If you want to scare a Data Engineer with four words, ‘big data, high concurrency’ will probably do it. As data moved from the realm of BI reporting to being a customer-facing commodity, serving huge volumes of data to thousands of unforgiving app users is no small challenge. In this session, Connor Carreras will share (and demo!) how a major martech platform uses Firebolt to serve data about millions of websites to their worldwide customers with consistent millisecond response times. After this session, you will know how you can build low-latency data applications yourself. You’ll also have a deep understanding of what it takes for modern high-performance query engines to do well on these workloads.

In this session Connor will dive into optimizing compute resources, accelerating query performance, and simplifying data transformations with dbt and cover in detail: - SQL-based data transformation, and why is it gaining traction as the preferred language with data engineers - Life cycle management for native objects like fact tables, dimension tables, primary indexes, aggregating indexes, join indexes, and others. - Declarative, version-controlled data modeling - Auto-generated data lineage and documentation

Learn about incremental models, custom materializations, and column-level lineage. Discover practical examples and real-world use cases how Firebolt enables data engineers to efficiently manage complex tasks and optimize data operations while achieving high efficiency and low latency on their data warehouse workloads.

Speaker: Connor Carreras Solutions Architect Firebolt

Read the blog to learn about the latest dbt Cloud features announced at Coalesce, designed to help organizations embrace analytics best practices at scale https://www.getdbt.com/blog/coalesce-2024-product-announcements

A key task that any aspiring data-driven organization needs to learn is data wrangling, the process of converting raw data into something truly useful. This practical guide provides business analysts with an overview of various data wrangling techniques and tools, and puts the practice of data wrangling into context by asking, "What are you trying to do and why?" Wrangling data consumes roughly 50-80% of an analyst’s time before any kind of analysis is possible. Written by key executives at Trifacta, this book walks you through the wrangling process by exploring several factors—time, granularity, scope, and structure—that you need to consider as you begin to work with data. You’ll learn a shared language and a comprehensive understanding of data wrangling, with an emphasis on recent agile analytic processes used by many of today’s data-driven organizations. Appreciate the importance—and the satisfaction—of wrangling data the right way. Understand what kind of data is available Choose which data to use and at what level of detail Meaningfully combine multiple sources of data Decide how to distill the results to a size and shape that can drive downstream analysis