The Curse of the Data Lake Monster

Watch here: https://bit.ly/2yxLo6f Moving applications and data to the Cloud is a priority for many organizations. The benefits - in terms of flexibility, agility, and cost savings - are driving Cloud adoption. However, the journey to the Cloud is not as easy as many people think. The process of moving application and data to the Cloud is challenging and can entail widespread disruption across the organization if not carefully managed. Even when systems are migrated to the Cloud, the resultant hybrid or multi-Cloud architecture is more complex for users to navigate, making it harder for them to get the data that they need to do their jobs. Data Virtualization can help organizations at all stages of their journey to the Cloud - during migration and also in the resultant hybrid or multi-Cloud architectures. Attend this webinar to learn how Data Virtualization can: - Help organizations manage risk and minimize the disruption caused as systems are moved to the Cloud - Provide a single point of access for data that is both on-premise and in the Cloud, making it easier for users to find and access the data that they need - Provide a security layer to protect and manage your data when it's distributed across hybrid or multi-Cloud architectures

Simplifying Cloud Architectures with Data Virtualization

Recently the concept of a ‘data mesh’ was introduced by Zhamak Deghani to solve architectural and organizational challenges with getting value from data at scale more logically and efficiently, built around four principles: * Domain-oriented decentralized data ownership * Data as a product * Self-serve data infrastructure as a platform * Federated computational governance This presentation will initially deep-dive into the ‘data mesh’ and how it fundamentally differs from the typical data lake architectures used today. Subsequently, it describes OLX Europe’s current data platform state aimed partially towards a more decentralized data architecture, covering its analytical data platform, data infrastructure, data discovery, and data privacy. Finally, it will see to what extent the main principles around the ‘data mesh’ can be applied to a future vision for our data platform and what advantages and challenges implementing such a vision can bring for OLX and other companies. For more information on data mesh principles, check out the original article by Zhamak: https://martinfowler.com/articles/data-mesh-principles.html.

To mesh or mess up your data organisation - Jochem van Grondelle (Prosus/OLX ...

Jochem van Grondelle

Watch full webinar here: https://bit.ly/3dhbZTK What started to evolve as the most agile and real-time enterprise data fabric, data virtualization is proving to go beyond its initial promise and is becoming one of the most important enterprise big data fabrics. Watch this session to learn: - What data virtualization really is. - How it differs from other enterprise data integration technologies. - Why data virtualization is finding enterprise-wide deployment inside some of the largest organizations.

Why Data Virtualization? An Introduction.

Abstract:- Companies are adopting big data for performing high-velocity real-time analytics on very large volumes of data to enable rapid analysis for business users using self-service and never-before-realized use cases. However, such projects have yielded limited value because these big data systems have become siloed from the rest of the enterprise systems holding critical business operational data. Big Data Fabric is a modern data architecture combining data virtualization, data prep, and lineage capabilities to seamlessly integrate at scale these huge, siloed volumes of structured and unstructured data with other enterprise data assets. This presentation will demonstrate with proven customer case studies in big data and IoT about the value of using big data fabric as a logical data lake for big data analytics.

Data Mesh @ Yelp - 2019

Steven Moy

Big Data Fabric for At-Scale Real-Time Analysis by Edwin Robbins

Data Con LA

Watch full webinar here: https://bit.ly/3ohtRqm Companies with corporate data lakes also need a strategy for how to best integrate them with their overall data fabric. To take full advantage of a data lake, data architects must determine what data belongs in the Lake vs. other sources, how end users are going to find and connect to the data they need as well as the best way to leverage the processing power of the data lake. This webinar will provide you with a deep dive look at how the Denodo Platform for data virtualization enables companies to maximize their investment in their corporate data lake. Watch on-demand this webinar to learn: - How to create a logical data fabric with Denodo - How to leverage the a data lake for MPP Acceleration and Summary Views - How to leverage Presto with Denodo for file based data lakes (ie. S3, ADLS, HDFS, etc.)

