Systems | Development | Analytics | API | Testing

The trouble with marketing initiatives is that it is almost impossible to tell how they impacted the business’s bottom line. As the marketing pioneer John Wanamaker said: A person scrolling through Twitter on their mobile app might have seen your ad, loved your brand, and then logged into their desktop to purchase your product. The gap between needs generated by marketing spans across marketing channels and time.

Data transformations are the backbone of smooth-running data operations. Transformations are used in data replication between databases, data migration from cloud to on-premise, and data cleaning (aggregations, outlier removal, deduplication …) aka all the good stuff that goes into extracting insights from data. But as any data professional can attest, transformation can also be a painful bottleneck. Think scripts that run for an entire day and finish just before the next scheduled job.

As your data operations evolve, they become messier. Diverse data sources and data models at their sources, multiple movements of data throughout your platform, and cobbled-up infrastructure, which has grown in complexity through every deployment have made it hard to identify, trace, classify, and understand your data assets. This can be as simple as an analyst spending hours trying to figure out where a data attribute in a table came from and whether it is trustworthy.

Working with raw or unprocessed data often leads to poor decision-making. This explains why data scientists, engineers, and other analytic professionals spend over 80% of their time finding, cleaning, and organizing data. Accordingly, the ETL process - the foundation of all data pipelines - devotes an entire section to T, transformations: the act of cleaning, molding, and reshaping data into a valuable format.

Founded in 1991, CNC (Czech News Center) is one of the largest media companies in the Czech Republic. They offer dozens of print and online publications to the Czech market, including Blesk, Aha!, and E15. A commitment to journalistic integrity has enabled their growth, now reaching millions of readers. They are currently undergoing a vast digitalization process with the aim to become the fastest-growing and largest media house in the Czech Republic.

Running an eCommerce store is very much like flying a plane - you can reach unprecedented heights, but you won't be able to do it blindfolded. You have to see where you are going to touch the skies. E-commerce analytics gives you the guidance to make the right choice and scale your online store to new heights. In this article, we will take a deep dive into Shopify Analytics Shopify offers analytics as an out-of-the-box default service to all Shopify store owners and admins.

To grow your eCommerce business ahead of your competitors you need to rely on analytics. Ecommerce analytics is the compass that replaces your gut feelings as you scale your e-shop to higher grounds and more online sales. In this ultimate guide to eCommerce analytics we will look at: What is eCommerce analytics? Why is eCommerce analytics crucial for the success of your store? What are the best metrics and KPIs to track for eCommerce?

You have started your data journey. You know you need to somehow collect data from various sources and land them into a data warehouse or data lake of some sort. Right now you’re browsing tools and calculating costs - there’s one for extraction, another one for transformations, there’s an ETL tool. What if we told you there’s a better way?

Enterprises are tapping and leveraging big data to get ahead of the competition. As Peter Sondergaard, ex-Executive Vice President at Gartner said: The problem with the combustion engine is that it does not scale well. As companies grow, the data platforms they previously relied on for analytics start to break apart.

The same way architects design blueprints to plan and support the building of houses, data architects design the data architecture to support building end-to-end data pipelines. In this article, we dive into data architecture, its principles, and how data mesh fits into the story.