Tag

Data engineering

snowflake-vs-bigquery

Engineering

Snowflake vs. BigQuery: What are the key differences?

Learn more about the differences between two popular data warehouse solutions, Snowflake and Google BigQuery, and understand how to identify which is right for your team.

November 29, 2022

Engineering

How we improved performance and scalability by migrating to Apache Pulsar

We recently made a significant investment in the scalability and performance of our platform by adopting Apache Pulsar as the streaming engine that powers our core features. Thanks to the time and effort we spent on this project, our mission-critical services now rest on a more flexible, scalable, and reliable data pipeline.

November 17, 2022
mparticle-warehouse-sync

Press Releases

mParticle announces Warehouse Sync, the convenience of reverse-ETL with the power of a real-time CDP

Combining data warehouse ingestion with robust data quality protections and rich predictions provides data teams with unmatched activation capabilities.

November 01, 2022

Engineering

New ways to understand in-app behavior with Apple iOS 16

With the latest updates to iOS and Xcode, Apple has introduced changes to its operating system and developer environment that give engineers and product teams creative new ways to uncover user behavior.

Sean Ryan – October 14, 2022
how-does-azure-work

Engineering

How does Azure work? An explanation of Microsoft’s cloud platform

Learn more about cloud platform Microsoft Azure and how it fits into your data infrastructure.

September 27, 2022
snowflake

Engineering

How does Snowflake work? A simple explanation of the popular data warehouse

Learn more about what Snowflake is and how it fits into your data stack.

September 27, 2022

Engineering

The engineer’s guide to working with marketers

While developers don’t readily admit it, working with marketers can sometimes be a pain. But when engineers and marketers collaborate effectively on data, amazing things can happen. We’ve assembled this guide to provide engineers with a roadmap for effectively working with their colleagues in marketing and making friends out of frenemies.

Sean Ryan – May 20, 2022
digital-transformation

Engineering

Harveer Singh leads Western Union’s digital transformation with data

Chief Data Architect Harveer Singh creates the data and tech roadmap that will help the iconic company emerge as a leader in fintech.

May 16, 2022

Engineering

Developer Deep Dive: mParticle Sample Apps

Recently, a cross-functional squad of engineers, PMs and designers at mParticle assembled to produce a labor of love––sample applications. These sample apps help developers implement our SDK in Web, iOS, and Android environments and understand the value of mParticle. Here’s the nuts-and-bolts story behind what they built, the technical choices they made while building these apps, and what they learned along the way.

Sean Ryan – May 05, 2022

Engineering

Implement a CDP with ease using mParticle's sample applications

Developers rarely look forward to integrating third-party systems into their projects. The learning curve to understand vendor platforms is time-consuming and diverts attention away from more interesting product initiatives. Our sample applications address this problem by helping developers understand how mParticle works on various platforms and providing production-quality, copy/paste-ready code to implement our CDP with ease.

Sean Ryan – April 13, 2022

Engineering

How we cut AWS costs by 80% while usage increased 20%

How do you replace a tire while driving on the highway? This is what it felt like to re-architect the engine behind one of our most heavily used and relied upon products, the mParticle Audience Manager. Here's how we optimized this critical piece of our architecture and positioned it to play a key role in the next phase of our growth, all while customer adoption and usage steadily increased.

Yuan Ren – March 25, 2022

Engineering

Data quality vital signs: Five methods for evaluating the health of your data

It’s simple: Bad data quality leads to bad business outcomes. What’s not so simple is knowing whether the data at your disposal is truly accurate and reliable. This article highlights metrics and processes you can use to quickly evaluate the health of your data, no matter where your company falls on the data maturity curve.

Sean Ryan – March 21, 2022

Engineering

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

Engineering

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

Engineering

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

Engineering

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
building-data-pipelines

Engineering

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

Engineering

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

Engineering

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

Engineering

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
reverb-data-workflows

Engineering

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

Engineering

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
customer-data-platform-implementation

Growth

How to prepare for your Customer Data Platform implementation

Implementing a Customer Data Platform at the infrastructure layer of your tech stack can transform the way in which you work with customer data—if you do it right. This article breaks down the steps you can take to prepare for your Customer Data Platform implementation and make the most of your investment.

Andy Wong – September 16, 2021

Engineering

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

Engineering

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

Engineering

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

Engineering

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

Data engineering

How EPIX leverages mParticle to simplify data collection and unlock use cases

In this installment of the Digital Anarchist web series, Sam Dozor, VP of Engineering at mParticle, and Sacha Stanton, SVP and Chief Technology Officer at EPIX, discuss the many wins the streaming innovator has realized as a result of placing mParticle at the heart of their data infrastructure.

