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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.

Keboola’s Storage UI now comes with a new - slicker - look which will improve the user experience for all Keboola veterans. (New to Keboola? Do not fear. Simply follow along with the guided tour when you sign up for free, and you can unlock all the new features after June 2nd).

Modern enterprises leverage over 400 data sources to stay ahead of the competition. The sheer volume and complexity of data operations raise several challenges for intrepid organizations: How to cut the complexity of data operations? Enterprises turn to data lineage for the answer. Data lineage is the process of recording and visualizing data assets as they flow along your system.

In the age when data is the new oil, more than 80% of IT decision-makers delay their business decisions due to slow data processing. Architecting your ETL pipeline with database replication can speed up your data processes. Database replication creates an analytic database as a separate copy of your production database. This unburdens the transactional database from analytical queries while securing fresh data in the analytical database for faster time-to-insights.

TL;DR: Yes, you can do it. And no, you don’t need a separate tool for it. “Reverse ETL” is a fairly recent addition to the data engineer’s dictionary. While you can read articles upon articles about it (there’s a pretty good ‘primer’ in the Memory Leak blog), it can be summarized as being the art and science of taking data from your data warehouse and sending it somewhere other than BI - generally into other tools and systems where it becomes operational.

We’re happy to announce that we added R to our selection of transformation and workspace backends in our freemium accounts. Access a wide variety of statistical methods offered by R and model data straight from Keboola. No more context switching and working in a few different tools. Start modeling data and running transformations in R. Create a free account and take Keboola for a spin. R transformations complement Python and SQL where computations or other operations are too difficult.