Maximizing Data Lake ROI with Data Virtualization: A Technical Demonstration

Watch full webinar here: https://bit.ly/2xc6IO0 To solve these challenges, according to Gartner "through 2022, 60% of all organizations will implement data virtualization as one key delivery style in their data integration architecture". It is clear that data virtualization has become a driving force for companies to implement agile, real-time and flexible enterprise data architecture. In this session we will look at the data integration challenges solved by data virtualization, the main use cases and examine why this technology is growing so fastly. You will learn: - What data virtualization really is - How it differs from other enterprise data integration technologies - Why data virtualization is finding enterprise-wide deployment inside some of the largest organizations

Data Virtualization: From Zero to Hero

A talk by Arif Wider & Emily Gorcenski presented at NDC Porto '20 Abstract: It is already challenging to transition a machine learning model or AI system from the research space to production, and maintaining that system alongside ever-changing data is an even greater challenge. In software engineering, Continuous Delivery practices have been developed to ensure that developers can adapt, maintain, and update software and systems cheaply and quickly, enabling release cycles on the scale of hours or days instead of weeks or months. Nevertheless, in the data science world Continuous Delivery is rarely been applied holistically. This is partly due to different workflows: data scientists regularly work on whole sets of hypotheses, whereas software engineers work more linearly even when evaluating multiple implementation alternatives. Therefore, existing software engineering practices cannot be applied as-is to machine learning projects. Learn how we used our expertise in both fields to adapt practices and tools to allow for Continuous Intelligence–the practice of delivering AI applications continuously.

Apache Kafka® and the Data Mesh

ConfluentInc1

Continuous Intelligence: Keeping your AI Application in Production

Dr. Arif Wider

Watch full webinar here: https://bit.ly/3FHKalT Given the growing demand for analytics and the need for organizations to advance beyond dashboards to self-service analytics and more sophisticated algorithms like machine learning (ML), enterprises are moving towards a unified environment for data and analytics. What is the best approach to accomplish this unification? In TDWI’s recent Best Practice Report, Unified Platforms for Modern Analytics, written by Fern Halper, TDWI VP Research, Senior Research Director for Advanced Analytics, adoption, use, challenges, architectures, and best practices for unified platforms for modern analytics is explored. One of the approaches for unification outlined in the report is a data fabric approach. Join us for a webinar with our Director of Product Marketing, Robin Tandon, where he will discuss the role of the logical data fabric in a unified platform for modern analytics, focusing on several of the key findings outlined in this report. He will share insights and use case examples that demonstrate how a properly implemented logical data fabric is the most suitable approach for Unified Data Platforms across enterprises and organizations. Watch on-demand & Learn: - The benefits of a unified platform and its ability to capture diverse & emerging data types and how to support high performance and scalable solutions. - The role of an enhanced AI driven data catalog and its implications towards the findings in the best practice report. - Implications of a logical data fabric as it relates to several of the recommendations outlined in the report.

The Role of the Logical Data Fabric in a Unified Platform for Modern Analytics

Watch full webinar here: https://buff.ly/309CZ1Y Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spent most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way. Attend this webinar and learn: *How data virtualization can accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice *How popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc. integrate with Denodo *How you can use the Denodo Platform with large data volumes in an efficient way *About the success McCormick has had as a result of seasoning the Machine Learning and Blockchain Landscape with data virtualization

Minimizing the Complexities of Machine Learning with Data Virtualization

Watch full webinar on demand here: https://goo.gl/Rdrc1w "Through 2020, 50% of enterprises will implement some form of data virtualization as one enterprise production option for data integration" according to Gartner. It is clear that data virtualization has become a driving force for companies to implement an agile, real-time and flexible enterprise data architecture. Attend this session to learn: • What data virtualization actually means and how it differs from traditional data integration approaches • The all important use cases and key patterns of data virtualization • What to expect in the upcoming sessions in the Packed Lunch Webinar Series, which will take a deeper dive into various challenges solved by data virtualization in big data analytics, cloud migration and various other scenarios Agenda: • Introduction & benefits of DV • Summary & next steps • Q&A

An Introduction to Data Virtualization in 2018

Watch the full session on-demand here: https://goo.gl/upxC5W Real-Time Analytics for Big Data, Cloud & Self-Service BI The world of data is only becoming distributed. Privacy, regulations, and the need for real-time decisions are challenging organizations’ legacy information strategy. This webinar will include an expert panel discussion on Logical Data Warehouse, Universal Semantic Layer, and Real-time Analytics by Paul Moxon (VP of Data Architectures), Pablo Alvarez (Director of Product Management), and Alberto Pan (CTO). Attend and learn: • The major challenges of legacy information strategies. • How data virtualization can help you overcome these challenges. • Strategies for enabling agile data management and analytics.

