More and more countries are imposing requirements on organisations to provide Standard Audit Files for Tax purposes (SAF-T), including the UK. HMRC requires businesses to keep their records digitally and provide their VAT returns through Making Tax Digital (MTD) functional compatible software as of April 2019. So this seems like an opportune moment to continue my series of blogs about Yellowfin partners by introducing the brilliant 4apps – a business helping tackle the VAT software problem.

We are happy to announce that our latest version of the Tideways extension (v5.0.22) includes support for the Spryker E-Commerce Platform through fully automated instrumentation and hooks into the core of Spryker. With the complexity of a large E-Commerce project, different channels, multiple language stores and landingpages for campaigns it is especially difficult to get a full view of the performance and errors from within your software as a project manager, developer or system administrator.

There’s no one-size-fits-all system for choosing the right KPIs. Instead, here’s how you can choose the most effective KPIs tailored specifically for your business.

With all the customers running Tideways on their Magento, Oxid or Shopware shops I was interested in how in the aggregated average, those shops usually perform on Black Friday compared to the 8 weeks before and the weeks after leading up to Christmas. A lot of e-commerce shops have either large Black Friday, Cyber Monday or week long christmas campaigns, which can increase the traffic to the shops significantly.

The best piece I read this month was about how to build a recommendation engine in R by Data-Mania. It’s a little bit tangential to the BI space but it was interesting to me personally because I’m not a data scientist.

A few weeks into year five of the Tideways project we are happy to announce the release of Tideways version 5, including large updates to all parts of the stack from PHP extension, daemon and the user-interface. Ladies and gentlemen, this is Tideways no. 5!

Ethereum and other cryptocurrencies have captured the imagination of technologists, financiers, and economists. Digital currencies are only one application of the underlying blockchain technology. Earlier this year, we made the Bitcoin dataset publicly available for analysis in Google BigQuery. Today we’re making the Ethereum dataset available.

This quote from Niccolò Machiavelli – an Italian politician, historian, and philosopher from the Renaissance period, has profound relevance to emerging technologies.

Having detailed response time percentiles and impact analysis for transactions is great. But often it's not the php application that is causing unwanted spikes in response times rather than services used by your application, such as MySQL, redis, HTTP APIs and so on.

In this article, I’ll walk you through the process of building a machine learning model using BigQuery ML. As a bonus, we’ll have the chance to use BigQuery’s support for spatial functions. We’ll use the New York City taxicab dataset, with the goal of predicting taxi fare, given both pick-up and drop-off locations for each ride — imagine that we are designing a trip planner.