Ascend.io, the Data Automation Cloud named a 2021 Gartner Cool Vendor, provides the most advanced automation for data and analytics engineering workloads. Ascend unifies data engineering’s core capabilities—ingestion, transformation, delivery, orchestration, and observability—into a single platform so teams deliver 10x faster. Engineering productivity is a top priority, and with Ascend’s DataAware™ intelligence, data teams no longer spend hours orchestrating brittle data workloads and instead rely on advanced automation to optimize the entire data lifecycle.
Data Automation Summit: 2022
April 13 - 14, 2022
Data Automation Summit 2022
About the Data Automation Summit
The Data Automation Summit is a free, virtual event that brings together data and analytics engineering leaders and practitioners to discuss the data automation challenges, case studies, best practices, and more that are pushing their data teams—and businesses—forward.
The breadth of capabilities data teams need to automate is virtually endless. The Data Automation Summit will accelerate the automation conversation among data professionals and walk through how some of today’s most innovative companies are using this powerful technology to create highly sophisticated data platforms and products.
Data Automation Summit sponsorships provide a variety of branding, lead generation, speaking opportunities, and more.
If you are interested in sponsoring future Data Automation Summits, please contact our Sponsorship team.
From Airbnb to Zocdoc, the world’s top startups build on AWS. But they didn’t do it alone. So whether you’re looking for help solving a technical challenge, hiring the right engineers, or finalizing a fundraising round, we have all the resources you need to get started. There’s a reason more startups build on AWS than any other provider: we’re here to help you succeed, from inception to IPO.
Analytics8 is a data and analytics consultancy. We help companies translate their data into meaningful and actionable information so they can stay ahead in a rapidly changing world. From data strategy to implementation, we design, develop, and deploy modern data and analytics solutions to enable our clients to transform and grow their business.
Moviri consultants and engineers use data, software and insights to solve substantial business challenges for Fortune 500 corporations, multinational banks, media conglomerates and some of the most respected global brands in a variety of industries.
Wednesday, April 13th
The Future of Data Engineering with Automation
Sean Knapp, Founder and CEO of Ascend.io, welcomes you to the 2022 Data Automation Summit with a discussion of the rise of automation's importance in today's modern data stack. Then Sean sits down with Ascend's Tom Weeks for a fireside chat about what attendees can look forward to seeing at this year's event.
Speed to Data Insights: the HNI Approach
How Automation Accelerates Data Workloads for User Consumption
Automating the Komodo Health Data Platform
DevOps & DataOps for Cloud Native Automation of Data Pipelines
Maytronics, a world leader manufacturer of robotic swimming pool cleaners, needs to manage and process large amounts of data coming from their products to enable advanced business analysis. From the application point of view, to achieve this result, it is necessary to develop and maintain ETL pipelines efficiently, enabling data ingestion, processing and transformations. These processes require a cloud architecture designed and configured specifically to provide maximum data availability to the final users.
What Your Phone Data Can Say About Your Mental Health
At Mindstrong, we collect phone typing data to extract features around typing dynamics.This talk will cover how we go into those logs, to inferring 5 areas of your cognitive state. We'll describe the end to end pipeline, from collection, to how we validate from science literature, that what we're inferring is directionally correct.
Developer Experience and the Modern Data Stack
Lessons from the Field: Transforming Ports of Auckland
Demystifying the Data
Goal: Strategic and innovative Digital Data Transformation initiative and transportation/public use case, which translated business vision into a successfully integrated solutions that improved performance, profitability, growth, and employee and customer engagement. The data initiative was a project on to itself. What we did: Implement new Information Technology RAIL INNOVATION DEVELOPMENT SOLUTIONs (RIDES) and directed Operations & Maintenance organization newly launched start-up operations from cleansing to auditing the data the effort and focus needed for success—a step by step process. Will share techniques, timetable, and timeline.
Data Automation: The Backbone of Real-time Machine Learning
Machine learning enables businesses to continuously automate and improve key business metrics increasing top-line revenues and decreasing costs. Recently there is increased interest around real-time machine learning which enables a business to leverage real-time user signals, in the millisecond time-range, and use those signals to optimize consumer experiences. Underlying any production real-time machine learning deployment is data automation. Data automation is required to both obtain a reliable stream of inputs to model training and to perform real-time inference. In addition, data automation is a prerequisite within the real-time machine learning pipeline for tasks like feature engineering.