3 Reasons Data Virtualization Matters in Your Portfolio

A Webinar with Hortonworks and Denodo (watch on demand here: https://goo.gl/xuP1Ak) Vizient needed a unified view of their accounting and financial data marts to enable business users to discover the information they need in a self-service manner and to be able to provide excellent service to their members. Vizient selected Hortonworks Big Data Platform and Denodo Data Virtualization Platform so that they can unify their distributed data sets in a data lake, and at the same time provide an abstraction for end users for easy self-serviceable information access. During this webinar, you will learn: 1) The role, use, and benefits of Hortonworks Data Platform in the Modern Data Architecture. 2) How Hadoop and data virtualisation simplify data management and self-service data discovery. 3) What data virtualisation is and how it can simplify big data projects. Best practices of using Hadoop with data virtualisation About Vizient Vizient, Inc. is the largest nationwide network of community-owned health care systems and their physicians in the US. Vizient™ combines the strengths of VHA, University HealthSystem Consortium (UHC), Novation and MedAssets SCM and Sg2, trusted leaders focused on solving health care's most pressing challenges. Vizient delivers brilliant resources and powerful data driven insights to healthcare organizations.

Powering Self Service Business Intelligence with Hadoop and Data Virtualization

Organizations have been chasing the dream of data democratization, unlocking and accessing data at scale to serve their customers and business, for over a half a century from early days of data warehousing. They have been trying to reach this dream through multiple generations of architectures, such as data warehouse and data lake, through a cambrian explosion of tools and a large amount of investments to build their next data platform. Despite the intention and the investments the results have been middling. In this keynote, Zhamak shares her observations on the failure modes of a centralized paradigm of a data lake, and its predecessor data warehouse. She introduces Data Mesh, a paradigm shift in big data management that draws from modern distributed architecture: considering domains as the first class concern, applying self-sovereignty to distribute the ownership of data, applying platform thinking to create self-serve data infrastructure, and treating data as a product. This talk introduces the principles underpinning data mesh and Zhamak's recent learnings in creating a path to bring data mesh to life in your organization.

Datamesh community meetup 28th jan 2021

Prasad Prabhakaran

How to Build the Data Mesh Foundation: A Principled Approach | Zhamak Dehghan...

HostedbyConfluent

Fast Data Strategy Houston Roadshow focused on the next industrial revolution on the horizon, driven by the application of big data, IoT and Cloud technologies. • Denodo’s innovative customer, Anadarko, elaborated on how data virtualization serves as the key component in their prescriptive and predictive analytics initiatives, driven by multi-structured data ranging from customer data to equipment data. • Denodo’s session, Unleashing the Power of Data, described the complexity of the modern data ecosystem and how to overcome challenges and successfully harness insights. • Our Partner Noah Consulting, an expert analytics solutions provider in the energy industry, explained how your peers are innovating using new business models and reducing cost in areas such as Asset Management and Operations by leveraging Data Virtualization and Prescriptive and Predictive Analytics. For more information on upcoming roadshows near you, follow this link: https://goo.gl/WBDHiE

Fast Data Strategy Houston Roadshow Presentation

Denodo Data Virtualization - IT Days in Luxembourg with Oktopus

Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/Bvmvc9 Data prep and data blending are terms that have come to prominence over the last year or two. On the surface, they appear to offer functionality similar to data virtualization…but there are important differences! In this session, you will learn: • How data virtualization complements or contrasts technologies such as data prep and data blending • Pros and cons of functionality provided by data prep, data catalog and data blending tools • When and how to use these different technologies to be most effective This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6

Denodo DataFest 2016: Comparing and Contrasting Data Virtualization With Data...

