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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.

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.

What is a data plan, and why is it important to have one?

"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.

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.

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

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.

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.

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.

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.

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?

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.

How to stop endless data shipping cycles

Engineers should ship products, not data. Product managers and marketers should experiment with data, increase personalization, and improve experiences. With a permanent data infrastructure, these goals are not mutually exclusive.

Capture page navigation events in a React Application

In a single-page application, understanding which pages your customers visit and the journeys they take through your website can be challenging. Here, we’ll look at a scalable and maintainable strategy for tracking page navigation events in a React application.

Track User Events in Single-Page Applications

Owing to their fast load times and smooth user experiences, Single-Page Applications (SPAs) are now an extremely popular design pattern for developing websites. While building your site as an SPA offers clear advantages for your customers, it places challenges in the way of collecting robust analytics on user behavior.

APIs vs. Webhooks: What’s the difference?

An API (Application Programming Interface) enables two-way communication between software applications driven by requests. A webhook is a lightweight API that powers one-way data sharing triggered by events. Together, they enable applications to share data and functionality, and turn the web into something greater than the sum of its parts.

What is data orchestration

Data orchestration is an automated process in which a software solution combines, cleanses, and organizes data from multiple sources, then directs it to downstream services where various internal teams can put it to use. The purpose of data orchestration is to help a company make its data as useful and versatile as possible.

Smartype Generate: Translate any JSON schema into data collection libraries for web, iOS and Android

mParticle’s Smartype is a platform-agnostic tool that can help every engineering team ensure data quality and consistency. Learn how to use Smartype to translate any JSON schema into custom data collection libraries for iOS, Android, and Web platforms.

CDPs vs. Data Lakes: What’s the difference, and can you use both?

CDPs and Data Lakes differ in the insights they surface, the users they serve, and the overall value they deliver. Though when used together, they are a powerful duo that can help your organization leverage historical and real-time customer data to the fullest extent.

CDP vs Data Warehouse: What's the difference?

Learn the differences between a CDP vs data warehouse, and how you can use both in tandem to configure an architecture that makes sense for your data and business needs.

Future-proof your customer data strategy: Get ready for iOS 14 privacy updates

There are significant changes coming to iOS relating to user privacy, tracking transparency, and specifically the use of the iOS advertising identifier (IDFA). Since the announcement, mParticle has been collaborating with some of the largest consumer brands in the world to holistically achieve a balance between adhering to compliance obligations and ethical data collection policies to protect consumer choice, while also delivering personalized and relevant information to people globally.

Improve mobile app performance with SDK abstraction

Implementing third-party SDKs in your mobile app allows Marketers and Product Managers to get data into the tools they love, but unstable third-party code can impact mobile performance and drain engineering resources. Learn how you can get high quality customer data to your team's favorite tools without having to manage excess third-party code.

Generate in-warehouse predictive audiences

Learn how Jayant Subramanian, data science intern, developed a proof-of-concept machine learning pipeline for predicting user behaviors from data pre-processing to model training and beyond using Snowflake and Apache Airflow.

How a CDP supports customer data security

The trust between a customer and brand is the foundation of a strong customer relationship. Part of maintaining that trust is sound customer data management and security. Learn how a Customer Data Platform helps you secure your customer data pipeline so that you can build trust throughout the customer journey.

Test in production with mParticle and Split

Testing with production data allows you to release features with more efficiency and greater confidence, but doing it successfully requires good testing control and data management processes. Learn more about using mParticle and Split feature flags to simplify testing in production.

Smartype: Proper event collection at run time

Smartype, a data quality product that translates any data model into type-safe code to help developers ensure proper event collection at run time. Smartype generates personalized SDKs, based on any data model, providing automated code completion and improving data collection and quality at scale. Now available in beta.

The difference between CDPs, DMPs, and CRMs

Discover the distinctions between these three very different martech solutions and which uses cases is best suited to your chosen technology provider.

How JetBlue improved their mobile customer experience

Learn how JetBlue uses mParticle to understand how customers experience the app on an individual basis, identify points of friction that affect customers' satisfaction, and test and deploy tools efficiently without adding third-party code that could impact end-user functionality

Cognetik and mParticle: Simplify mobile tags

Learn how using Cognetik and mParticle can help you simplify mobile tagging and reduce vendor overhead.

10 Critical data infrastructure capabilities

Instead of focusing on core data management challenges, many Customer Data Platforms are focused on the application of data. Learn about the 10 critical components of modern data infrastructure.

How Bleacher Report automates their data pipeline

Learn how the team at Bleacher Report uses mParticle to automate their data pipeline, leading to better insight, reduced storage costs, and less engineering time spent on non-core development.

Get ready for iOS 12

In just a few days, Apple will be releasing iOS 12. Is your app ready to support this new software? With a mobile data layer like mParticle, you could be ready to support iOS 12 in as little as one day.

How to assemble a best in class tech stack

How to implement a best in class stack...the right way