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March 2, 2023

5 reasons to attend DRE Con 2023

In April, DRE Con will bring together many of the world's leading data reliability engineering experts. Why should you attend this virtual conference? We'll tell you 5 great reasons.

Liz Elfman
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On April 19, 2023, we’re gathering the largest collection of data reliability engineers and experts in the world. The third annual Data Reliability Engineering Conference (DRE Con) is going to be bigger and more spectacular than ever before. Here are 5 reasons you should attend this year’s virtual DRE Con:

1. You’ll join a community

By attending DRE Con, you’re joining a community of trailblazers, industry experts, and technical practitioners pushing the bounds of a new field. DRE Con is the one place you can access research-backed sessions from SMEs, get expert advice in real-time on your individual challenges, and interact with peers, all under the data reliability umbrella.

2. You’ll progress on your data reliability engineering journey

How do you become a data reliability expert? How do you avoid some of the pitfalls common to data teams? How can you understand data quality problems from new, interesting angles?  Expand your knowledge, not only from our speakers, but also from those in the community who are facing similar challenges and sharing knowledge.

3. You’ll be in good company

Executives and engineers from the biggest names in tech attend DRE Con. In years past we’ve seen attendees from Google, Netflix, Uber, Meta, Doordash, Microsoft, Shopify, McKinsey, BCG, Stripe, and more. Get in front of hiring managers and partners from the biggest names in tech and data engineering.

4. You’ll discover new data reliability tools

What technology and tools do leading DREs use in their day-to-day? What does their data stack look like? Throughout the day, you’ll hear about innovative technologies and the specific use cases to which they apply. Analyze and compare how leading engineers gain an edge through technology, so you can walk away with some new ideas about how to add value to your organization.

5. You’ll hear from an unparalleled speaking lineup

With Kelsey Hightower of Google and Kubernetes leading the day, you’ll have access to our expert invite-only speaker lineup. Each speaker is a master of their niche in the industry, and they’ll each have an entire 30 minutes to dive deep into their area of expertise. Many of our speakers are published authors, and all have held leadership roles in some of the world’s biggest, most influential organizations, from Stack Overflow to the FBI.

Not registered yet? Secure your free spot at DRE Con today.

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Resource
Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
$15,000
3
12
$540,000
Data analyst
$12,000
2
6
$144,000
Business analyst
$10,000
1
3
$30,000
Data/product manager
$20,000
2
6
$240,000
Total cost
$954,000
Role
Goals
Common needs
Data engineers
Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.
Freshness + volume
Monitoring
Schema change detection
Lineage monitoring
Data scientists
Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.
Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing
Analytics engineers
Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.
Lineage monitoringETL blue/green testing
Business intelligence analysts
The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.
Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing
Other stakeholders
Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
Integration with analytics toolsReporting and insights

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