In this episode of DataFramed, Adel speaks with Alessya Visnjic, CEO and co-founder of WhyLabs,  an AI Observability company on a mission to build the interface between AI and human operators. Throughout the episode, Alessya talks about the unique challenges data teams face when operationalizing machine learning that spurred the need for MLOps, how MLOps intersects and diverges with different terms such as DataOps, ModelOps, and AIOps, how and when organizations should get started on their MLOps journey, the most important components of a successful MLOps practice, and more. 

Relevant links from the interview:

Connect with Alessya on LinkedInAndrew Ng on the important of being data-centricJoe Reis on the data culture and all things datawhylogs: the standard for data logging — please send you feedback, contribute, help us build integrations into your favorite data tools and extend the concept of logging to new data types. Join the effort of building a new open standard for data logging!Try the WhyLabs platform

Summary The Data industry is changing rapidly, and one of the most active areas of growth is automation of data workflows. Taking cues from the DevOps movement of the past decade data professionals are orienting around the concept of DataOps. More than just a collection of tools, there are a number of organizational and conceptual changes that a proper DataOps approach depends on. In this episode Kevin Stumpf, CTO of Tecton, Maxime Beauchemin, CEO of Preset, and Lior Gavish, CTO of Monte Carlo, discuss the grand vision and present realities of DataOps. They explain how to think about your data systems in a holistic and maintainable fashion, the security challenges that threaten to derail your efforts, and the power of using metadata as the foundation of everything that you do. If you are wondering how to get control of your data platforms and bring all of your stakeholders onto the same page then this conversation is for 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! Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask. RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. Your host is Tobias Macey and today I’m interviewing Max Beauchemin, Lior Gavish, and Kevin Stumpf about the real world challenges of embracing DataOps practices and systems, and how to keep things secure as you scale

Interview

Introduction How did you get involved in the area of data management? Before we get started, can you each give your definition of what "DataOps" means to you?

How does this differ from "business as usual" in the data industry? What are some of the things that DataOps isn’t (despite what marketers might say)?

What are the biggest difficulties that you have faced in going from concept to production with a workflow or system intended to power self-serve access to other membe

We talked about:

Lars’ career Doing DataOps before it existed What is DataOps Data platform Main components of the data platform and tools to implement it Books about functional programming principles Batch vs Streaming Maturity levels Building self-service tools MLOps vs DataOps Data Mesh Keeping track of transformations Lake house

Links:

https://www.scling.com/reading-list/ https://www.scling.com/presentations/

Join DataTalks.Club: https://datatalks.club/slack.html​​​

Every December, Eckerson Group fulfills its industry obligation to summon its collective knowledge and insights about data and analytics and speculate about what might happen in the coming year. The diversity of predictions from our research analysts and consultants exemplifies the breadth of their research and consulting experiences and the depth of their thinking. Predictions from Kevin Petrie, Joe Hilleary, Dave Wells, Andrew Sohn, and Sean Hewitt range from data and privacy governance to artificial intelligence with stops along the way for DataOps, data observability, data ethics, cloud platforms, and intelligent robotic automation.

We covered:

What is MLOps The difference between MLOps and ML Engineering Getting into MLOps Kubeflow and its components, ML Platforms Learning Kubeflow DataOps 

And other things

Links:

Microsoft MLOps maturity model: https://docs.microsoft.com/en-us/azure/architecture/example-scenario/mlops/mlops-maturity-model Google MLOps maturity levels: https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning MLOps roadmap 2020-2025: https://github.com/cdfoundation/sig-mlops/blob/master/roadmap/2020/MLOpsRoadmap2020.md Kubeflow website: https://www.kubeflow.org/ TFX Paper: https://research.google/pubs/pub46484/

Join DataTalks.Club: https://datatalks.club​​

The Data Mirage: Why Companies Fail to Actually Use Their Data is a business book for executives and leaders who want to unlock more insights from their data and make better decisions. The importance of data doesn’t need an introduction or a fancy pitch deck. Data plays a critical role in helping companies to better understand their users, beat out their competitors, and breakthrough their growth targets. However, despite significant investments in their data, most organizations struggle to get much value from it. According to Forrester, only 38% of senior executives and decision-makers “have a high level of confidence in their customer insights and only 33% trust the analytics they generate from their business operations.” This reflects the real world that I have experienced. In this book, I will help readers formulate an analytics strategy that works in the real world, show them how to think about KPIs and help them tackle the problems they are bound to come across as they try to use data to make better decisions.