Many are confused when it comes to data. Architecture, models, data – it can seem a bit overwhelming. This program offers a clear explanation of both Data Architecture and Data Modeling. Data Modeling is a primary means of achieving better understanding of specific Data Architecture components. Data Architecture is the sum of the various organizational data models. Both are made more useful by the other. Data models are literally the pages, intersecting Data Architecture and Data Modeling. Any time you are talking architecture, it is important to include the complementary role of engineering. Engineering must be addressed from both forward and reverse perspectives. Only when working in a coordinated manner can organizations take steps to better understand what they have and what they need to accomplish – employing Data Modeling and Data Architecture to achieve their mission. Data models are required for this coordination, providing the means of verifying integration, the primary documentation, and required input to data systems evolution. Program learning objectives include: • Understanding the role played by models • Incorporating the interrelated concepts of architecture/engineering • What is taught: forward engineering with a goal of building • What is also needed: reverse engineering with a goal of understanding • How increasing coordination requirements increase design simplicity

What's hot (20)

Data virtualization an introduction

Simplifying Cloud Architectures with Data Virtualization

To mesh or mess up your data organisation - Jochem van Grondelle (Prosus/OLX ...

Why Data Virtualization? An Introduction.

Data Mesh @ Yelp - 2019

Big Data Fabric for At-Scale Real-Time Analysis by Edwin Robbins

Maximizing Data Lake ROI with Data Virtualization: A Technical Demonstration

Data Virtualization: From Zero to Hero

Apache Kafka® and the Data Mesh

Continuous Intelligence: Keeping your AI Application in Production

The Role of the Logical Data Fabric in a Unified Platform for Modern Analytics

Minimizing the Complexities of Machine Learning with Data Virtualization

An Introduction to Data Virtualization in 2018

3 Reasons Data Virtualization Matters in Your Portfolio

Powering Self Service Business Intelligence with Hadoop and Data Virtualization

Datamesh community meetup 28th jan 2021

How to Build the Data Mesh Foundation: A Principled Approach | Zhamak Dehghan...

Fast Data Strategy Houston Roadshow Presentation

Denodo Data Virtualization - IT Days in Luxembourg with Oktopus

Denodo DataFest 2016: Comparing and Contrasting Data Virtualization With Data...

Similar to The Curse of the Data Lake Monster

2020 | Metadata Day | LinkedIn

Deepak Chandramouli

Data Warehousing Trends

Chris Riccomini

Big Data overview

alexisroos

DataEd Slides: Data Architecture vs. Data Modeling – Compare and Contrast

We must grow the data capabilities of our organization to fully deal with the many and varied forms of data. This cannot be accomplished without an intense focus on the many and growing technical bases that can be used to store, view, and manage data. There are many, now more than ever, that have merit in organizations today. This session sorts out the valuable data stores, how they work, what workloads they are good for, and how to build the data foundation for a modern competitive enterprise.

Machine Learning - It's the Data, Stupid

Eoin Delahunty

Using Data Platforms That Are Fit-For-Purpose

Watch full webinar here: https://bit.ly/3mdj9i7 You will often hear that "data is the new gold"? In this context, data management is one of the areas that has received more attention from the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen. In this webinar, we will discuss the technology trends that will drive the enterprise data strategies in the years to come. Don't miss it if you want to keep yourself informed about how to convert your data to strategic assets in order to complete the data-driven transformation in your company. Watch this on-demand webinar as we cover: - The most interesting trends in data management - How to build a data fabric architecture? - How to manage your data integration strategy in the new hybrid world - Our predictions on how those trends will change the data management world - How can companies monetize the data through data-as-a-service infrastructure? - What is the role of voice computing in future data analytic

Schema.org Structured data the What, Why, & How

Richard Wallis

Citizens Bank: Data Lake Implementation – Selecting BigInsights ViON Spark/Ha...

