue and how does that influence the feature priorities and API design? How does Fugue compare to projects such as Modin, etc. for abst

Summary There are many dimensions to the work of protecting the privacy of users in our data. When you need to share a data set with other teams, departments, or businesses then it is of utmost importance that you eliminate or obfuscate personal information. In this episode Will Thompson explores the many ways that sensitive data can be leaked, re-identified, or otherwise be at risk, as well as the different strategies that can be employed to mitigate those attack vectors. He also explains how he and his team at Privacy Dynamics are working to make those strategies more accessible to organizations so that you can focus on all of the other tasks required of you.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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 $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy. The only thing worse than having bad data is not knowing that you have it. With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders. With complete API access, a user-friendly interface, and automated yet flexible alerting, you’ve got everything you need to establish and maintain trust in your data. Go to dataengineeringpodcast.com/bigeye today to sign up and start trusting your analyses. Your host is Tobias Macey and today I’m interviewing Will Thompson about managing data privacy concerns for data sets used in analytics and machine learning

Interview

Introduction How did you get involved in the area of data management? Data privacy is a multi-faceted problem domain. Can you start by enumerating the different categories of privacy concern that are involved in analytical use cases? Can you describe what Privacy Dynamics is and the story behind it?

Which categor(y|ies) are you focused on addressing?

What are some of the best practices in the definition, protection, and enforcement of data privacy policies?

Is there a data security/privacy equivalent to the OWASP top 10?

What are some of the techniques that are available for anonymizing data while maintaining statistical utility/significance?

What are some of the engineering/systems capabilities that are required for data (platform) engineers to incorporate these practices in their platforms?

What are the tradeoffs of encryption vs. obfuscation when anonymizing data? What are some of the types of PII that are non-obvious? What are the risks associated with data re-identification, and what are some of the vectors that might be exploited to achieve that?

How can privacy risks mitigation be maintained as new data sources are introduced that might contribute to these re-identification vectors?

Can you describe how Privacy Dynamics is implemented?

What are the most challenging engineering problems that you are dealing with?

How do you approach validation of a data set’s privacy? What have you found to be useful heuristics for identifying private data?

What are the risks of false positives vs. false negatives?

Can you describe what is involved in integrating the Privacy Dynamics system into an existing data platform/warehouse?

What would be required to integrate with systems such as Presto, Clickhouse, Druid, etc.?

What are the most interesting, innovative, or unexpected ways that you have seen Privacy Dynamics used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Privacy Dynamics? When is Privacy Dynamics the wrong choice? What do you have planned for the future of Privacy Dynamics?

Contact Info

LinkedIn @willseth on Twitter

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 show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. 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 iTunes and tell your friends and co-workers

Links

Privacy Dynamics Pandas

Podcast Episode – Pandas For Data Engineering

Homomorphic Encryption Differential Privacy Immuta

Podcast Episode

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

Summary Pandas is a powerful tool for cleaning, transforming, manipulating, or enriching data, among many other potential uses. As a result it has become a standard tool for data engineers for a wide range of applications. Matt Harrison is a Python expert with a long history of working with data who now spends his time on consulting and training. He recently wrote a book on effective patterns for Pandas code, and in this episode he shares advice on how to write efficient data processing routines that will scale with your data volumes, while being understandable and maintainable.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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 $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy. The only thing worse than having bad data is not knowing that you have it. With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders. With complete API access, a user-friendly interface, and automated yet flexible alerting, you’ve got everything you need to establish and maintain trust in your data. Go to dataengineeringpodcast.com/bigeye today to sign up and start trusting your analyses. Your host is Tobias Macey and today I’m interviewing Matt Harrison about useful tips for using Pandas for data engineering projects

Interview

Introduction How did you get involved in the area of data management? What are the main tasks that you have seen Pandas used for in a data engineering context? What are some of the common mistakes that can lead to poor performance when scaling to large data sets? What are some of the utility features that you have found most helpful for data processing? One of the interesting add-ons to Pandas is its integration with Arrow. What are some of the considerations for how and when to use the Arrow capabilities vs. out-of-the-box Pandas? Pandas is a tool that spans data processing and data science. What are some of the ways that data engineers should think about writing their code to make it accessible to data scientists for supporting collaboration across data workflows? Pandas is often used for transformation logic. What are some of the ways that engineers should approach the design of their code to make it understandable and maint

Discover valuable machine learning techniques you can understand and apply using just high-school math. In Grokking Machine Learning you will learn: Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. About the Technology Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations. About the Book Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you’ll build interesting projects with Python, including models for spam detection and image recognition. You’ll also pick up practical skills for cleaning and preparing data. What's Inside Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets About the Reader For readers who know basic Python. No machine learning knowledge necessary. About the Author Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple. Quotes Did you think machine learning is complicated and hard to master? It’s not! Read this book! Serrano demystifies some of the best-held secrets of the machine learning society. - Sebastian Thrun, Founder, Udacity The first step to take on your machine learning journey. - Millad Dagdoni, Norwegian Labour and Welfare Administration A nicely written guided introduction, especially for those who want to code but feel shaky in their mathematics. - Erik D. Sapper, California Polytechnic State University The most approachable introduction to machine learning I’ve had the pleasure to read in recent years. Highly recommended. - Kay Engelhardt, devstats

