There’s a good chance you’re an analytics engineer who just sort of landed in an analytics engineering career. Or made a murky transition from data science/data engineering/software engineering to full-time analytics person. When did you realize you fell into the wild world of analytics engineering?

In this session, Michael Chow (RStudio) draws upon his experience building open source data science tools and working with the data science community to discuss the early signs of a budding analytics engineer, and the small steps these folks can take to keep the best parts of Python and R, all while moving towards engineering best practices.

Check the slides here: https://docs.google.com/presentation/d/1H2fVa-I4D8ibanlqLutIrwPOVypIlXVzEITDUNzzPpU/edit?usp=sharing

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

The year is 2022, data teams are earning their stripes, data work is being valued, and our organizations recognize that analytics engineering is no longer the future of data teams—it’s the present. Join Mong Dang as she discusses how Aritzia, a normal company with a talented and growing roster of data team members, scaled up their delivery of quality data through analytics engineering and leveraged their data product management approach. Like all experimental work, Mong will cover what they tried, what has been working, what didn’t work, and where they still have questions and can learn more on.

Check the slides here: https://drive.google.com/file/d/1iZkrLvF6UkzE_XfF06B4dZncRSMo6nNY/view?usp=sharing

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

Roleplaying games? Roleplaying games in data? You read that right. To Ian, there's more commonality in roleplaying games and the data world than most of us think. In this session, Ian Fahey (dbt Labs) will draw on his vast experiences in roleplaying games and analytics engineering work to walk through the adventuring classes of "the world's most popular tabletop roleplaying game" (Dungeons and Dragons) and talk about how they can inform data professionalism.

Check the slides here:https://docs.google.com/presentation/d/16Wm4ChDPORvEkDxUu3-mHBYRLJtwUTRdP7rj-LIrUB4/edit#slide=id.g1571952a68b_0_12

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

As analysts lean further into analytics engineering workflows, they’ll need to source opportunities for more hands-on experience. Forunately, this is achieveable just by applying a fresh framework to existing analytics projects. In this session with Brittany Krauth (Degreed), you’ll learn how to approach common problems through the analytics engineering workflow.

Check the slides here: https://docs.google.com/presentation/d/1RO7EAbHqcxsRWp40EKusANwD5IwUAoEs/edit?usp=sharing&ouid=110788023771657617483&rtpof=true&sd=true

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

Academia/open science is an as-yet untapped market for analytics engineering, as well as one that could majorly benefit from the tight coupling of data transformation and software engineering best practices. But introducing dbt into this context comes with its own set of challenges. In this session, Šimon Podhajský (iLife Technologies), explains what’s slowing progress here,, and what academics can do to progress this work.

Check the slides here: https://docs.google.com/presentation/d/1aw_cs6V0n-oT9Lp7Vq3MNcRbthEFJEYwcvkCBfuzlR0/edit?usp=sharing

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

See how visual data modeling and dbt combine to improve interaction and understanding between analytics engineering practitioners, product owners, and business partners. We will demonstrate conceptual and logical modeling techniques and diagrams to establish common understanding, enhance business partner collaboration, enhance translation of requirements, and ultimately complement analytics engineering within dbt to improve time to value. Demonstrate how to pair data modeling concepts (conceptual, logical, physical) and tools (SqlDBM) to engage your customers and inform the analytics engineering with dbt and Snowflake. We will show how this workbench and tools complement the analytics lifecycle for engineers and data consumers alike. The workbench includes a dbt, a visual modeling tool, and phData Toolkit CLI.

This session requires pre-registration. Sign up here. If session is filled you are welcome to come to the room and join the waitlist onsite. Open seats will be made available 10 minutes after session start.

Check the slides here: https://docs.google.com/presentation/d/1fJhaMGvD7TvVft4nEJYhMRhyanQTw3lbzLrgZFsmj-0/edit?usp=sharing

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

Data apps are the next wave in analytics engineering. The explosion of data volume and variety combined with an increasing demand for analytics by consumers, and a leap in cloud data technologies triggered an evolution of traditional analytics into the realms of modern data apps. Question is: How do you prepare for this wave? In this session we’ll explore real-world examples of modern data apps, and how the modern data stack is advancing to support sub-second and high concurrency analytics to meet the new wave of demand. We will cover: performance challenges, semi-structured data, data freshness, data modeling and toolsets.

Check the slides here: https://docs.google.com/presentation/d/1MC18SgT_ZHOJePjYizz_WT7dVveaycNw/edit?usp=sharing&ouid=110293204340061069659&rtpof=true&sd=true

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

As analytics engineers and data people, we know the value we create in our own blood, sweat, and dbt models. But how is this value actually realized in practice? In this talk, David Jayatillake (Metaplane) draws on his experiences to discuss the processes, ways of thinking, tooling, and governance needed to realize the benefits from analytics engineering work in the greater organization.

Check the slides here: https://docs.google.com/presentation/d/1VmmqNQsrv1t0uuV81O6PJQ1XASyLRGxvAdB8eWIG9TQ/edit?usp=sharing

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

Katie was a founding member of Reddit's data science team and, currently, as Twitter's Data Science Manager, she leads the company's infrastructure data science and analytics organization. In this conversation with Tristan and Julia, Katie explores how, as a manager, to help data people (especially those new to the field!) do their best work. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

dbt's analytics engineering workflow has been adopted by 11,000+ teams, and quickly become an industry standard for data transformation. This is a great chance to see why.

dbt allows anyone who knows SQL to develop, document, test, and deploy models. With the native, SQL-first integration between Databricks and dbt Cloud, analytics teams can collaborate in the same workspace as data engineers and data scientists to build production-grade data transformation pipelines on the lakehouse.