Seeling Cheung

Future of Data Strategy (ASEAN)

The Briefing Room with Rick Sherman and MarkLogic Live Webcast on May 13, 2014 Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=9cd8eec52f7968721fdcd922e4f70369 The number of data types and sources is increasing almost daily anymore, which poses serious challenges for analytics and discovery. With many of these data sets in the Cloud, analysts are realizing that merging such public resources with internal information assets can be quite problematic. Solutions like virtualization and federation can get the job done, but another option is to employ a database that can natively connect to all these external sources. Register for this episode of The Briefing Room to hear veteran Analyst Rick Sherman as he explains how the changing needs of the user are driving database innovation. He’ll be briefed by Ken Krupa of MarkLogic, who will tout his company’s NoSQL document database. He’ll discuss the importance of expanding the definition of what it means to be a database, and he’ll show how MarkLogic’s ability to tap into more sources than ever creates a scale-out data nerve center, thus delivering faster and better insights. Visit InsideAnlaysis.com for more information.

The New Database Frontier: Harnessing the Cloud

Inside Analysis

This session looks at where we are today with data and analytics and what is needed to transition to the Artificially Intelligent Enterprise. How do you mobilise developers to exploit what data scientists and business analysts have built? How do you align it all with business strategy to maximise business outcomes? How do you combine BI, predictive and prescriptive analytics, automation and reinforcement learning to get maximum value across the enterprise? What is the blueprint for building the artificially intelligent enterprise? •Data and analytics – Where are we? •Why is the journey only half-way done? •2021 and beyond – The new era of AI usage and not just build •The requirement – event-driven, on-demand and automated analytics •Operationalising what you build – DataOps, MLOps and RPA •Mobilising the masses to integrate AI into processes – what needs to be done? •Business strategy alignment – the guiding light to AI utilisation for high reward •Agility step change – the shift to no-code integration of AI by citizen developers •Recording decisions, and analysing business impact •Reinforcement-learning – transitioning to continuous reward

Building the Artificially Intelligent Enterprise

Databricks

Driving Business Value Through Agile Data Assets

Embarcadero Technologies

It is a fascinating, explosive time for enterprise analytics. It is from the position of analytics leadership that the mission will be executed, and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics. The coming years will be full of big changes in enterprise analytics and Data Architecture. William will kick off the third year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.

ADV Slides: 2021 Trends in Enterprise Analytics

Watch full webinar here: https://bit.ly/3y4yMPU It’s almost impossible to find any organization that does not have data and analytics as one of their top priorities to further their business objectives. At the same time the data and analytics landscape is evolving faster than ever, making the data management ecosystem more complex than ever before. As data gets increasingly distributed across systems and locations, every forward looking organization should adopt a logical architecture to be future ready. Watch On-Demand and Learn: - Key priorities of data and analytics leaders for business transformation - Why a monolithic and physical data architecture is not suitable for such transformation - How a logical data architecture can help organizations in their business transformation

Adopting a Logical Data Architecture for Today's Data and Analytics Requirements

20 липня відбувся вебінар від Java Community – “Zaloni’s Architecture for Data-Driven Design” by Максим Дем’яновський — Software Engineer, GlobalLogic. Доповідь надасть уявлення про Data-Driven Design, основні його переваги і практичну користь, а також покаже як його можна реалізувати на практиці.

GlobalLogic Java Community Webinar #16 “Zaloni’s Architecture for Data-Driven...

GlobalLogic Ukraine

Watch full webinar here: https://bit.ly/3fBpO2M Data Fabric has been a hot topic in town and Gartner has termed it as one of the top strategic technology trends for 2022. Noticeably, many mid-to-large organizations are also starting to adopt this logical data fabric architecture while others are still curious about how it works. With a better understanding of data fabric, you will be able to architect a logical data fabric to enable agile data solutions that honor enterprise governance and security, support operations with automated recommendations, and ultimately, reduce the cost of maintaining hybrid environments. In this on-demand session, you will learn: - What is a data fabric? - How is a physical data fabric different from a logical data fabric? - Which one should you use and when? - What’s the underlying technology that makes up the data fabric? - Which companies are successfully using it and for what use case? - How can I get started and what are the best practices to avoid pitfalls?

