Understand and implement big data analysis solutions in pandas with an emphasis on performance. This book strengthens your intuition for working with pandas, the Python data analysis library, by exploring its underlying implementation and data structures. Thinking in Pandas introduces the topic of big data and demonstrates concepts by looking at exciting and impactful projects that pandas helped to solve. From there, you will learn to assess your own projects by size and type to see if pandas is the appropriate library for your needs. Author Hannah Stepanek explains how to load and normalize data in pandas efficiently, and reviews some of the most commonly used loaders and several of their most powerful options. You will then learn how to access and transform data efficiently, what methods to avoid, and when to employ more advanced performance techniques. You will also go over basic data access and munging in pandas and the intuitive dictionary syntax. Choosing the right DataFrame format, working with multi-level DataFrames, and how pandas might be improved upon in the future are also covered. By the end of the book, you will have a solid understanding of how the pandas library works under the hood. Get ready to make confident decisions in your own projects by utilizing pandas—the right way. What You Will Learn Understand the underlying data structure of pandas and why it performs the way it does under certain circumstances Discover how to use pandas to extract, transform, and load data correctly with an emphasis on performance Choose the right DataFrame so that the data analysis is simple and efficient. Improve performance of pandas operations with other Python libraries Who This Book Is For Software engineers with basic programming skills in Python keen on using pandas for a big data analysis project. Python software developers interested in big data.

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that "learn" from data Unsupervised learning methods for extracting meaning from unlabeled data

Summary Modern applications frequently require access to real-time data, but building and maintaining the systems that make that possible is a complex and time consuming endeavor. Eventador is a managed platform designed to let you focus on using the data that you collect, without worrying about how to make it reliable. In this episode Eventador Founder and CEO Kenny Gorman describes how the platform is architected, the challenges inherent to managing reliable streams of data, the simplicity offered by a SQL interface, and the interesting projects that his customers have built on top of it. This was an interesting inside look at building a business on top of open source stream processing frameworks and how to reduce the burden on end users.

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 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Your host is Tobias Macey and today I’m interviewing Kenny Gorman about the Eventador streaming SQL platform

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what the Eventador platform is and the story behind it?

How has your experience at ObjectRocket influenced your approach to streaming SQL? How do the capabilities and developer experience of Eventador compare to other streaming SQL engines such as ksqlDB, Pulsar SQL, or Materialize?

What are the main use cases that you are seeing people use for streaming SQL?

How does it fit into an application architecture? What are some of the design changes in the different layers that are necessary to take advantage of the real time capabilities?

Can you describe how the Eventador platform is architected?

How has the system design evolved since you first began working on it? How has the overall landscape of streaming systems changed since you first began working on Eventador? If you were to start over today what would you do differently?

What are some of the most interesting and challenging operational aspects of running your platform? What are some of the ways that you have modified or augmented the SQL dialect that you support?

What is the tipping point for when SQL is insufficient for a given task and a user might want to leverage Flink?

What is the workflow for developing and deploying different SQL jobs?

How do you handle versioning of the queries and integration with the software development lifecycle?

What are some data modeling considerations that users should be aware of?

What are some of the sharp edges or design pitfalls that users should be aware of?

What are some of the most interesting, innovative, or unexpected ways that you have seen your customers use your platform? What are some of the most interesting, unexpected, or challenging lessons that you have learned in the process of building and scaling Eventador? What do you have planned for the future of the platform?

Contact Info

LinkedIn Blog @kennygorman on Twitter kgorman 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 t

Summary The software applications that we build for our businesses are a rich source of data, but accessing and extracting that data is often a slow and error-prone process. Rookout has built a platform to separate the data collection process from the lifecycle of your code. In this episode, CTO Liran Haimovitch discusses the benefits of shortening the iteration cycle and bringing non-engineers into the process of identifying useful data. This was a great conversation about the importance of democratizing the work of data collection.

