Blog

Engineering

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

Engineering

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.

mParticle Product

Personalize Support with mParticle and Zendesk

You wouldn’t expect your Marketing or Product teams to function without data. Show your support team some love and help them provide personalized service, using the same data points you’re already collecting.

mParticle Product

Smartype Custom Receivers

mParticle’s Smartype is an open-source tool that delivers data quality benefits to any engineering team, whether or not you’re working with an mParticle Data Plan. Here, we’ll review how to automatically generate typesafe libraries with Smartype, and learn how Smartype can send user events to any third-party database or library.

mParticle Product

Get ready for Apple’s App Tracking Transparency Framework

This week’s release of iOS 14.5 marks the beginning of enforcement of Apple’s App Tracking Transparency (ATT) Framework, announced last year. The ATT brings significant changes to the way iOS apps handle privacy and user tracking transparency. mParticle is continuing to roll out updates to help customers adhere to the new framework, as well as tools you can use to build a future-proof data strategy based on first-party customer data infrastructure.

Growth

How to set up your Customer Data Platform team

In part two of our two-part series on making the most of your Customer Data Platform, we discuss how you can set up a cross-functional team of 'customer data excellence' to operate your CDP.

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.

Engineering

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.

Growth

Where should you be building your audience segments?

Every tool seems to offer segmentation capabilities these days. Disconnected audiences builders, however, can lead to overreliance on developer support and disjointed customer experiences. Learn more about data infrastructure and when it helps to centralize your audience segmentation.

Engineering

The value of a universal customer ID across your tech stack

Teams across industries are striving to create a 360-degree customer view. But if that view isn't seamlessly integrated with the tools and systems throughout the tech stack in real time, growth teams aren't able to use it to drive results. Learn more about how you can implement a universal ID and make it available across the stack.

Engineering

Relational vs. Non-Relational Databases

What are the key differences between these two main categories of databases, and how do you select the right type of database for different use cases?

Engineering

Data enrichment and machine learning: Maximizing the value of your data insights

Data enrichment and machine learning are two techniques that can enhance the ability of your customer data to drive personalized experiences. While there is some overlap in the end goal of both approaches to enhancing data value, there are significant differences in the time, resources, and overhead they each require.