Data Fabric - Why Should Organizations Implement a Logical and Not a Physical...

In-memory database technology enables a new wave of fast data use cases that are extremely challenging and in some cases not possible with older technologies. In this webinar, Noel Yuhanna, Principal Analyst of Forrester Research, and VoltDB CMO, Peter Vescuso will discuss the latest market and data access technology trends, the new use cases these trends enable, and the implications for business and IT leaders.

Memory Database Technology is Driving a New Cycle of Business Innovation

VoltDB

Big Data Pitfalls

Alex Meadows

Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.

ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...

Watch here: https://bit.ly/3i2iJbu You will often hear that "data is the new gold". In this context, data management is one of the areas that has received more attention by the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen. Join us for an exciting session that will cover: - The most interesting trends in data management. - Our predictions on how those trends will change the data management world. - How these trends are shaping the future of data virtualization and our own software.

What is the future of data strategy?

Design System as a Product - Maria Elena Duenias, Esther Butcher Design systems are a great example where web development and design meet. You can find innumerable resources on the internet, books and conferences on how to build them, and how they are exactly what your organization needs. But, building one requires a lot more than following a recipe. In this talk we are going to discuss how to build a design system as an internal product, and how it evolves to become what the users need.

Similar to The Curse of the Data Lake Monster (20)

2020 | Metadata Day | LinkedIn

Data Warehousing Trends

Big Data overview

DataEd Slides: Data Architecture vs. Data Modeling – Compare and Contrast

Machine Learning - It's the Data, Stupid

Using Data Platforms That Are Fit-For-Purpose

Schema.org Structured data the What, Why, & How

Citizens Bank: Data Lake Implementation – Selecting BigInsights ViON Spark/Ha...

Future of Data Strategy (ASEAN)

The New Database Frontier: Harnessing the Cloud

Building the Artificially Intelligent Enterprise

Driving Business Value Through Agile Data Assets

ADV Slides: 2021 Trends in Enterprise Analytics

Adopting a Logical Data Architecture for Today's Data and Analytics Requirements

GlobalLogic Java Community Webinar #16 “Zaloni’s Architecture for Data-Driven...

Data Fabric - Why Should Organizations Implement a Logical and Not a Physical...

Memory Database Technology is Driving a New Cycle of Business Innovation

Big Data Pitfalls

ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...

What is the future of data strategy?

More from Thoughtworks

Design System as a Product

Designers, Developers and Dogs: Finding the magic balance between product and tech - Charlotte Vorbeck, ShareNow and Sahil Bajaj How can an agile delivery team become a successful product team? When does collaboration between product and tech succeed and when not? Why do people in some teams inspire each other while others in the same environment don't speak the same language? In this talk we want to share our learnings and experiences from rebuilding an internal tool for customer support at ShareNow. What could have been just another boring rewrite surprisingly became one of our best experiences in collaboration. We will look at how a joint discovery phase helped us to come up with a shared vision, how a better team setup enabled us to do the necessary work, how focusing on the customer kept us aligned during our journey, and also how we built upon existing collaborative techniques to achieve this new level of cooperation and trust.

Designers, Developers & Dogs

Cloud-first for fast innovation

How to create more business impact with flexible teams - Jan Hegewald, Zalando & Rebekka Beels, Zalando Usually, Software Engineering teams are organized around a fixed set of components which they develop further and maintain. Such component teams gain a high level of expert knowledge about their services. However, with agile product development, it often is difficult to implement the most important initiatives with such teams. This leads to a situation where the teams do not work on the most relevant business topics but on those for the respective team. At Zalando, we introduced a new model where we shape teams flexibly around business goals to create the highest impact. How we organize these teams and which challenges especially for the software quality need to be addressed, will be explored in this talk.

More impact with flexible teams

Culture of Innovation

Dual-Track Agile for Discovery & Development - Adriana Katrandzhieva The talk will focus on one of the ways teams can ensure continuous delivery and design in their projects. The so-called ‘Dual-track’ model shows the parallel tracks of discovery and development throughout the product design and delivery process. These continually feedback into each other informing new hypothesis that can be tested in order to be proven/disproven. This model is not always easy to implement out of the box and so I will share my own experiences in applying it in practice - what worked, what didn't and how the model can be adjusted to fit different teams and organisational environments.