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 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Your host is Tobias Macey and today I’m interviewing Liran Haimovitch, CTO of Rookout, about the business value of operations metrics and other dark data in your organization

Interview

Introduction How did you get involved in the area of data management? Can you start by describing the types of data that we typically collect for the systems operations context?

What are some of the business questions that can be answered from these data sources?

What are some of the considerations that developers and operations engineers need to be aware of when they are defining the collection points for system metrics and log messages?

What are some effective strategies that you have found for including business stake holders in the process of defining these collection points?

One of the difficulties in building useful analyses from any source of data is maintaining the appropriate context. What are some of the necessary metadata that should be maintained along with operational metrics?

What are some of the shortcomings in the systems we design and use for operational data stores in terms of making the collected data useful for other purposes?

How does the existing tooling need to be changed or augmented to simplify the collaboration between engineers and stake holders for defining and collecting the needed information? The types of systems that we use for collecting and analyzing operations metrics are often designed and optimized for different access patterns and data formats than those used for analytical and exploratory purposes. What are your thoughts on how to incorporate the collected metrics with behavioral data? What are some of the other sources of dark data that we should keep an eye out for in our organizations?

Contact Info

LinkedIn @Liran_Last 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 Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

Rookout Cybersecurity DevOps DataDog Graphite Elasticsearch Logz.io Kafka

The intro and o

With Interactive Data Visualization with Python, you will learn to turn raw data into compelling, interactive visual stories. This book guides you through the practical uses of Python libraries such as Bokeh and Plotly, teaching you skills to create visualizations that captivate and inform. What this Book will help me do Understand and apply different principles and techniques of interactive data visualization to bring your data to life. Master the use of libraries like Matplotlib, Seaborn, Altair, and Bokeh for creating a variety of data visualizations. Learn how to customize data visualizations effectively to meet the needs of different audiences and use cases. Gain proficiency in using advanced tools like Plotly for creating dynamic and engaging visual presentations. Acquire the ability to identify common pitfalls in visualization and learn strategies to avoid them, ensuring clarity and impact. Author(s) Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, and Anshu Kumar are experts in Python programming and data visualization with years of experience in data science and software development. They have collaborated to blend their knowledge into this book-a clear and practical guide to mastering interactive visualization with Python. Who is it for? This book is perfect for Python developers, data analysts, and data scientists who want to enhance their skills in data presentation. If you are ready to transform complex data into digestible and interactive visuals, this book is for you. A basic familiarity with Python programming and libraries like pandas is recommended. By the end of the book, you'll feel confident in creating professional-grade data visualizations.

Imagine what you could do if scalability wasn't a problem. With this hands-on guide, you’ll learn how the Cassandra database management system handles hundreds of terabytes of data while remaining highly available across multiple data centers. This third edition—updated for Cassandra 4.0—provides the technical details and practical examples you need to put this database to work in a production environment. Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra’s nonrelational design, with special attention to data modeling. If you’re a developer, DBA, or application architect looking to solve a database scaling issue or future-proof your application, this guide helps you harness Cassandra’s speed and flexibility. Understand Cassandra’s distributed and decentralized structure Use the Cassandra Query Language (CQL) and cqlsh—the CQL shell Create a working data model and compare it with an equivalent relational model Develop sample applications using client drivers for languages including Java, Python, and Node.js Explore cluster topology and learn how nodes exchange data

The 'Pandas 1.x Cookbook' offers a recipe-based guide for mastering the powerful Python library, pandas. You will gain practical knowledge for handling and manipulating data efficiently, from the fundamentals to advanced techniques. The book is an essential resource for exploring and analyzing datasets with pandas. What this Book will help me do Understand and apply data exploration techniques in pandas. Use pandas to manipulate, aggregate, and clean datasets to extract meaningful insights. Combine pandas with Matplotlib and Seaborn to create effective visualizations. Perform time series analysis and transform datasets for machine learning. Implement workflows for handling large-scale data that exceeds your computer's memory. Author(s) Matthew Harrison and Theodore Petrou are highly experienced educators and practitioners in data science and Python programming. With their extensive expertise in using pandas, they provide insights through practical exercises and approachable narratives. Their aim is to make complex concepts accessible to learners of varying skill levels. Who is it for? This book is ideal for Python programmers, analysts, and data scientists seeking to expand their data handling and analysis capabilities. It caters to both beginners who are new to pandas and those looking to deepen their understanding of its advanced features. If your goal is to explore, clean, and analyze complex datasets efficiently, this book is tailored for you.

