Blog

reverb-data-workflows

How Reverb’s engineers optimized their data workflows at scale and gave users the rockstar treatment

With mParticle at the heart of their data stack, engineers at the world’s largest online music marketplace said goodbye to burdensome ETL pipelines, slashed their data maintenance workload, and unlocked new opportunities to build data-driven features into their product.

Sean Ryan

How to assemble a cross-functional data quality team

Smash your data silos and improve data quality across your organization by assembling a cross-functional team to own data planning.

Sean Ryan – October 20, 2021

What is data integrity and why does it matter for customer data?

Integrity is a good quality. Just like you want the people around you to have integrity, you also want the data on which you base strategic decisions to be of high integrity as well. That sounds good, but what does it mean for data to have integrity, and why is this so important? In this post, we’ll explore this broad and nuanced concept, define what it means in the context of customer data, and learn a strategy to ensure your customer data maintains high integrity throughout its lifecycle.

September 14, 2021

Debug customer event collection in real time

If you are responsible for implementing data tracking plans across your apps and websites, you’re probably familiar with how tedious and time consuming it can be to track down data collection bugs when they pop up. This video walks through how you can use mParticle’s Live Stream to simplify your team’s testing and debugging cycles.

Sean Ryan – September 09, 2021
mparticle-data-integrations

Everything you need to know about data integrations

Data integrations are ubiquitous throughout the SaaS ecosystem. But not all data integrations are created equal. This article walks through the different types of integrations commonly available and provides tips on how to choose the right integration types for your use cases.

Joey Colvin – September 02, 2021

How do CDPs benefit engineers?

Customer Data Platforms (CDPs) have traditionally been thought of as tools that benefit marketers and product managers. But from simplifying data collection to enabling data-driven feature development, CDPs have far-reaching value for engineers as well. Learn more about the benefits of CDPs for technical teams.

Sean Ryan – August 24, 2021

What is a data tracking plan, and why should engineers care?

"Wait, why do we need this data again?" "Was that attribute supposed to use snake or camel case?" Data tracking plans keep everyone in your organization aligned on your data efforts, from the high-level strategy to the nittiest, grittiest details.

Sean Ryan – August 20, 2021

What is a UUID?

The challenge of identifying data shared between systems dates back to the advent of networked computing. One of the earliest solutions to this problem, the Universally Unique Identifier (UUID), is still in wide use today. Here, we’ll explore this ever-present data identifier in detail.

July 12, 2021

How we improved our core web vitals by migrating to Gatsby

By migrating the architecture of this website to Gatsby, we were able to double key core web vitals, increase our accessibility rating by 50%, and boost our SEO scores from 80 to 100

Sean Ryan – May 18, 2021

What is Gatsby?

Gatsby is an open-source framework that combines functionality from React, GraphQL and Webpack into a single tool for building static websites and apps. Owing to the fast performance of the sites it powers, impressive out-of-the-box features like code splitting, and friendly developer experience, Gatsby is fast becoming a staple of modern web development.

May 11, 2021
What is data engineering?

What is data engineering?

The quantity and complexity of the data that companies deal with is constantly increasing. While Data Scientists analyze and generate actionable insights from data, they cannot do this effectively with data that suffers from poor quality. Data Engineering roles exist in companies to build data pipelines, transform data into useful formats and structures, and ensure quality and completeness in data sets.

Sean Ryan – April 08, 2021
Comparing SQL and Python for Data Analysis use cases.

Python and SQL: Complementary tools for complex challenges in data science

While Data Scientists today have an ever-expanding list of toolkits, languages, libraries and platforms at their disposal, two mainstays––Python and SQL––are likely to remain staples of data analysis for years to come. Here, we’ll look at the role these languages play in the rapidly evolving field of Data Science.

Sean Ryan – March 31, 2021