Supercharge your analytics engineering with the power of automated CI checks. Learn how FINN, a global car subscription service, has harnessed the capabilities of automated CI checks to maintain the delicate balance between swift development and robust data pipeline quality as they've scaled their data teams. Dive into insights and strategies to ensure quality without sacrificing speed and discover how to improve your data operations.

Speakers: Chiel Fernhout, Software Engineer, Datafold; Jorrit Posor, Tech Lead Data Engineering, FINN GmbH; Felix Kreitschmann, Senior PM, Data, FINN Auto

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Behind any good DataOps within a Modern Data Stack (MDS) architecture is a solid DevOps design! This is particularly pressing when building an MDS solution at scale, as reliability, quality and availability of data requires a very high degree of process automation while remaining fast, agile and resilient to change when addressing business needs.

While DevOps in Data Engineering is nothing new - for a broad-spectrum solution that includes data warehouse, BI, etc seemed either a bit out of reach due to overall complexity and cost - or simply overlooked due to perceived issues around scaling often attributed to the challenges of automation in CI/CD processes. However, this has been fast changing with tools such as dbt having super cool features which allow a very high degree of autonomy in the CI/CD processes with relative ease, with flexible and cutting edge features around pre-commits, Slim CI, etc.

In this session, Datatonic covers the challenges around building and deploying enterprise-grade MDS solutions for analytics at scale and how they have used dbt to address those - especially around near-complete autonomy to the CI/CD processes!

Speaker: Ash Sultan, Lead Data Architect, Datatonic

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As Joe Reis recently opined, if you want to know what’s next in data engineering, just look at the software engineer. The MDS-in-a-box pattern has been a game changer for applying software engineering principles to local data development– improving the ability to share data, collaborate on modeling work and data analysis the same way we build and share open source tooling.

This panel brings together experts in data engineering, data analytics and software engineering to explore the current state of the pattern, pieces that remain missing today and how emerging tools and data engineering testing capabilities can refine the transition from local development to production workflows.

Speakers: Matt Housley, CTO, Halfpipe Systems; Mehdi Ouazza, Developer Advocate, MotherDuck; Sung Won Chung, Solutions Engineer, Datafold; Louise de Leyritz, Host, The Data Couch podcast

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Two years ago, the data team at Carsales embarked on a journey to modernize their data platform, transforming their approach to analytics, reporting, and data-driven decision making. Navigating through an ever-evolving data landscape, Carsales has prioritized tools and methodologies that boost operational efficiency. This talk delves into their strategic transition towards a decentralized data platform, emphasizing the design to elevate scalability, reliability, and accessibility by embracing best-in-class software engineering practices. By enabling the application of SQL-based infrastructure, they've ensured that even individuals with minimal data engineering background can extract valuable insights while upholding system reliability.

Speaker: Adam Carbone, Data Engineering Lead, Carsales

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In this presentation, Sarah and Darren discuss RMIT University's journey to implementing the modern data stack with dbt. They bring tales of their musical successes and misadventures, lessons learned with both music and data engineering, and how these seemingly disparate worlds overlap.

Speakers: Darren Ware, Senior Data Engineer, RMIT University; Sarah Taylor, Lead Data Engineer, RMIT University

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Hear from Red Ventures about how through the creation of standard implementation playbooks, their data team is able to quickly roll out modern tools like dbt and Hightouch across businesses to drive growth.

Speakers: Tejas Manohar, cofounder/co-CEO, Hightouch; Brandon Beidel, Director of Data Engineering, Red Ventures

Register for Coalesce at https://coalesce.getdbt.com

Join the team from Moody's Analytics as they take you on a personal journey of optimizing their data pipelines for data quality and governance. Like many data practitioners, Ryan and Ravi understand the frustration and anxiety that comes with accidentally introducing bad code into production pipelines—they've spent countless hours putting out fires caused from these unexpected changes.

In this session, Ryan and Ravi recount their experiences with a previous data stack that lacked standardized testing methods and visibility into the impact of code changes on production data. They also share how their new data stack is safeguarded by Datafold's data diffing and continuous integration (CI) capabilities, which enables their team to work with greater confidence, peace of mind, and speed.