Summary One of the biggest challenges in building reliable platforms for processing event pipelines is managing the underlying infrastructure. At Snowplow Analytics the complexity is compounded by the need to manage multiple instances of their platform across customer environments. In this episode Josh Beemster, the technical operations lead at Snowplow, explains how they manage automation, deployment, monitoring, scaling, and maintenance of their streaming analytics pipeline for event data. He also shares the challenges they face in supporting multiple cloud environments and the need to integrate with existing customer systems. If you are daunted by the needs of your data infrastructure then it’s worth listening to how Josh and his team are approaching the problem.

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 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Josh Beemster about how Snowplow manages deployment and maintenance of their managed service in their customer’s cloud accounts.

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of the components in your system architecture and the nature of your managed service? What are some of the challenges that are inherent to private SaaS nature of your managed service? What elements of your system require the most attention and maintenance to keep them running properly? Which components in the pipeline are most subject to variability in traffic or resource pressure and what do you do to ensure proper capacity? How do you manage deployment of the full Snowplow pipeline for your customers?

How has your strategy for deployment evolved since you first began Soffering the managed service? How has the architecture of the pipeline evolved to simplify operations?

How much customization do you allow for in the event that the customer has their own system that they want to use in place of one of your supported components?

What are some of the common difficulties that you encounter when working with customers who need customized components, topologies, or event flows?

How does that reflect in the tooling that you use to manage their deployments?

What types of metrics do you track and what do you use for monitoring and alerting to ensure that your customers pipelines are running smoothly? What are some of the most interesting/unexpected/challenging lessons that you have learned in the process of working with and on Snowplow? What are some lessons that you can generalize for management of data infrastructure more broadly? If you could start over with all of Snowplow and the infrastructure automation for it today, what would you do differently? What do you have planned for the future of the Snowplow product and infrastructure management?

Contact Info

LinkedIn jbeemster on GitHub @jbeemster1 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 Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

Snowplow Analytics

Podcast Episode

Terraform Consul Nomad Meltdown Vulnerability Spectre Vulnerability AWS Kinesis Elasticsearch SnowflakeDB Indicative S3 Segment AWS Cloudwatch Stackdriver Apache Kafka Apache Pulsar Google Cloud PubSub AWS SQS AWS SNS AWS Redshift Ansible AWS Cloudformation Kubernetes AWS EMR

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

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Fala, Data Hackers! Seja bem-vindo a mais um episódio do podcast de Ciência de Dados da maior comunidade de Data Science do Brasil-sil-sil! No episódio de hoje falaremos de uma das melhores amigas da área de dados: a Estatística!

No episódio de hoje, convidamos os Estatísticos Luciana Lima — Head de Analytics na A3Data — e André Calaça — Co-fundador da Oper — para falar sobre como eles trabalham com Data Science, como Cientistas de Dados podem aprender Estatísticas, como Estatísticos podem se tornar Cientistas de Dados, e se Python é melhor que R mesmo.

Summary Every business collects data in some fashion, but sometimes the true value of the collected information only comes when it is combined with other data sources. Data trusts are a legal framework for allowing businesses to collaboratively pool their data. This allows the members of the trust to increase the value of their individual repositories and gain new insights which would otherwise require substantial effort in duplicating the data owned by their peers. In this episode Tom Plagge and Greg Mundy explain how the BrightHive platform serves to establish and maintain data trusts, the technical and organizational challenges they face, and the outcomes that they have witnessed. If you are curious about data sharing strategies or data collaboratives, then listen now to learn more!