Send us a text 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.

Abstract Hosted by Al Martin, VP, Data and AI Expert Services and Learning at IBM, 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 Oliver Claude Portfolio Offering Manager for Data and AI and Oliver is a Data Governance expert. Oliver also worked as a Chief Marketing Officer, VP and Chief Solution Owner, Solution Management, and Consulting, Al and Oliver discuss Data Governance and Data Ops and how it all fits into your business. 

Show Notes 2:50 - What is the definition of Data Governance? 4:06 - What is Data Ops? 4:40 - What is IBM doing with Data Ops? 5:16 - How have we automated our tools? 6:58 - What is better red or white wine? 7:33 - What is the future of Data Governance? 9:37 - How is Data Governance and Data Ops related to AI? 11:06 - What are the pitfalls for customers implementing Data Governance? 12:10 - How do companies get started? Oliver Claude - LinkedIn IBM DataOps    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.

Summary There are an increasing number of use cases for real time data, and the systems to power them are becoming more mature. Once you have a streaming platform up and running you need a way to keep an eye on it, including observability, discovery, and governance of your data. That’s what the Lenses.io DataOps platform is built for. In this episode CTO Andrew Stevenson discusses the challenges that arise from building decoupled systems, the benefits of using SQL as the common interface for your data, and the metrics that need to be tracked to keep the overall system healthy. Observability and governance of streaming data requires a different approach than batch oriented workflows, and this episode does an excellent job of outlining the complexities involved and how to address them.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode of the Data Engineering Podcast is sponsored by Datadog, a SaaS-based monitoring and analytics platform for cloud-scale infrastructure, applications, logs, and more. Datadog uses machine-learning based algorithms to detect errors and anomalies across your entire stack—which reduces the time it takes to detect and address outages and helps promote collaboration between Data Engineering, Operations, and the rest of the company. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial. If you start a trial and install Datadog’s agent, Datadog will send you a free T-shirt. 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 platforms. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today! Your host is Tobias Macey and today I’m interviewing Andrew Stevenson about Lenses.io, a platform to provide real-time data operations for engineers

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Lenses is and the story behind it? What is your working definition for what constitutes DataOps?

How does the Lenses platform support the cross-cutting concerns that arise when trying to bridge the different roles in an organization to deliver value with data?

What are the typical barriers to collaboration, and how does Lenses help with that?

Many different systems provide a SQL interface to streaming data on various substrates. What was your reason for building your own SQL engine and what is unique about it? What are the main challenges that you see engineers facing when working with s

Summary The PostgreSQL database is massively popular due to its flexibility and extensive ecosystem of extensions, but it is still not the first choice for high performance analytics. Swarm64 aims to change that by adding support for advanced hardware capabilities like FPGAs and optimized usage of modern SSDs. In this episode CEO and co-founder Thomas Richter discusses his motivation for creating an extension to optimize Postgres hardware usage, the benefits of running your analytics on the same platform as your application, and how it works under the hood. If you are trying to get more performance out of your database then this episode is for 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 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 monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Tidy Data is the DataOps monitoring platform that you’ve been missing. With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. Go to dataengineeringpodcast.com/tidydata today and get started for free with no credit card required. Your host is Tobias Macey and today I’m interviewing Thomas Richter about Swarm64, a PostgreSQL extension to improve parallelism and add support for FPGAs

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Swarm64 is?

How did the business get started and what keeps you motivated?

What are some of the common bottlenecks that users of postgres run into? What are the use cases and workloads that gain the most benefit from increased parallelism in the database engine? By increasing the processing throughput of the database, how does that impact disk I/O and what are some options for avoiding bottlenecks in the persistence layer? Can you describe how Swarm64 is implemented?