Speakers: Gleb Mezhanskiy, CEO, Datafold; Ravi Ramadoss, Director of Data Engineering, Moody's Analytics CRE; Ryan Kelly, Data Engineer, Moody's Analytics CRE

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Traditional data warehouses excel at churning through terabytes of data for historical analysis. But for real-time, business-critical use cases, traditional data warehouses can’t produce results fast enough—and they still rack up a huge bill in the process.

So when Ramp’s data engineering team needed to serve complex analytics queries on the critical path of their production application, they knew they needed a new tool for the job. Enter Materialize, the first operational data warehouse. Like a traditional data warehouse, Materialize centralizes the data from all of a business’s production systems, from application databases to SaaS tools. But unlike a traditional data warehouse, Materialize enables taking immediate and automatic action when that data changes. Queries that once took hours or minutes to run are up-to-date in Materialize within seconds.

This talk presents how Ramp is unlocking new real-time use cases using Materialize as their operational data warehouse. The best part? The team still uses dbt for data modeling and deployment management, just like they are able to with their traditional batch workloads.

Speakers: Nikhil Benesch, CTO, Materialize; Ryan Delgado, Staff Software Engineer, Data Platform, Ramp

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It’s 2023, why are software engineers still breaking analytics reporting? We’ve all been there, being alerted by an analyst or C-level stakeholders, saying “this report is broken”, only to spend hours determining that an engineer deleted a column on the source database that is now breaking your pipeline and reporting.

At Xometry, the data engineering team wanted to fix this problem at its root and give the engineering teams a clear and repeatable process that allowed them to be the owners of their own database data. Xometry named the process DPICT (data pipeline contract) and built several internal tools that integrated seamlessly with their developer’s microservice toolsets.

Their software engineers mostly build their database microservices using Postgres, and bring in the data using Fivetran. Using that as the baseline, the team created a set of tools that would allow the engineers to quickly build the staging layer of their database in the data warehouse (Snowflake), but also alert them of the consequences of removing a table or column in downstream reporting.

In this talk, Jisan shares the nuts and bolts of the designed solution and process that allowed the team to onboard 13 different microservices seamlessly, working with multiple domains and dozens of developers. The process also helped software engineers to own their own data and realize their impact. The team has saved hundreds hours of data engineering time and resources not having to chase down what changed upstream to break data. Overall, this process has helped to bring transparency to the whole data ecosystem.

Speaker: Jisan Zaman, Data Engineering Manager, Xometry

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A financial institution's success hinges on its ability to remain competitive in the digital space and the evolve its data culture. Fifth Third’s vision for data modernization isn’t just about adopting the latest technologies; it’s about enhancing the employee experience with data and promoting Data Product ownership. This breakout explores the bank’s transition from legacy to modern data architecture, showcasing its realistic commitment to people, process and technology.

Speakers: Kayleigh Lavorini, VP - Director of Product Ownership | Data Strategy, Fifth Third Bank; Ben Rottinghaus, SVP | Senior Director, Data Engineering, Fifth Third Bank

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Two public servants at the City of Boston and the State of California are tasked with improving data services and data engineering practices within their respective governments. As part of the modernization process, they are adopting dbt and associated tools within their respective teams.

This session discusses the similarities and differences between the implementations of dbt, and how some of the constraints and challenges of working in government shape both the technical and social design of data services. The speakers will reflect on successes, challenges, and lessons learned about adopting modern data tooling in state and local governments.

Speakers: Jenna Jordan, Data Engineer, City of Boston; Ian Rose, Senior Data Engineer, California Office of Data and Innovation; Laurie Merrell, Senior Analytics Engineer, Jarvus Innovations

On Survivor, it's all fun and games until someone fails and a challenge is lost. People scramble, they frantically search for hidden immunity idols (and sometimes even crazier things happen).

Within data teams, everything is fine until that one Slack alert comes in about a failed run. Chaos doesn't need to ensue between analytics and data engineers when triaging an error with the warehouse or upstream infrastructure. Order fulfillment? Orders are initiated by a user in a web app. Fraud detection? The logic is usually based on ML models that require training and testing. Workflows are connected, making disparate systems a necessity to connect.

The keys to success on Survivor, and on data teams: create links to find hidden immunity idols—I mean, the root cause of why a workflow failed.

Run double duty during this talk: learn about Survivor social science and how to bridge the gap between analytics and data engineering beyond the warehouse for a one-happy-family feeling.

Speaker: Sarah Krasnik Bedell, Growth Lead, Prefect

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