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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Tom Plagge and Gregory Mundy about BrightHive, a platform for building data trusts

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what a data trust is?

Why might an organization want to build one?

What is BrightHive and what is its origin story? Beyond having a storage location with access controls, what are the components of a data trust that are necessary for them to be viable? What are some of the challenges that are common in establishing an agreement among organizations who are participating in a data trust?

What are the responsibilities of each of the participants in a data trust? For an individual or organization who wants to participate in an existing trust, what is involved in gaining access?

How does BrightHive support the process of building a data trust? How is ownership of derivative data sets/data products and associated intellectual property handled in the context of a trust? How is the technical architecture of BrightHive implemented and how has it evolved since it first started? What are some of the ways that you approach the challenge of data privacy in these sharing agreements? What are some legal and technical guards that you implement to encourage ethical uses of the data contained in a trust? What is the motivation for releasing the technical elements of BrightHive as open source? What are some of the most interesting, innovative, or inspirational ways that you have seen BrightHive used? Being a shared platform for empowering other organizations to collaborate I imagine there is a strong focus on long-term sustainability. How are you approaching that problem and what is the business model for BrightHive? What have you found to be the most interesting/unexpected/challenging aspects of building and growing the technical and business infrastructure of BrightHive? What do you have planned for the future of BrightHive?

Contact Info

Tom

LinkedIn tplagge on GitHub

Gregory

LinkedIn gregmundy on GitHub @graygoree 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 Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

BrightHive Data Science For Social Good Workforce Data Initiative NASA NOAA Data Trust Data Collaborative Public Benefit Corporation Terraform Airflow

Podcast.init Episode

Dagster

Podcast Episode

Secure Multi-Party Computation Public Key Encryption AWS Macie Blockchain Smart Contracts

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

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The Data Science Workshop is designed for beginners looking to step into the rigorous yet rewarding world of data science. By leveraging a hands-on approach, this book demystifies key concepts and guides you gently into creating practical machine learning models with Python. What this Book will help me do Understand supervised and unsupervised learning and their applications. Gain hands-on experience with Python libraries like scikit-learn and pandas for data manipulation. Learn practical use cases of machine learning techniques such as regression and clustering. Discover techniques to ensure robustness in machine learning with hyperparameter tuning and ensembling. Develop efficiency in feature engineering with automated tools to accelerate workflows. Author(s) Anthony So None, Thomas Joseph, Robert Thas John, and Andrew Worsley are seasoned experts in data science and Python programming. Along with Dr. Samuel Asare None, they bring decades of experience and practical knowledge to this book, delivering an engaging and approachable learning experience. Who is it for? This book is targeted toward individuals who are beginners in data science and are eager to acquire foundational knowledge and practical skills. It appeals to those who prefer a structured, hands-on approach to learning, possibly having some prior programming experience or interest in Python. Professionals aspiring to pivot into data-oriented roles or students aiming to strengthen their understanding of data science concepts will find this book particularly valuable. If you're looking to gain confidence in implementing data science projects and solving real-world problems, this text is for you.