Automating Batch + Streaming Analytics with Ascend and Imply
Navigating Top Challenges to Unlocking Value from Data
A fireside chat discussing how the Dr. Ratneesh Suri, Head of Data Analytics at the Ports of Auckland, began her career in data team leadership and what important lessons she’s learned as she’s worked in and led some of the world’s largest data analytics organizations.
Thursday, April 14th
How to Eliminate Data Downtime and Start Trusting Your Data
Asking the Right Questions: Supporting Explainable, Question-Driven Profiling for Data
Getting started with a data quality project can present a major challenge. Common anomaly detection techniques can create useful alerts, but often create systems that are hard to inspect. In this talk, James Campbell will discuss a modular, composable framework for building Expectations about data using components built around asking good questions about data.
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Automation in Amazon Redshift Serverless
Amazon Redshift Serverless enables you to get started in seconds and run data warehousing and analytics workloads at scale without worrying about data warehouse management. In this session, learn how Amazon Redshift automatically provisions data warehouse capacity and intelligently scales the underlying resources to deliver consistently high performance and simplified operations for even the most demanding and volatile workloads.
Automating Data Pipelines Across AWS Services
AWS provides the world’s most scalable cloud services for data and analytics processing, with different services dedicated to different types of workloads, like databases, streams, batch analytics, machine learning and business intelligence. However, to knit together these services into comprehensive, highly automated data pipeline platforms requires deep and highly specialized data engineering skills. Ascend presents a new class of software that solves many of the hundreds of edge cases unique to data pipelines, empowering data engineers to implement and operationalize data pipelines across many of the AWS services far faster and more reliably than ever before.
Automation and the Money Value of Time
In the information age, compelling analogies for data are legion. Data shares functional properties with such critical commodities as blood, oil, water, air, and food. For example, data, like air, is the conduit of thr critical combustible of a given process, like oil is the primitive of further refined products, like water is predominantly present and not always accessible, like food can be both nutritive and poisonous. And yet, as data professionals, we not infrequently struggle to secure funding for projects, fail to deliver sufficient value when funded, and not rarely see procurement or architectural decisions decided by a cadre with less technical understanding of data. With the separation of storage and compute, data architects are closer to the focus of enterprise architecture than ever before and should be more able to directly and visibly support - and therefore influence - enterprise strategy than ever before. This last point is key and let us speak plainly about it. Data architectures and platforms must deliver measurable value to the enterprise. Measurability requires quantification and atomic traceability. Value requires prioritization and success. In this presentation we will explore how Ascend.io helps data teams deliver compounding, measurable value to the enterprise.
Data Automation for the Lean Startup
Lean engineering teams and tight budgets can keep many data engineering projects sidelined at tech startups. However, the tools available today enable lean teams to take advantage of big data architecture to deliver data products quickly and without breaking the bank. In my session, I’ll talk about the evolution of data challenges at a fast-growing startup and how we solved them without the need to hire a team of data engineers.
Deal with Real Datasets: How to Treat Data Assets to Create Business Value
In this presentation it is shown an example of how companies can treat proprietary data to create business value - from the storage of raw records until the deployment of a new containerised application. Overall, the development of an Automated MLOps pipeline is described. In particular, the focus is put on which data engineering skills are required in order to satisfy the requests of product owners. Due to this, suggestions about software tools, libraries and learning resources are given. The audience for which this work was conceived are organizations that want to become data driven and Students/Graduates that want to have insights into the industry of data.
Data at Scale 2.5 PB, 60B Rows: Tagging, Analyzing, Delivering
How to Maximize the Impact of Your Data Automation Initiatives
In order to thrive and elevate the role of data in your organization, you can’t just focus on the technology, data, and architecture. You need to build solutions that are impactful and bring measurable benefits. But how do you align your technical work with business objectives and priorities? How do you know if you are focusing on the right problems that will bring the most value to your company? In this presentation, Tony will discuss: * Why it is crucial to care about the impact of your data team’s work * Common challenges and roadblocks that often prevent solutions from bringing maximum value * Our secret sauce to developing valuable solutions * Real life examples of building technical solutions that bring business impact
Three Eras of Data Engineering at Fivestars
Accelerating the Power of Curiosity with Automation
Sean Knapp will sit down with former U.S. Chief Data Scientist DJ Patil to discuss how the role of the data scientist has evolved and solidified over the last 10-15 years and what the next 15+ years may bring with the rise of data automation technologies.