Learn the core aspects of NumPy, Matplotlib, and Pandas, and use them to write programs with Python 3. This book focuses heavily on various data visualization techniques and will help you acquire expert-level knowledge of working with Matplotlib, a MATLAB-style plotting library for Python programming language that provides an object-oriented API for embedding plots into applications. You'll begin with an introduction to Python 3 and the scientific Python ecosystem. Next, you'll explore NumPy and ndarray data structures, creation routines, and data visualization. You'll examine useful concepts related to style sheets, legends, and layouts, followed by line, bar, and scatter plots. Chapters then cover recipes of histograms, contours, streamplots, and heatmaps, and how to visualize images and audio with pie and polar charts. Moving forward, you'll learn how to visualize with pcolor, pcolormesh, and colorbar, and how to visualize in 3D in Matplotlib, create simple animations, and embed Matplotlib with different frameworks. The concluding chapters cover how to visualize data with Pandas and Matplotlib, Seaborn, and how to work with the real-life data and visualize it. After reading Hands-on Matplotlib you'll be proficient with Matplotlib and able to comfortably work with ndarrays in NumPy and data frames in Pandas. What You'll Learn Understand Data Visualization and Python using Matplotlib Review the fundamental data structures in NumPy and Pandas Work with 3D plotting, visualizations, and animations Visualize images and audio data Who This Book Is For Data scientists, machine learning engineers and software professionals with basic programming skills.

Dive into the world of advanced analytics and visualizations in Power BI with "Extending Power BI with Python and R". This comprehensive guide will teach you how to integrate Python and R scripting into your Power BI projects, allowing you to build data models, transform data, and create rich visualizations. Learn practical techniques to make your Power BI dashboards more interactive and insightful. What this Book will help me do Master the integration of Python and R scripts into Power BI to enhance its functionality. Learn to implement advanced data transformations and enrichments using external APIs. Create advanced visualizations and custom visuals with R for improved analytics. Perform advanced data analysis including handling missing data using Python and R. Leverage machine learning techniques within Power BI projects to extract actionable insights. Author(s) None Zavarella is a data science expert and renowned author specializing in data analytics and visualization tools. With years of experience working with Power BI, Python, and R in diverse data-driven projects, Zavarella offers a unique perspective on enhancing Power BI capabilities. Passionate about teaching, they craft clear and impactful tutorials for learners. Who is it for? This book is perfect for business intelligence professionals, data scientists, and business analysts who already use Power BI and want to augment its features with Python and R. If you have a foundational understanding of Power BI and some basic familiarity with Python and R, this book will help you explore their combined potential for advanced analytics.

Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science. In Data Science Bookcamp you will learn: Techniques for computing and plotting probabilities Statistical analysis using Scipy How to organize datasets with clustering algorithms How to visualize complex multi-variable datasets How to train a decision tree machine learning algorithm In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. About the Technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the Book Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results. What's Inside Web scraping Organize datasets with clustering algorithms Visualize complex multi-variable datasets Train a decision tree machine learning algorithm About the Reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the Author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Quotes Valuable and accessible… a solid foundation for anyone aspiring to be a data scientist. - Amaresh Rajasekharan, IBM Corporation Really good introduction of statistical data science concepts. A must-have for every beginner! - Simone Sguazza, University of Applied Sciences and Arts of Southern Switzerland A full-fledged tutorial in data science including common Python libraries and language tricks! - Jean-François Morin, Laval University This book is a complete package for understanding how the data science process works end to end. - Ayon Roy, Internshala

Printed in full color! Unlock the groundbreaking advances of deep learning with this extensively revised new edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world. In Deep Learning with Python, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Timeseries forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Printed in full color throughout Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised full color second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You’ll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks. About the Technology Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is quickly becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach—even if you have no background in mathematics or data science. This book shows you how to get started. About the Book Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you’ll build your understanding through intuitive explanations, crisp color illustrations, and clear examples. You’ll quickly pick up the skills you need to start developing deep-learning applications. What's Inside Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Printed in full color throughout About the Reader For readers with intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet is a software engineer at Google and creator of the Keras deep-learning library. Quotes Chollet is a master of pedagogy and explains complex concepts with minimal fuss, cutting through the math with practical Python code. He is also an experienced ML researcher and his insights on various model architectures or training tips are a joy to read. - Martin Görner, Google Immerse yourself into this exciting introduction to the topic with lots of real-world examples. A must-read for every deep learning practitioner. - Sayak Paul, Carted The modern classic just got better. - Edmon Begoli, Oak Ridge National Laboratory Truly the bible of deep learning. - Yiannis Paraskevopoulos, University of West Attica