Modern data science solutions need to be clean, easy to read, and scalable. In Mastering Large Datasets with Python, author J.T. Wolohan teaches you how to take a small project and scale it up using a functionally influenced approach to Python coding. You’ll explore methods and built-in Python tools that lend themselves to clarity and scalability, like the high-performing parallelism method, as well as distributed technologies that allow for high data throughput. The abundant hands-on exercises in this practical tutorial will lock in these essential skills for any large-scale data science project. About the Technology Programming techniques that work well on laptop-sized data can slow to a crawl—or fail altogether—when applied to massive files or distributed datasets. By mastering the powerful map and reduce paradigm, along with the Python-based tools that support it, you can write data-centric applications that scale efficiently without requiring codebase rewrites as your requirements change. About the Book Mastering Large Datasets with Python teaches you to write code that can handle datasets of any size. You’ll start with laptop-sized datasets that teach you to parallelize data analysis by breaking large tasks into smaller ones that can run simultaneously. You’ll then scale those same programs to industrial-sized datasets on a cluster of cloud servers. With the map and reduce paradigm firmly in place, you’ll explore tools like Hadoop and PySpark to efficiently process massive distributed datasets, speed up decision-making with machine learning, and simplify your data storage with AWS S3. What's Inside An introduction to the map and reduce paradigm Parallelization with the multiprocessing module and pathos framework Hadoop and Spark for distributed computing Running AWS jobs to process large datasets About the Reader For Python programmers who need to work faster with more data. About the Author J. T. Wolohan is a lead data scientist at Booz Allen Hamilton, and a PhD researcher at Indiana University, Bloomington. Quotes A clear and efficient path to mastery of the map and reduce paradigm for developers of all levels. - Justin Fister, GrammarBot An amazing book for anybody looking to add parallel processing and the map/reduce pattern to their toolkit. - Gary Bake, Radius Payment Solutions Learn fundamentals of MapReduce and other core concepts and save money on expensive hardware! - Al Krinker, USPTO A comprehensive guide to the fundamentals of efficient Python data processing. - Craig Pfeifer, MITRE Corporation

Summary Building a reliable data platform is a neverending task. Even if you have a process that works for you and your business there can be unexpected events that require a change in your platform architecture. In this episode the head of data for Mayvenn shares their experience migrating an existing set of streaming workflows onto the Ascend platform after their previous vendor was acquired and changed their offering. This is an interesting discussion about the ongoing maintenance and decision making required to keep your business data up to date and accurate.

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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Sheel Choksi and Sean Knapp about Mayvenn’s experience migrating their dataflows onto the Ascend platform

Interview

Introduction How did you get involved in the area of data management? Can you start off by describing what Mayvenn is and give a sense of how you are using data? What are the sources of data that you are working with? What are the biggest challenges you are facing in collecting, processing, and analyzing your data? Before adopting Ascend, what did your overall platform for data management look like? What were the pain points that you were facing which led you to seek a new solution?

What were the selection criteria that you set forth for addressing your needs at the time? What were the aspects of Ascend which were most appealing?

What are some of the edge cases that you have dealt with in the Ascend platform? Now that you have been using Ascend for a while, what components of your previous architecture have you been able to retire? Can you talk through the migration process of incorporating Ascend into your platform and any validation that you used to ensure that your data operations remained accurate and consistent? How has the migration to Ascend impacted your overall capacity for processing data or integrating new sources into your analytics? What are your future plans for how to use data across your organization?

