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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.

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?

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.

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.

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.

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.

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?

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!

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!

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.