Dual-Track Agile

Designing the Developer Experience - Tanja Bach, Jacob Bo Tiedemann Working with software that some other people have built, is not only daily business for private and business users but also for developers. Just like any other product, a product for developers needs to solve their problems and focus on the right jobs-to-be-done in order to be successfully adopted by the developer community. In this talk, we will explain why the developer experience matters not only to developers but also to the business. We will share our learnings and real-world examples of how we created a developer experience for a cloud infrastructure product and an IoT platform that the developers love.

Developer Experience

When we design together - Sabrina Mach, Ammara Gafoor and James Emmott From three distinct perspectives, this talk will contend that design is an activity undertaken by everyone in a software development team. It occurs throughout the process of delivery — not only at the beginning or the end — and it is a powerful instrument for learning about and adapting to the problems our work seeks to solve, which is a shared responsibility. Making the best use of our multidisciplinary expertise in the activity of design requires forms of collaboration that are too often disrupted by the role-based silos that keep us separated and weaken the valuable contribution our diverse approaches could make to our collective efforts. If you care about accelerating time to market, improving customer experience, or building happy and productive teams, you will want to know why and how it matters that we believe ‘design is in everything that we do’.

When we design together

Hardware is hard(er): designing for distributed user experiences in IoT - Claire Rowland, www.clairerowland.com Designing connected devices and hardware-enabled services is significantly more complex than pure software. There are more devices on which code can run, connectivity and data sharing patterns to consider, and often multiple and varied touchpoints for users to interact with. Pulling this all together into a coherent experience involves strong collaboration between design and engineering, and a systems thinking approach to UX. In this talk, we’ll introduce what designers need to know about the tech, what engineers need to know about UX for IoT, and how to facilitate the whole-collaboration needed to create great products. www.clairerowland.com

Hardware is hard(er)

Customer-centric innovation enabled by cloud

Amazon's Culture of Innovation

Find out how to validate hypotheses quickly using feedback that comes from a (large enough) number of actual users interacting with your product. In this talk, we will show you the technical foundations, research techniques and organisational setup that we have used successfully on large-scale products. These will save you development time, enable you to go live with confidence, make decisions based on real behaviour instead of best guesses, and solve the actual problems your users are facing.

When in doubt, go live

As a tech leader at ThoughtWorks, a large part of my job involves recommending practices to our clients so they can build and deliver good quality software faster. In doing so repeatedly for many clients I have created a toolkit that contains practical advice from being on the ground. This is what we do, we know it works. When Julius Caesar entered Rome with his army by crossing the river Rubicon, he did something that couldn’t be undone ever again. In your journey as a leader, avoid mistakes that are difficult to correct later. Here are a set of practices that you want to adopt as soon as possible.

Don't cross the Rubicon

Handling error conditions is a core part of the software we write. However, we often treat it as a second class citizen, obscuring our intent through abuse of null values and exceptions that make our code hard to understand and maintain. In the functional programming community, it is common to use datatypes such as Option, Either or Validated to make our intentions explicit when dealing with errors. We can leverage the compiler to verify that we are handling them instead of hoping for the best at runtime. This results in code that is clearer, without hidden path flows. We’ll show how we have been doing this in Kotlin, with the help of the Arrow library.

Error handling

Your test coverage is a lie!

Docker container security

Mainframes handle 30 billion business transactions each day and 87% of all credit card transactions*, they are not traditionally associated with flexible, fail-fast development approaches. Can we bring the practices of agile, CI/CD and fully automated deployments to applications running on a mainframe? During our talk, we'll tell you a story about test automation; redefining the smallest testable unit of a program. And we'll discuss our learnings from introducing continuous integration and agile practices to the world of insurance and mainframes. *9 Mainframe statistics that may surprise you

Redefining the unit

Technology Radar Webinar UK - Vol. 22

A Tribute to Turing

Rsa maths worked out