Contact Info

Sheel

LinkedIn sheelc on GitHub

Sean

LinkedIn @seanknapp 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 b

Summary The modern era of software development is identified by ubiquitous access to elastic infrastructure for computation and easy automation of deployment. This has led to a class of applications that can quickly scale to serve users worldwide. This requires a new class of data storage which can accomodate that demand without having to rearchitect your system at each level of growth. YugabyteDB is an open source database designed to support planet scale workloads with high data density and full ACID compliance. In this episode Karthik Ranganathan explains how Yugabyte is architected, their motivations for being fully open source, and how they simplify the process of scaling your application from greenfield to global. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementWhen 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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.Your host is Tobias Macey and today I’m interviewing Karthik Ranganathan about YugabyteDB, the open source, high-performance distributed SQL database for global, internet-scale apps.Interview IntroductionHow did you get involved in the area of data management?Can you start by describing what YugabyteDB is and its origin story?A growing trend in database engines (e.g. FaunaDB, CockroachDB) has been an out of the box focus on global distribution. Why is that important and how does it work in Yugabyte? What are the caveats?What are the most notable features of YugabyteDB that would lead someone to choose it over any of the myriad other options? What are the use cases that it is uniquely suited to?What are some of the systems or architecture patterns that can be replaced with Yugabyte?How does the design of Yugabyte or the different ways it is being used influence the way that users should think about modeling their data?Yugabyte is an impressive piece of engineering. Can you talk through the major design elements and how it is implemented?Easy scaling and failover is a feature that many database engines would like to be able to claim. What are the difficult elements that prevent them from implementing that capability as a standard practice? What do you have to sacrifice in order to support the level of scale and fault tolerance that you provide?Speaking of scaling, there are many ways to define that term, from vertical scaling of storage or compute, to horizontal scaling of compute, to scaling of reads and writes. What are the primary scaling factors that you focus on in Yugabyte?How do you approach testing and validation of the code given the complexity of the system that you are building?In terms of the query API you have support for a Postgres compatible SQL dialect as well as a Cassandra based syntax. What are the benefits of targeting compatibility with those platforms? What are the challenges and benefits of maintaining compatibility with those other platforms?Can you describe how the storage layer is implemented and the division between the different query formats?What are the operational characteristics of YugabyteDB? What are the complexities or edge cases that users should be aware of when planning a deployment?One of the challenges of working with large volumes of data is creating and maintaining backups. How does Yugabyte handle that problem?Most open source infrastructure projects that are backed by a business withhold various "enterprise" features such as backups and change data capture as a means of driving revenue. Can you talk through your motivation for releasing those capabilities as open source?What is the business model that you are using for YugabyteDB and how does it differ from the tribal knowledge of how open source companies generally work?What are some of the most interesting, innovative, or unexpected ways that you have seen yugabyte used?When is Yugabyte the wrong choice?What do you have planned for the future of the technical and business aspects of Yugabyte?Contact Info @karthikr on TwitterLinkedInrkarthik007 on GitHubParting 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-workersJoin the community in the new Zulip chat workspace at dataengineeringpodcast.com/chatLinks YugabyteDBGitHubNutanixFacebook EngineeringApache CassandraApache HBaseDelphiFuanaDBPodcast EpisodeCockroachDBPodcast EpisodeHA == High AvailabilityOracleMicrosoft SQL ServerPostgreSQLPodcast EpisodeMongoDBAmazon AuroraPGCryptoPostGISpl/pgsqlForeign Data WrappersPipelineDBPodcast EpisodeCitusPodcast EpisodeJepsen TestingYugabyte Jepsen Test ResultsOLTP == Online Transaction ProcessingOLAP == Online Analytical ProcessingDocDBGoogle SpannerGoogle BigTableSpot InstancesKubernetesCloudformationTerraformPrometheusDebeziumPodcast EpisodeThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s!

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Get Up to Speed on Microsoft® SQL Server® 2019 Quickly and Easily Start working with Microsoft SQL Server 2019 in no time with help from this thoroughly revised, practical resource. Filled with real-world examples and hands-on exercises, Microsoft SQL Server 2019: A Beginner’s Guide, Seventh Edition starts by explaining fundamental relational database system concepts. From there, you’ll learn how to write Transact-SQL statements, execute simple and complex database queries, handle system administration and security, and use powerful analysis and reporting tools. New topics such as SQL and JSON support, graph databases, and support for machine learning with R and Python are also covered in this step-by-step tutorial. • Install, configure, and customize Microsoft SQL Server 2019 • Create and modify database objects with Transact-SQL statements • Write stored procedures and user-defined functions • Handle backup and recovery, and automate administrative tasks • Tune your database system for optimal availability and reliability • Secure your system using authentication, encryption, and authorization • Work with SQL Server Analysis Services, Reporting Services, and other BI tools • Gain knowledge of relational storage, presentation, and retrieval of data stored in the JSON format • Manage graphs using SQL Server Graph Databases • Learn about machine learning support for R and Python

Summary DataDog is one of the most successful companies in the space of metrics and monitoring for servers and cloud infrastructure. In order to support their customers, they need to capture, process, and analyze massive amounts of timeseries data with a high degree of uptime and reliability. Vadim Semenov works on their data engineering team and joins the podcast in this episode to discuss the challenges that he works through, the systems that DataDog has built to power their business, and how their teams are organized to allow for rapid growth and massive scale. Getting an inside look at the companies behind the services we use is always useful, and this conversation was no exception.

