Winter is clearly over here, the sun is shining, which means it is time to sum up everything that is new in the Tideways Winter 2021 release. Aside from this blog post explaining the new features in detail and the documentation, we also invite you to our 30 minute webinar "New Features and Improvements launched in Winter 2021" scheduled for 4th March 2021, 15:00 Europe/Berlin. The webinar will also include an exclusive intro to two brand new features that are launching into Beta next week.

Today, we are excited to announce the general availability (GA) of BigQuery materialized views. Materialized views (MV’s) are precomputed views that periodically cache results of a query to provide customers increased performance and efficiency.

Business analysts working with larger and larger data sets are finding traditional BI methods can't keep up with their need for speed. BigQuery BI Engine is designed to meet this need by accelerating the most popular dashboards and reports that connect to BigQuery. With the freshest data available, your analysts can identify trends faster, reduce risk, match the pace of customer demand, even improve operational efficiency in an ever-changing business climate.

In this quick guide for cybersecurity professionals, we’ve invited some of our favourite security experts who have previously worked with Metasploit to explain why this tool is so valuable for conducting effective penetration tests and network reconnaissance tasks. Our first expert Michael Roninson, Security Expert at Cerber Tech gives a brief overview of this tool and how to use it in his response below;

Many people think because we’re an online business, with technology at the heart of everything we do, we’d be better placed than many to make the transition from in-person meetings to virtual meetings during the pandemic. Whilst we have made that leap and made it work well, it’s not because we’re au fait with tech and the latest tools, it’s quite the opposite.

These days, there are two kinds of businesses: data-driven organizations; and companies that are about to go bust. And often, the only difference is the data stack. Data quality is an existential issue—to survive, you need a fast, reliable flow of information. The data stack is the entire collection of technologies that make this possible. Let's take a look at how any company can assemble a data stack that's ready for the future.

There is nothing permanent except change. In a world of turbulent, unpredictable change, we humans are always learning to cope with the unexpected. Hopefully, your machine learning business applications do this every moment, by adapting to fresh data. In a previous post, we discussed the impact of COVID-19 on the data science industry.

Today’s enterprise data science teams have one of the most challenging, yet most important roles to play in your business’s ML strategy. In our current landscape, businesses that have adopted a successful ML strategy are outperforming their competitors by over 9%. The implications of ML on the future of business are clear. However, only 4% of enterprise executives today report seeing success from their ML investment.

Start-ups founded on analytics have been shaking up every industry. Finance has been disrupted by Monzo's data focus, Netflix’s analytics has upended film entertainment, and Swyfft has used data to change the game for US home insurance. Today's users have come to expect analytics in their applications.

According to the European Banking Authority report on Advanced Analytics and Big Data in banking, the implementation of data technologies, infrastructure, and practices is still at “an early stage”. The game is on for early contenders in this winner-takes-most market. Banks that move quickly are likely to get ahead of the curve, grabbing more of the market pie before others rise to the challenge.