Blog

BigQuery vs. Redshift: Which cloud data warehouse is right for you?

The data warehouse is the source of truth from your business's data set. Choosing the right solution is critical. This article explains how BigQuery and Redshift compare in factors such as performance, security, and cost so that you can select the right warehouse for your needs.

Kinesis vs. Kafka: Comparing performance, features, and cost

In this article, we compare two leading streaming solutions, Kinesis and Kafka. We focus on how they match up in performance, deployment time, fault tolerance, monitoring, and cost, so that you can identify the right solution for your streaming needs.

What the heck is reverse ETL?

Reverse ETL is a process in which data is delivered from a data warehouse to the business applications where non-technical teams can put it to use. By piping data from a data warehouse to downstream business systems, reverse ETL tools fill the gap between data storage and activation.

Snowflake vs. Redshift: Which Data Warehouse Is Better for You?

Learn how popular data warehouse providers Snowflake and Redshift compare in maintenance requirements, pricing, structure, and security so that you can understand which solution is right for your team.

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.

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.

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.

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.

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.

Enhancements to mParticle’s developer tools make it easier to collect data and ensure quality at the source

mParticle makes it easy for engineers to accurately collect customer data by translating data schemas into production-ready code.

How we reduced Standard Audience calculation time by 80%

mParticle’s Audiences feature allows customers to define user segments and forward them directly to downstream tools for activation. Thanks to our engineering team’s recent project to optimize one of one of our audience products, mParticle customers will be able to engage high-value customers with even greater efficiency.

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.