How has the product evolved since you first began working on it?

How has the evolution of postgres impacted your product direction?

What are some of the notable challenges that you have dealt with as a result of upstream changes in postgres?

How has the hardware landscape evolved and how does that affect your prioritization of features and improvements? What are some of the other extensions in the postgres ecosystem that are most commonly used alongside Swarm64?

Which extensions conflict with yours and how does that impact potential adoption?

In addition to your work to optimize performance of the postres engine, you also provide support for using an FPGA as a co-processor. What are the benefits that an FPGA provides over and above a CPU or GPU architecture?

What are the available options for provisioning hardware in a datacenter or the cloud that has access to an FPGA? Most people are familiar with the relevant attributes for selecting a CPU or GPU, what are the specifications that they should be looking at when selecting an FPGA?

For users who are adopting Swarm64, how does it impact the way they should be thinking of their data models? What is involved in migrating an existing database to use Swarm64? What are some of the most interesting, unexpected, or

Summary There have been several generations of platforms for managing streaming data, each with their own strengths and weaknesses, and different areas of focus. Pulsar is one of the recent entrants which has quickly gained adoption and an impressive set of capabilities. In this episode Sijie Guo discusses his motivations for spending so much of his time and energy on contributing to the project and growing the community. His most recent endeavor at StreamNative is focused on combining the capabilities of Pulsar with the cloud native movement to make it easier to build and scale real time messaging systems with built in event processing capabilities. This was a great conversation about the strengths of the Pulsar project, how it has evolved in recent years, and some of the innovative ways that it is being used. Pulsar is a well engineered and robust platform for building the core of any system that relies on durable access to easily scalable streams of data.

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 monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Tidy Data is the DataOps monitoring platform that you’ve been missing. With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. Go to dataengineeringpodcast.com/tidydata today and get started for free with no credit card required. Your host is Tobias Macey and today I’m interviewing Sijie Guo about the current state of the Pulsar framework for stream processing and his experiences building a managed offering for it at StreamNative

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of what Pulsar is?

How did you get involved with the project?

What is Pulsar’s role in the lifecycle of data and where does it fit in the overall ecosystem of data tools? How has the Pulsar project evolved or changed over the past 2 years?

How has the overall state of the ecosystem influenced the direction that Pulsar has taken?

One of the critical elements in the success of a piece of technology is the ecosystem that grows around it. How has the community responded to Pulsar, and what are some of the barriers to adoption?

How are you and other project leaders addressing those barriers?

You were a co-founder at Streamlio, which was built on top of Pulsar, and now you have founded StreamNative to offer Pulsar as a service. What did you learned from your time at Streamlio that has been most helpful in your current endeavor?

How would you characterize your relationship with the project and community in each role?

What motivates you to dedicate so much of your time and enery to Pulsar in particular, and the streaming data ecosystem in general?

Why is streaming data such an important capability? How have projects such as Kafka and Pulsar impacted the broader software and data landscape?

What are some of the most interesting, innovative, or unexpected ways that you have seen Pulsar used? When is Pulsar the wrong choice? What do you have planned for the future of S

Summary Data is a critical element to every role in an organization, which is also what makes managing it so challenging. With so many different opinions about which pieces of information are most important, how it needs to be accessed, and what to do with it, many data projects are doomed to failure. In this episode Chris Bergh explains how taking an agile approach to delivering value can drive down the complexity that grows out of the varied needs of the business. Building a DataOps workflow that incorporates fast delivery of well defined projects, continuous testing, and open lines of communication is a proven path to success.

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! If DataOps sounds like the perfect antidote to your pipeline woes, DataKitchen is here to help. DataKitchen’s DataOps Platform automates and coordinates all the people, tools, and environments in your entire data analytics organization – everything from orchestration, testing and monitoring to development and deployment. In no time, you’ll reclaim control of your data pipelines so you can start delivering business value instantly, without errors. Go to dataengineeringpodcast.com/datakitchen today to learn more and thank them for supporting the show! Your host is Tobias Macey and today I’m welcoming back Chris Bergh to talk about ways that DataOps principles can help to reduce organizational complexity

Interview

Introduction How did you get involved in the area of data management? How are typical data and analytic teams organized? What are their roles and structure? Can you start by giving an outline of the ways that complexity can manifest in a data organization?