August 17, 2021

mParticle Product

Part 1: Integrate Server-side Events from your Shopify eCommerce Store with mParticle

mParticle's Shopify integration allows you to easily unify, transform and activate customer data collection across all of your eCommerce channels. Here, we'll look at how to capture server-side events with the pre-built connector.

Jefferson Haw – July 28, 2021

Engineering

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
customer-data-platform-tracking-plan

Growth

6 tips for building a Customer Data Platform tracking plan

The value of your Customer Data Platform depends on the quality of data that you get into it. Here are six implementation tips you can follow to set yourself up for success.

Glenn Vanderlinden – June 29, 2021
probabilistic-vs-deterministic

Growth

Probabilistic vs deterministic: Which method should you be using for identity resolution?

The way in which you build your customer profiles can have big consequences on marketing strategy, data privacy, and customer relationships. Learn about the difference between probabilistic and deterministic identity models and how to determine which method you should be using.

Joey Colvin – June 16, 2021

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.

May 11, 2021

Growth

How to set up your customer data 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.

Joey Colvin – April 19, 2021
What is data engineering?

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.

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.

Sean Ryan – March 31, 2021
universal-customer-id

Engineering

The value of a universal customer ID across your tech stack

Teams across industries are striving to create a consolidated view of their customers. But if that view isn't cleanly integrated with the tools and systems throughout the tech stack, 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.

Joey Colvin – March 17, 2021
Relational vs. Non-Relational Databases

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?

Sean Ryan – March 16, 2021

Engineering

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.

Sean Ryan – February 17, 2021

Engineering

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.

Sean Ryan – February 08, 2021

Growth

Avoiding the growth trap

What do cattle farmers from the 1600s have in common with teams across modern companies? Both rely on shared resources that can quickly be depleted by an overzealous desire for growth, leading to the tragedy of the commons. Learn how you can avoid the growth trap by leveraging your customer data infrastructure and saving your engineering resources from depletion. Stop the vicious cycle, not the development cycle.

Michael Katz – January 14, 2021

Engineering

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.

Sean Ryan – January 07, 2021

Engineering

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.

Sean Ryan – November 30, 2020

Engineering

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.

Sean Ryan – November 10, 2020
cdp data warehouse

Engineering

CDP vs Data Warehouse: What's the difference?

Data warehouses enable critical insights, and speed of data collection and stability of warehousing are important to their performance. Learn the differences between a CDP vs data warehouse and how you can use both to improve functionality and take action on business intelligence.

Joey Colvin – August 18, 2022
building a data platform

Growth

Should you build or buy a Customer Data Platform?

Customer Data Platforms are a critical piece of the modern data infrastructure. Learn what it takes to build a Customer Data Platform and how to determine whether building a solution or working with a leading vendor is the right path for your organization.

Joey Colvin – October 15, 2020
block-data-quality

mParticle Product

Introducing Block Data: Diagnose, quarantine, fix, and backfill bad data

Bad data leads to bad decisions, but most teams are unable to address their data quality problems proactively in real time. Today, we are excited to announce Block Data, a new early access feature that helps teams automatically identify and drop unplanned data before it’s forwarded downstream, review and quarantine suspected bad data for investigation, and replay quarantined data once it’s been inspected and modified.

Niko Stahl – October 14, 2020
ios-14-privacy-updates

Engineering

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.

Sam Dozor – September 16, 2020
customer data for ai

Growth

Meet your marketing goals with customer data

Your marketing campaigns will only be as effective as the customer data that you use to power them, especially if leveraging AI/ML. Check out these tips and tricks to get your customer data in the best shape possible and begin accelerating your marketing strategy.

Megan Warhurst – August 12, 2020
testing in production

Engineering

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.

Joey Colvin – July 02, 2020
event collection

Engineering

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.

Shabih Syed – May 13, 2020
amazon advertising targeting

mParticle Product

Target with precision with Amazon Advertising

With the new Amazon Advertising integration, marketers can target advertising on Amazon properties and across the web, with audiences based on reliable first-party customer data.

Kale Bogdanovs – April 23, 2020

Engineering

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

Abril McCloud

mParticle Product

Data is a team sport: Q4 feature announcements

Bad news for the rogue product manager, the lone wolf data scientist, the intransigent head of infosec, and the obstinate engineering manager: Data is a team sport. Learn about the forthcoming releases designed to help your whole team do more with more of your data.

