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Mobile attribution in Android is constantly changing. Like Apple, Google has introduced restrictions on how advertisers can obtain user data, giving users the option to either opt in or opt out of being tracked across mobile apps and websites. Both Google and Apple now also require app developers and publishers to supply a report on exactly what data is being collected and how it is being utilized, which is then displayed to the end user when they install an app or when the app requests their permission to collect certain kinds of data.

Working with data in a big company can be very tiresome, and that is to put it lightly. Ever-growing volumes of data, flowing in from frequently added streams, with even more logic to transform, shape, and load all of that "fluid" into some required form for later use, are becoming more and more challenging tasks with each passing day.

This article will cover the basics of mobile attribution and explain how it works under the hood before sharing a few popular mobile attribution platforms that you can use.

If your organization has at least some online presence, you might be wondering how to leverage your data. Data points are generated whenever someone signs up for your service, sends you an inquiry, or performs a transaction on your website. While such data is not very informative on its own, it can produce astounding insights when aggregated.

Apple’s new privacy framework App Tracking Transparency finally went live in April 2021. The new framework changes how mobile attribution and tracking will be done on iOS versions 14.5+, with other platforms expected to follow suit in the future. The update significantly impacts how data is collected on iOS, as well as the types of data that trackers can collect and their accuracy of the data, thus restricting the amount of information available to mobile attribution platforms.

Meltano is a full-package data integration platform that’s poised to challenge even the most established players in the data space. By building on top of the industry’s best open-source tools and infusing them with DataOps best practices, Meltano sure packs a powerful punch.

Data is one of the main drivers behind the new industrial revolution. It’s a unique resource that, when utilized correctly, allows businesses to operate more efficiently and effectively. Centralized data storage solutions like data warehouses and data marts play a crucial role in allowing companies to make the most out of their data.

Building pipelines is not easy. Moving the data from point A to point B as reliably and as fast as possible are the main things you need to focus on. Read this article to learn more!

The DataOps methodology is increasingly embraced by companies looking to improve product quality, customer satisfaction, staff productivity, and cost-efficiency. Created to help you maximize your DataOps implementation, Singer is the open-source ELT standard that’s revolutionizing data processing. But what is Singer, and how is it changing the DataOps landscape? This article will go over key concepts and practical examples to illustrate why you should use Singer to implement DataOps in your organization.

Were you wondering what is a data warehouse, and why do people talk about it? Building something like that might sound complex but the reasons to do so might be too good to ignore. Learn more in this article!

How long does it take to create an absolutely new report based on new data with some data transformations? Is it possible to reuse the same views and tables for a new report? If yes - how long will take to update some view or table with just one additional column?

Data fuels the modern organization, hence the business world’s acceptance that “data is the new oil.” But much like crude oil, raw data must undergo transformation. A business can only reap the true value of data when it is organized and interrelated in a meaningful way. This is the task of data modeling.

Cloud data warehouses can get expensive, commonly due to a lack of efficient compute options. Companies find that their existing setup cannot handle bursty workloads, so they scale out by commissioning more compute nodes. As costs rise, companies often limit analytics to stay within budget, thus undermining the main goal of making data insights more accessible.

Whether you're an early stage startup or a mature industry leader, you're probably thinking about how you can use data to grow your business. Even if you already have data, you’re only halfway there. After all, your data may not mean much unless you have the right data infrastructure and analysis tools in place.

Net Promoter Score℠, (also known as NPS®) is the prevalent customer sentiment metric for modern products and services, thanks largely to its simplicity and impactfulness. An indicator of customer or user loyalty, you’ll find at least some variant of the NPS at the majority of Fortune 1000 companies. But not all companies understand how to take advantage of the NPS while being aware of its limitations.

Centralized data stores like data lakes and data warehouses are key solutions to a data-based decision-making process. But which data store should you invest in, and which is more likely to generate results for your organization?

Fivetran is a managed Extract-Load-Transform (ELT) service that allows cloud-based companies to efficiently get the most out of their data. With more data being generated and stored in the cloud, managed solutions like Fivetran emerge as an attractive and cost-efficient solution compared to hosting data pipelines in-house.