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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Vadim Semenov about how data engineers work at DataDog

Interview

Introduction How did you get involved in the area of data management? For anyone who isn’t familiar with DataDog, can you start by describing the types and volumes of data that you’re dealing with? What are the main components of your platform for managing that information? How are the data teams at DataDog organized and what are your primary responsibilities in the organization? What are some of the complexities and challenges that you face in your work as a result of the volume of data that you are processing?

What are some of the strategies which have proven to be most useful in overcoming those challenges?

Who are the main consumers of your work and how do you build in feedback cycles to ensure that their needs are being met? Given that the majority of the data being ingested by DataDog is timeseries, what are your lifecycle and retention policies for that information? Most of the data that you are working with is customer generated from your deployed agents and API integrations. How do you manage cleanliness and schema enforcement for the events as they are being delivered? What are some of the upcoming projects that you have planned for the upcoming months and years? What are some of the technologies, patterns, or practices that you are hoping to adopt?

Contact Info

LinkedIn @databuryat 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 Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

DataDog Hadoop Hive Yarn Chef SRE == Site Reliability Engineer Application Performance Management (APM) Apache Kafka RocksDB Cassandra Apache Parquet data serialization format SLA == Service Level Agreement WatchDog Apache Spark

Podcast Episode

Apache Pig Databricks JVM == Java Virtual Machine Kubernetes SSIS (SQL Server Integration Services) Pentaho JasperSoft Apache Airflow

Podcast.init Episode

Apache NiFi

Podcast Episode

Luigi Dagster

Podcast Episode

Prefect

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

Support Data Engineering Podcast

Summary Building clean datasets with reliable and reproducible ingestion pipelines is completely useless if it’s not possible to find them and understand their provenance. The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform. The metadata repository serves as a data catalog and a means of reporting on the health and status of your datasets when it is properly integrated into the rest of your tools. At WeWork they needed a system that would provide visibility into their Airflow pipelines and the outputs produced. In this episode Julien Le Dem and Willy Lulciuc explain how they built Marquez to serve that need, how it is architected, and how it compares to other options that you might be considering. Even if you already have a metadata repository this is worth a listen to learn more about the value that visibility of your data can bring to your organization.

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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You work hard to make sure that your data is clean, reliable, and reproducible throughout the ingestion pipeline, but what happens when it gets to the data warehouse? Dataform picks up where your ETL jobs leave off, turning raw data into reliable analytics. Their web based transformation tool with built in collaboration features lets your analysts own the full lifecycle of data in your warehouse. Featuring built in version control integration, real-time error checking for their SQL code, data quality tests, scheduling, and a data catalog with annotation capabilities it’s everything you need to keep your data warehouse in order. Sign up for a free trial today at dataengineeringpodcast.com/dataform and email [email protected] with the subject "Data Engineering Podcast" to get a hands-on demo from one of their data experts. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference, the Strata Data conference, and PyCon US. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Willy Lulciuc and Julien Le Dem about Marquez, an open source platform to collect, aggregate, and visualize a data ecosystem’s metadata

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Marquez is?

What was missing in existing metadata management platforms that necessitated the creation of Marquez?

How do the capabilities of Marquez compare with tools and services that bill themselves as data catalogs?

How does it compare to the Amundsen platform that Lyft recently released?

What are some of the tools or platforms that are currently integrated with Marquez and what additional integrations would you like to see? What are some of the capabilities that are unique to Marquez and how are you using them at WeWork? What are the primary resource types that you support in Marquez?

What are some of the lowest common denominator attributes that are necessary and useful to track in a metadata repository?

Can you explain how Marquez is architected and how the design has evolved since you first began working on it?