Craig Kelly – October 09, 2019
event driven architectures

mParticle Product

Real-time event processing with Kafka

Learn how mParticle's Kafka integration can help you stream customer data to systems and applications with event data forwarding, advanced filtering and compliance, distributed event notification, and event sourcing.

Kale Bogdanovs – September 30, 2019
user-aliasing-API

mParticle Product

User Aliasing API: Clarify customer journeys

mParticle’s User Aliasing API allows brands to programmatically merge anonymous user events with their logged-in events, providing brands with a complete, accurate view of their customers’ profile and event journeys as they move from being anonymous to authenticated across every touchpoint.

Shabih Syed – July 17, 2019
AI machine learning

Improve products with AI and Machine Learning

Traditional digital marketing can’t keep up with machine learning and AI marketing solutions. Learn exactly how these powerful point-and-click tools work and how they can help your team experiment at scale.

Abril McCloud – June 06, 2019
Data warehousing snowflake

mParticle Product

Load data to Snowflake data warehouse

Learn how mParticle's Snowflake integration can help you warehouse your customer data more efficiently and securely at scale with automated data exports, advanced filtering and compliance, and easier querying.

Craig Kelly – April 29, 2019

mParticle Product

Introducing the Profile API for hyper-personalization at scale

Personalize on-site and in-app experiences wherever your customers are, in real time with the new Profile API feature. Now in limited beta.

Craig Kelly – April 22, 2019
modern data infrastructure

Engineering

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.

Abril McCloud – November 02, 2018
Customer Data Platform Use Cases: Media

Customer Data Platform Use Cases Guide: Media

Customers expect media brands to create seamless, contextual, and concurrent experiences across every device, which cannot be achieved with legacy systems unable to collect and activate data from every channel. Use these common use cases to help you determine which CDP features are relevant and find the CDP that will help them meet current and future business needs.

Abril McCloud
integrated data layer

Growth

How to plan your integrated data layer

Learn how to develop an integrated data layer that aligns with your business and techical goals and provides you with a centralized source of clean customer data.

Andy Wong – August 28, 2018
How to design high throughput and low latency NoSQL deployments

Engineering

ScyllaDB Migration: How to design high throughput and low latency NoSQL deployments

Yuan Ren, Head of Data Science at mParticle, discusses our ScyllaDB migration and how to process 50 billion monthly messages via NoSQL deployments.

Yuan Ren – August 24, 2017

mParticle Product

Introducing Rules

mParticle customers can transform their data without source code updates to ensure that each service receives data in the correct format.

Rashel Shehata – July 05, 2017

mParticle Product

Announcing our integration with Google Cloud Storage

 The new Google Cloud Storage integration allows mParticle customers to seamlessly onboard customer data to Google’s Cloud Platform without requiring code updates and ongoing maintenance.

Tricia Prashad – June 27, 2017
15 questions to evaluate CDP data preparation capabilities

Growth

15 questions to evaluate CDP data preparation capabilities

Marketing technology analyst David Raab explains how customer data platforms can enable data transformations and how to ensure the right fit for your needs.

David Raab – September 26, 2017

Engineering

Hyperloglog Algorithm: A must-know for data scientists

Hyperloglog (HLL), a powerful streaming algorithm, helps mParticle deliver real-time analytics products. Learn why it's a must-know for data scientists.

Yuan Ren – October 23, 2014

Engineering

Make the right mobile architecture decisions

David Spitz – August 30, 2016

mParticle Product

Announcing our Feeds integration with Salesforce Marketing Cloud!

We are excited to announce our latest Feeds integration with Salesforce Marketing Cloud (SFMC), making it easier for brands to…

Tricia Prashad – May 16, 2017

Engineering

Behind the script: Building a Roku SDK

mParticle's Sam Dozor walks us through building our open source Roku SDK and the many eccentricities of the platform that made the experience so unique.

Sam Dozor – February 09, 2017
mParticle and Venmo Case Study Hero Background

Growth

How Venmo increased user engagement by 30%

Tricia Prashad

Engineering

The problem with app updates: Communication breakdown

While apps continue to make improvements from the time they launch, app stores don’t offer them a means to share progress within app updates.

Coby Berman – August 12, 2015

mParticle Product

Why webhooks aren’t enough for rapid deployment

How we resolved to move beyond webhooks making a Firehose SDK mindful of the types of requests and required responses involved in partner integrations.

Sam Dozor – April 21, 2016