In a few weeks, it will be exactly ten years since AWS Redshift was made public for the first time through a limited preview. Redshift is considered the O.G. cloud data warehouse, followed by BigQuery and Snowflake. According to many, this piece of technology paved the way for “the Modern…

We are excited to announce that Validio has joined the Google Cloud Partner Advantage program as a Build Partner! — Validio’s next generation data quality validation and monitoring platform will enable joint customers of Validio and Google Cloud to achieve more trust in their data and data pipelines, proactively monitor data health, and identify and remediate data quality issues as they arise. Validio supports both batch data and streaming data…

In 2021, we saw quite an acceleration of the buzz around the rise of the Modern Data Stack. We now have a tsunami of newsletters, influencers, investors, dedicated websites, conferences, and events evangelizing it. The concept around the Modern Data Stack (albeit still in its early innings) is tightly connected with the explosion of data tools in the cloud. The cloud comes with a new model of infrastructure that will help us build these data stacks fast, programmatically, and on-demand, using cloud-native technologies like Kubernetes, infrastructure as code like Terraform and cloud best practices of DevOps. So, infrastructure becomes a critical factor in building and implementing a Modern Data Stack.

I still remember how not that many years ago it was common for people to refer to a data scientist as a “unicorn”. The expectation level was set at a super full-stack engineer/mathematician who could understand all business problems. However, during the past 2 years, as we’ve passed the peak AI/ML hype, we’ve witnessed the rapid rise of the data engineer. Dice’s 2020 tech jobs report cites data engineering as the fastest-growing job in tech in 2020, increasing by a staggering 50%.

We’re happy to announce that we’ve strengthened our legal and PR & Communications side with Dara Gill (former VP General Counsel at Truecaller) and Kim Fai Kok (former founding team member and Director of Communications at Truecaller) — both highly appreciated startup veterans and early-stage investors in the Nordic tech scene. — Dara will step in as our external General Counsel and support us with all things legal, and Kim Fai will come in as Communications Advisor to help lay the foundations for our PR & Communications strategy as we’re accelerating towards our next phase of growth. …

Product-led growth (PLG) has emerged as the buzzword of 2021 in the startup and venture capital scene. As go-to-market strategies continue to transform, we are seeing a movement from sales-led to PLG motions. According to OpenView, PLG is “an end user-focused growth model that relies on the product itself as the primary driver of customer acquisition, conversion and expansion.” — LogMeIn went public in 2012, in what many perceive as the first B2B SaaS IPO with a true product-led growth model. This triggered a mindset change in B2B SaaS — changing the perception of the old-school “Oracle” top-down enterprise sales playbook (a dark time in tech history where almost all…

If you work in the world of data, you have at this point heard a lot of talk about the Modern Data Stack. It has gained a lot of buzz and attention as companies have begun a fundamental shift in how they think about analytics and machine learning. The Modern Data Stack is built on the new cloud-native technologies that have emerged in the last decade that are fast, reliable, scalable, and, most importantly, accessible everywhere. Some of the technologies that have made this possible are massively parallel processing (MPP) cloud data warehouses like Redshift, Snowflake, and BigQuery; ingestion tools like Stitch, Airbyte and Fivetran that have improved reliability and connector coverage; and analytics platforms like ThoughtSpot that enhance users’ experience when finding and sharing data insights, and make that data accessible to everyone, everywhere.

Having worked with tech startups who have a strong academic/ research background and at startups that even had specific research teams at a very early stage, I started reflecting on the difference between research culture and startup culture. There’s no idea to beat around the bush: they’re very different. Based on my experience, there are more differences between the two than common denominators for a harmonious coexistence.

In our ML & Data trends post from February we discussed whether one believes MLOps has crossed the chasm or not, the rise of MLOps (i.e. DevOps for ML) signals an industry shift from PoC’s (how to build models) to operations (how to run models). Even though this shift is something that we’re extremely excited about, there’s a recurrent bottleneck that keeps haunting us year after year: data quality.