What are some of the contributing factors that generate this complexity? How does the size or scale of an organization and their data needs impact the segmentation of responsibilities and roles?

How does this organizational complexity play out within a single team? For example between data engineers, data scientists, and production/operations? How do you approach the definition of useful interfaces between different roles or groups within an organization?

What are your thoughts on the relationship between the multivariate complexities of data and analytics workflows and the software trend toward microservices as a means of addressing the challenges of organizational communication patterns in the software lifecycle?

How does this organizational complexity play out between multiple teams? For example between centralized data team and line of business self service teams? Isn’t organizational complexity just ‘the way it is’? Is there any how in getting out of meetings and inter team conflict? What are some of the technical elements that are most impactful in reducing the time to delivery for different roles? What are some strategies that you have found to be useful for maintaining a connection to the business need throughout the different stages of the data lifecycle? What are some of the signs or symptoms of problematic complexity that individuals and organizations should keep an eye out for? What role can automated testing play in improving this process? How do the current set of tools contribute to the fragmentation of data wor

Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will Learn Develop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data to users at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production.

Learn how to achieve the DataOps objectives of improved efficiency and data quality by migrating to a streaming architecture based on Apache Kafka.

Summary Data professionals are working in a domain that is rapidly evolving. In order to stay current we need access to deeply technical presentations that aren’t burdened by extraneous marketing. To fulfill that need Pete Soderling and his team have been running the Data Council series of conferences and meetups around the world. In this episode Pete discusses his motivation for starting these events, how they serve to bring the data community together, and the observations that he has made about the direction that we are moving. He also shares his experiences as an investor in developer oriented startups and his views on the importance of empowering engineers to launch their own companies.

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! Listen, I’m sure you work for a ‘data driven’ company – who doesn’t these days? Does your company use Amazon Redshift? Have you ever groaned over slow queries or are just afraid that Amazon Redshift is gonna fall over at some point? Well, you’ve got to talk to the folks over at intermix.io. They have built the “missing” Amazon Redshift console – it’s an amazing analytics product for data engineers to find and re-write slow queries and gives actionable recommendations to optimize data pipelines. WeWork, Postmates, and Medium are just a few of their customers. Go to dataengineeringpodcast.com/intermix today and use promo code DEP at sign up to get a $50 discount! 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, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. 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 Pete Soderling about his work to build and grow a community for data professionals with the Data Council conferences and meetups, as well as his experiences as an investor in data oriented companies

Interview

Introduction How did you get involved in the area of data management? What was your original reason for focusing your efforts on fostering a community of data engineers?

What was the state of recognition in the industry for that role at the time that you began your efforts?

The current manifestation of your community efforts is in the form of the Data Council conferences and meetups. Previously they were known as Data Eng Conf and before that was Hakka Labs. Can you discuss the evolution of your efforts to grow this community?

How has the community itself changed and grown over the past few years?

Communities form around a huge variety of focal points. What are some of the complexities or challenges in building one based on something as nebulous as data? Where do you draw inspiration and direction for how to manage such a large and distributed community?

What are some of the most interesting/challenging/unexpected aspects of community management that you have encountered?

What are some ways that you have been surprised or delighted in your interactions with the data community? How do you approach sustainability of the Data Council community and the organization itself? The tagline that you have focused on for Data Council events is that they are no fluff, juxtaposing them against larger business oriented events. What are your guidelines for fulfilling that promise and why do you think that is an important distinction? In addition to your community building you are also an investor. How did you get involved in that side of your business and how does it fit into your overall mission? You also have a stated mission to help engineers build their own companies. In your opinion, how does an engineer led business differ from one that may be founded or run by a business oriented individual and why do you think that we need more of them?

What are the ways that you typically work to empower engineering founders or encourage them to create their own businesses?

