Blog

How to choose the right foundation for your data stack

If you’re relying on downstream activation tools to combine data events into profiles, don’t. You’ll end up with fragmented and redundant datasets across systems. Enriching each data point before it is forwarded downstream will prevent this problem, but not all customer data infrastructure solutions deliver this capability.

Sean Ryan – March 02, 2022

Clear costs: How we used data aggregation to understand our Cost of Goods Sold

Understanding our cost allocation on the level of individual customers and services is an important metric for us to track. However, the major cloud providers do not readily provide this information, so to obtain it, our data engineering had to get creative. This case study describes how we built a custom library that combines data housed in disparate sources to acquire the insights we needed.

Matt Phillips – February 16, 2022

Smartype Hubs: Keeping developers in sync with your Data Plan

Implementing tracking code based on an outdated version of your organization's data plan can result in time-consuming debugging, dirty data pipelines, and misguided decisions. mParticle's Smartype Hubs helps your engineering team avoid these problems by importing the latest version of your Data Plan into your codebase using Github Actions.

Sean Ryan – February 11, 2022

A simpler way to implement and maintain video analytics code

Video analytics are essential to maximizing the impact and value of video content. For technical teams, however, capturing this data can often be more challenging than collecting other user events. In this article, we’ll show how mParticle’s Media SDK simplifies this process for engineering teams, and provides data stakeholders with actionable user insights.

Sean Ryan – February 01, 2022

Prevent data quality issues with these six habits of highly effective data

Maintaining data quality across an organization can feel like a daunting task, especially when your data comes from a myriad of devices and sources. While there is no one magic solution, adopting these six habits will put your organization on the path to consistently reaping the benefits of high quality data.

Sean Ryan – December 15, 2021

How to use a CDP with your data warehouse

Data warehouses and CDPs are two pillars of the modern data stack. Recently, a perception has emerged that companies need to choose one system or the other as a “source of truth” for their data. This article poses a counter perspective, and demonstrates how when used together, a CDP and a data warehouse can form a dynamic duo at the core of your data infrastructure.

Sean Ryan – December 10, 2021

How to implement an mParticle data plan in an eCommerce app

This sample application allows you to see mParticle data events and attributes displayed in an eCommerce UI as you perform them, and experiment with implementing an mParticle data plan yourself.

November 16, 2021

What does good data validation look like?

Data engineers should add data validation processes in various stages throughout ETL pipelines to ensure that data remains accurate and consistent throughout its lifecycle. This article outlines strategies and best practices for doing this effectively.

November 11, 2021
building-data-pipelines

Should you be buying or building your data pipelines?

With demand for data increasing across the business, data engineers are inundated with requests for new data pipelines. With few cycles to spare, engineers are often forced to decide between implementing third-party solutions and building custom pipelines in-house. This article discusses when it makes sense to buy, and when it makes sense to build.

Joey Colvin – November 10, 2021

Three threats to customer data quality (and how to avoid them)

In this video, Jodi Bernardini, a Senior Solutions Consultant at mParticle, lays out three major threats standing in the way of customer data quality, and offers advice on how organizations can address them.

Sean Ryan

Ask an mParticle Solutions Consultant: What is data quality?

In this video, Andy Wong, a senior leader on mParticle’s Solutions Consulting team, discusses what data quality means, why it is important prioritize, and the benefits of creating a centralized data planning team to oversee data quality.

Sean Ryan
data-lake-vs-data-warehouse

When to use a data lake vs data warehouse

Enabling teams with access to high-quality data is important for business success. The way in which this data is stored impacts on cost, scalability, data availability, and more. This article breaks down the difference between data lakes and data warehouses, and provides tips on how to decide which to use for data storage.

Joey Colvin – November 04, 2021