In this live session, Aaron Steichen, Solutions Architect at dbt Labs will walk you through dbt's workflow, how it works with Databricks, and what it makes possible.

Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data... Instagram: https://www.instagram.com/databricksinc/

We've come a long way from the way data analysis used to be done. The emergence of the analytics engineering workflow, with dbt at its center, has helped usher in a new era of productivity. Not quite data engineering or data analysis, analytics engineering has enabled new levels of collaboration between two key sets of practitioners.

But that's not the only coming together happening right now. Enabled by the open lakehouse, the worlds of data analysis and AI/ML are also converging under a single roof, hinting at a new future of intertwined workloads and silo-free collaboration. It's a future that's tantalizing, and entirely within reach. Let's talk about making it happen.

Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data... Instagram: https://www.instagram.com/databricksinc/

Day 1 Morning Keynote | Data + AI Summit 2022 Welcome & "Destination Lakehouse" | Ali Ghodsi Apache Spark Community Update | Reynold Xin Streaming Lakehouse | Karthik Ramasamy Delta Lake | Michael Armbrust How Adobe migrated to a unified and open data Lakehouse to deliver personalization at unprecedented scale | Dave Weinstein Data Governance and Sharing on Lakehouse |Matei Zaharia Analytics Engineering and the Great Convergence | Tristan Handy Data Warehousing | Shant Hovespian Unlocking the power of data, AI & analytics: Amgen’s journey to the Lakehouse | Kerby Johnson

Get insights on how to launch a successful lakehouse architecture in Rise of the Data Lakehouse by Bill Inmon, the father of the data warehouse. Download the ebook: https://dbricks.co/3ER9Y0K

Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data... Instagram: https://www.instagram.com/databricksinc/

As Head of Analytics at Clearbit, Julie serves as a data team of one in a 200+ person company (wow!). In this conversation with Tristan and Julia, Julie dives into how she's helped Clearbit implement data activation throughout the business, and realize the glorious dream of self-serve analytics. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

Matt Bornstein and Jennifer Li (and their co-author Martin Casado) of a16z have compiled arguably the most nuanced diagram of the data ecosystem ever made.  They recently refreshed their classic 2020 post, "Emerging Architectures for Modern Data Infrastructure" and in this conversation, Tristan attempts to pin down: what does all of this innovation in tooling mean for data people + the work we're capable of doing? When will the glorious future come to our laptops? For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

ClickHouse, the lightning-fast open source OLAP database, was initially released in 2016 as an open source project out of Yandex, the Russian search giant. In 2021, Aaron Katz helped form a group to spin it out of Yandex as an independent company, dedicated to the development + commercialization of the open source project. In this conversation with Tristan and Julia, Aaron gets into why he believes open source, independent software companies are the future. And of course, this conversation wouldn't be complete without a riff on the classic "one database to rule all workloads" thread. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

Justin Borgman is the co-founder, Chairman and CEO of Starburst, and has almost a decade spent in senior executive roles building new businesses in the data warehousing and analytics space.  In this conversation with Tristan and Julia, Justin dives into the nuts and bolts of Trino, the open source distributed query engine, and explores how teams are adopting a data mesh architecture without making a mess.  For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

Amit Prakash is Co-founder and CTO at ThoughtSpot. He has a deep background in search, having previously led the AdSense engineering team at Google and served on the early Bing team at Microsoft. In this conversation with Tristan and Julia, Amit gets real about the promise of AI in data: which applications are being widely used today, and which are still a few years out? For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

Most recently leading a data engineering team at Perpay, Sarah has built and managed data platforms end to end by working closely with internal engineering, product, and operational teams. She recently left her role to pursue a wide variety of endeavors, including writing on her Substack (https://sarahsnewsletter.substack.com/). In this conversation with Tristan and Julia, Sarah dives into how configuration-as-code can automate away data work, why you might want to consider adding a data lake to your architecture, and how those looking to build a self-serve data culture can look to self-serve frozen yogurt shops for inspiration. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

A debate has erupted on data Twitter and data Substack - should the modern data stack remain unbundled, or should it consolidate? In this conversation, Benn Stancil (Mode), David Jayatillake (Avora) and our host Tristan Handy try to make some sense of this debate, and play with various future scenarios for the modern data stack.  For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

We talked about:

Juan Pablo's Backround Data engineering resources Teaching calculus Transitioning to Analytics Data Analytics bootcamp Getting money while studying Going to meetups to get a job Looking for uncrowded doors Using LinkedIn Portfolio Talking to people on meetups Eight tips to get your first analytics job Consider contracts and temporary roles Getting experience with non-profits Create your own internship Networking Website for hosting a portfolio I’m a math teacher. What should I learn first? Analytics engineering Best suggestion: keep showing up Networking on online conferences Communication skills and being organized

Links:

Website: https://www.thatjuanpablo.com/ Twitter: https://twitter.com/thatjuanpablo BROKE teacher to FAANG engineer Twitter thread: https://twitter.com/thatjuanpablo/status/1475806246317875203 LinkedIn: https://www.linkedin.com/in/thatjuanpablo/

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