What are some of the challenges that engineering founders face and what are some common difficulties or misunderstandings related to business?

What are your opinions on venture-backed vs. "lifestyle" or bootstrapped businesses?

What are the characteristics of a data business that you look at when evaluating a potential investment? What are some of the current industry trends that you are most excited by?

What are some that you find concerning?

What are your goals and plans for the future of Data Council?

Contact Info

@petesoder on Twitter LinkedIn @petesoder on Medium

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

Data Council Database Design For Mere Mortals Bloomberg Garmin 500 Startups Geeks On A Plane Data Council NYC 2019 Track Summary Pete’s Angel List Syndicate DataOps

Data Kitchen Episode DataOps Vs DevOps Episode

Great Expectations

Podcast.init Interview

Elementl Dagster

Data Council Presentation

Data Council Call For Proposals

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

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Many large organizations have accumulated dozens of disconnected data sources to serve different lines of business over the years. These applications might be useful to one area of the enterprise, but they’re usually inaccessible to other data consumers in the organization. In this short report, five data industry thought leaders explore DataOps—the automated, process-oriented methodology for making clean, reliable data available to teams throughout your company. Andy Palmer, Michael Stonebraker, Nik Bates-Haus, Liam Cleary, and Mark Marinelli from Tamr use real-world examples to explain how DataOps works. DataOps is as much about changing people’s relationship to data as it is about technology, infrastructure, and process. This report provides an organizational approach to implementing this discipline in your company—including various behavioral, process, and technology changes. Through individual essays, you’ll learn how to: Move toward scalable data unification (Michael Stonebraker) Understand DataOps as a discipline (Nik Bates-Haus) Explore the key principles of a DataOps ecosystem (Andy Palmer) Learn the key components of a DataOps ecosystem (Andy Palmer) Build a DataOps toolkit (Liam Cleary) Build a team and prepare for future trends (Mark Marinelli)

When data-driven applications fail, identifying the cause is both challenging and time-consuming—especially as data pipelines become more and more complex. Hunting for the root cause of application failure from messy, raw, and distributed logs is difficult for performance experts and a nightmare for data operations teams. This report examines DataOps processes and tools that enable you to manage modern data pipelines efficiently. Author Ted Malaska describes a data operations framework and shows you the importance of testing and monitoring to plan, rebuild, automate, and then manage robust data pipelines—whether it’s in the cloud, on premises, or in a hybrid configuration. You’ll also learn ways to apply performance monitoring software and AI to your data pipelines in order to keep your applications running reliably. You’ll learn: How performance management software can reduce the risk of running modern data applications Methods for applying AI to provide insights, recommendations, and automation to operationalize big data systems and data applications How to plan, migrate, and operate big data workloads and data pipelines in the cloud and in hybrid deployment models

In this podcast, Dr. Michael Stonebraker discussed his perspective on the growing data ops industry and its future. Dr. Stonebraker has launched several startups that defined data ops. He shares his insights into the data ops market and what to expect in the future of data and operations.

Timeline: 0:30 Mike's take on the "no sequel movement". 6:48 Evolution of database. 13:55 Mobility of data and cloud. 18:41 Tamr's shift from the database to AI. 29:00 Ingredient for a successful start-up. 36:50 Leadership qualities that keep you successful and sane. 41:50 Mike's parting thoughts.

Podcast Link: https://futureofdata.org/dr-mikestonebraker-on-the-future-of-dataops-and-ai/

Dr. Stonebraker's BIO: Dr. Stonebraker has been a pioneer of database research and technology for more than forty years. He was the main architect of the INGRES relational DBMS, and the object-relational DBMS, POSTGRES. These prototypes were developed at the University of California at Berkeley, where Stonebraker was a Professor of Computer Science for twenty-five years. More recently, at M.I.T., he was a co-architect of the Aurora/Borealis stream processing engine, the C-Store column-oriented DBMS, the H-Store transaction processing engine, which became VoltDB, the SciDB array DBMS, and the Data Tamer data curation system. Presently he serves as an advisor to VoltDB and Chief Technology Officer of Paradigm4 and Tamr, Inc.