Dive into the world of scalable data processing with 'Essential PySpark for Scalable Data Analytics'. This book is a comprehensive guide that helps beginners understand and utilize PySpark to process, analyze, and draw insights from large datasets effectively. With hands-on tutorials and clear explanations, you will gain the confidence to tackle big data analytics challenges. What this Book will help me do Understand and apply the distributed computing paradigm for big data. Learn to perform scalable data ingestion, cleansing, and preparation using PySpark. Create and utilize data lakes and the Lakehouse paradigm for efficient data storage and access. Develop and deploy machine learning models with scalability in mind. Master real-time analytics pipelines and create impactful data visualizations. Author(s) None Nudurupati is an experienced data engineer and educator, specializing in distributed systems and big data technologies. With years of practical experience in the field, None brings a clear and approachable teaching style to technical topics. Passionate about empowering readers, the author has designed this book to be both practical and inspirational for aspiring data practitioners. Who is it for? This book is ideal for data professionals including data scientists, engineers, and analysts looking to scale their data analytics processes. It assumes familiarity with basic data science concepts and Python, as well as some experience with SQL-like data analysis. This is particularly suitable for individuals aiming to expand their knowledge in distributed computing and PySpark to handle big data challenges. Achieving scalable and efficient data solutions is at the core of this guide.

Data Engineering with Apache Spark, Delta Lake, and Lakehouse is a comprehensive guide packed with practical knowledge for building robust and scalable data pipelines. Throughout this book, you will explore the core concepts and applications of Apache Spark and Delta Lake, and learn how to design and implement efficient data engineering workflows using real-world examples. What this Book will help me do Master the core concepts and components of Apache Spark and Delta Lake. Create scalable and secure data pipelines for efficient data processing. Learn best practices and patterns for building enterprise-grade data lakes. Discover how to operationalize data models into production-ready pipelines. Gain insights into deploying and monitoring data pipelines effectively. Author(s) None Kukreja is a seasoned data engineer with over a decade of experience working with big data platforms. He specializes in implementing efficient and scalable data solutions to meet the demands of modern analytics and data science. Writing with clarity and a practical approach, he aims to provide actionable insights that professionals can apply to their projects. Who is it for? This book is tailored for aspiring data engineers and data analysts who wish to delve deeper into building scalable data platforms. It is suitable for those with basic knowledge of Python, Spark, and SQL, and seeking to learn Delta Lake and advanced data engineering concepts. Readers should be eager to develop practical skills for tackling real-world data engineering challenges.

This comprehensive guide to FastAPI walks readers through developing modern web backends optimized for data science applications. By mastering key concepts like dependency injection and asynchronous programming, you will create high-performing REST APIs and machine learning powered systems. What this Book will help me do Master asynchronous programming and type hinting in Python for efficient coding. Design comprehensive RESTful APIs for machine learning with FastAPI. Build, test, and maintain scalable data science applications. Integrate Python libraries like NumPy and scikit-learn into web backends. Deploy modular and efficient FastAPI-backed systems to production. Author(s) None Voron is a seasoned software developer specialized in web frameworks and data science applications. With a strong background in building scalable systems, they bring invaluable insights on utilizing FastAPI. Voron emphasizes clarity and hands-on learning, sharing their expertise to help developers master the technology efficiently. Who is it for? This book is ideal for data scientists and Python developers interested in creating efficient data science backends. If you have groundwork knowledge of machine learning concepts and Python programming, this book will enhance your ability to deploy and manage APIs for data-driven applications.

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Abstract Hosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts. This week on Making Data Simple, we have Davit Buniatyan. Davit is founding CEO of Activeloop, he started his PhD at Princeton University, his research involved reconstructing the connectome of the mouse brain. In this research he dealt with large-scale unstructured data which was extremely expensive (amounting to millions of dollars) to manage. Later on, he realized that this problem is a real pain point, not only in the lab setting but also for many companies across industries. This made him think of a radically more efficient, and a machine-learning native way to work with data. The idea of changing how an ML team can create and manage datasets got him into Y Combinator, where he started Activeloop, a startup that has attracted the investment of prominent Silicon Valley VC firms and angel investors, and the attention of the open-source community, with the framework trending number 1 in Python on GitHub worldwide earlier this year. Show Notes 2:05 – Davit’s experience 6:44 – What is your success criteria in the mouse connectome? 8:44 – What did you learn from this? 10:00 – Could this solve ALS? 13:19 – What is the problem you’re solving 17:17 – How do you prepare the data? 24:00 – Why are the naysayers wrong? 25:21 – What is the name of the technology? 31:19 – What problem have you not solved? 37:44 – What keeps you up at night? 38:42 – How are you finding talent?  41:06 – What do you do for fun? Activeloop Activeloop - Twitter Davit Buniatyan - LinkedIn Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.