Many metadata management systems are simply a service layer on top of a separate data storage engine. What are the benefits of using PostgreSQL as the system of record for Marquez?

What are some of the complexities that arise from relying on a relational engine as opposed to a document store or graph database?

How is the metadata itself stored and managed in Marquez?

How much up-front data modeling is necessary and what types of schema representations are supported?

Can you talk through the overall workflow of someone using Marquez in their environment?

What is involved in registering and updating datasets? How do you define and track the health of a given dataset? What are some of the interesting questions that can be answered from the information stored in Marquez?

What were your assumptions going into this project and how have they been challenged or updated as you began using it for production use cases? For someone who is interested in using Marquez what is involved in deploying and maintaining an installation of it? What have you found to be the most challenging or unanticipated aspects of building and maintaining a metadata repository and data discovery platform? When is Marquez the wrong choice for a metadata repository? What do you have planned for the future of Marquez?

Contact Info

Julien Le Dem

@J_ on Twitter Email julienledem on GitHub

Willy

LinkedIn @wslulciuc on Twitter wslulciuc on GitHub

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 Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

Marquez

DataEngConf Presentation

WeWork Canary Yahoo Dremio Hadoop Pig Parquet

Podcast Episode

Airflow Apache Atlas Amundsen

Podcast Episode

Uber DataBook LinkedIn DataHub Iceberg Table Format

Podcast Episode

Delta Lake

Podcast Episode

Great Expectations data pipeline unit testing framework

Podcast.init Episode

Redshift SnowflakeDB

Podcast Episode

Apache Kafka Schema Registry

Podcast Episode

Open Tracing Jaeger Zipkin DropWizard Java framework Marquez UI Cayley Graph Database Kubernetes Marquez Helm Chart Marquez Docker Container Dagster

Podcast Episode

Luigi DBT

Podcast Episode

Thrift Protocol Buffers

The intro and outro music is from a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug?utm_source=rss&utm_medium=rss"…

Discover the methodologies and best practices for getting started with Google Cloud Platform relational services – CloudSQL and CloudSpanner. The book begins with the basics of working with the Google Cloud Platform along with an introduction to the database technologies available for developers from Google Cloud. You'll then take an in-depth hands on journey into Google CloudSQL and CloudSpanner, including choosing the right platform for your application needs, planning, provisioning, designing and developing your application. Sample applications are given that use Python to connect to CloudSQL and CloudSpanner, along with helpful features provided by the engines. You''ll also implement practical best practices in the last chapter. Hands On Google Cloud SQL and Cloud Spanner is a great starting point to apply GCP data offerings in your technology stack and the code used allows you to try out the examples and extend them in interestingways. What You'll Learn Get started with Big Data technologies on the Google Cloud Platform Review CloudSQL and Cloud Spanner from basics to administration Apply best practices and use Google’s CloudSQL and CloudSpanner offering Work with code in Python notebooks and scripts Who This Book Is For Application architects, database architects, software developers, data engineers, cloud architects.

Did fake Twitter accounts help sway a presidential election? What can Facebook and Reddit archives tell us about human behavior? In Mining Social Media, senior BuzzFeed reporter Lam Thuy Vo shows you how to use Python and key data analysis tools to find the stories buried in social media. Whether you’re a professional journalist, an academic researcher, or a citizen investigator, you’ll learn how to use technical tools to collect and analyze data from social media sources to build compelling, data-driven stories. Learn how to: •Write Python scripts and use APIs to gather data from the social web •Download data archives and dig through them for insights •Inspect HTML downloaded from websites for useful content •Format, aggregate, sort, and filter your collected data using Google Sheets •Create data visualizations to illustrate your discoveries •Perform advanced data analysis using Python, Jupyter Notebooks, and the pandas library •Apply what you’ve learned to research topics on your own Social media is filled with thousands of hidden stories just waiting to be told. Learn to use the data-sleuthing tools that professionals use to write your own data-driven stories.