Professor Stonebraker was awarded the ACM System Software Award in 1992 for his work on INGRES. Additionally, he was awarded the first annual SIGMOD Innovation award in 1994 and was elected to the National Academy of Engineering in 1997. He was awarded the IEEE John Von Neumann award in 2005 and the 2014 Turing Award and is presently an Adjunct Professor of Computer Science at M.I.T, where he is co-director of the Intel Science and Technology Center focused on big data.

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Summary Delivering a data analytics project on time and with accurate information is critical to the success of any business. DataOps is a set of practices to increase the probability of success by creating value early and often, and using feedback loops to keep your project on course. In this episode Chris Bergh, head chef of Data Kitchen, explains how DataOps differs from DevOps, how the industry has begun adopting DataOps, and how to adopt an agile approach to building your data platform.

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! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. "There aren’t enough data conferences out there that focus on the community, so that’s why these folks built a better one": Data Council is the premier community powered data platforms & engineering event for software engineers, data engineers, machine learning experts, deep learning researchers & artificial intelligence buffs who want to discover tools & insights to build new products. This year they will host over 50 speakers and 500 attendees (yeah that’s one of the best "Attendee:Speaker" ratios out there) in San Francisco on April 17-18th and are offering a $200 discount to listeners of the Data Engineering Podcast. Use code: DEP-200 at checkout 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, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. 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 Your host is Tobias Macey and today I’m interviewing Chris Bergh about the current state of DataOps and why it’s more than just DevOps for data

Interview

Introduction How did you get involved in the area of data management? We talked last year about what DataOps is, but can you give a quick overview of how the industry has changed or updated the definition since then?

It is easy to draw parallels between DataOps and DevOps, can you provide some clarity as to how they are different?

How has the conversat

In this episode, Wayne Eckerson and Shakeeb Ahkter dive into DataOps. They discuss what DataOps is, the goals and principles of DataOps, and reasons to adopt a DataOps strategy. Shakeeb also reveals the benefits gained from DataOps and what tools he uses. He is the Director of Enterprise Data Warehouse at Northwestern Medicine and is responsible for direction and oversight of data management, data engineering, and analytics.

Summary

One of the longest running and most popular open source database projects is PostgreSQL. Because of its extensibility and a community focus on stability it has stayed relevant as the ecosystem of development environments and data requirements have changed and evolved over its lifetime. It is difficult to capture any single facet of this database in a single conversation, let alone the entire surface area, but in this episode Jonathan Katz does an admirable job of it. He explains how Postgres started and how it has grown over the years, highlights the fundamental features that make it such a popular choice for application developers, and the ongoing efforts to add the complex features needed by the demanding workloads of today’s data layer. To cap it off he reviews some of the exciting features that the community is working on building into future releases.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Jonathan Katz about a high level view of PostgreSQL and the unique capabilities that it offers

Interview

Introduction How did you get involved in the area of data management? How did you get involved in the Postgres project? For anyone who hasn’t used it, can you describe what PostgreSQL is?

Where did Postgres get started and how has it evolved over the intervening years?

What are some of the primary characteristics of Postgres that would lead someone to choose it for a given project?

What are some cases where Postgres is the wrong choice?

What are some of the common points of confusion for new users of PostGreSQL? (particularly if they have prior database experience) The recent releases of Postgres have had some fairly substantial improvements and new features. How does the community manage to balance stability and reliability against the need to add new capabilities? What are the aspects of Postgres that allow it to remain relevant in the current landscape of rapid evolution at the data layer? Are there any plans to incorporate a distributed transaction layer into the core of the project along the lines of what has been done with Citus or CockroachDB? What is in store for the future of Postgres?

Contact Info

@jkatz05 on Twitter jkatz on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

PostgreSQL Crunchy Data Venuebook Paperless Post LAMP Stack MySQL PHP SQL ORDBMS Edgar Codd A Relational Model of Data for Large Shared Data Banks Relational Algebra Oracle DB UC Berkeley Dr. Michae