I just got an email from a venture capitalist. For about the hundredth time, the venture capitalist told me they were anxious to invest money in us. The only qualification was that we needed to already have at least $10 million in sales. If we had $10 million in sales, we wouldn’t need to be talking with the venture capitalist. How stupid is that? I suggested to the venture capitalist that they go invest in IBM or ATT because they do have $10 million in sales.

Do you know where your data is? Most organizations store data in various destinations (in-house databases, SaaS locations, cloud-based apps, etc.), which makes running analytics far more complicated. Imagine pulling data from all these destinations into one data warehouse or data lake. Life would be so much easier... "But doesn't this require a lot of code?" you may ask. Not necessarily.

Google Cloud customers who want app-level encryption in hybrid cloud data warehouses can encrypt and decrypt that data outside BigQuery. Here’s how to do that securely.

The Chief Data Officer is arguably one of the most important roles at a company, particularly those that aspire to be data-driven. CDO appointments and the elevation of data leaders have accelerated in recent years, and the role has morphed as perceptions of data have evolved. Responsibilities span strategy and execution, people and processes, and the technology needed to deliver on the promise of data.

Recently, I published a blog on whether self-service BI is attainable, and spoiler alert: it certainly is. Of course, anything of value usually does require a bit of planning, collaboration, and effort. After the article was published, I began having conversations with technical leaders, analysts, and analytics engineers, and the topic of data modeling for self-service analytics came up repeatedly.

Extract, transform, load (ETL) is a critical component of data warehousing, as it enables efficient data transfer between systems. In the current scenario, Python is considered the most popular language for ETL. There are numerous Python-based ETL tools available in the market, which can be used to define data warehouse workflows. However, choosing the right ETL tool or your needs can be a daunting task.

Built with BigQuery: How to Accelerate Data-Centric AI development with Google Cloud and Snorkel AI.

The point of evidence is to guide decisions, so transforming a business into being evidence-based has to start with leaders.

In today's increasingly digital world, businesses of all sizes rely on data to make informed decisions and drive growth. This is why more and more organizations have started using data warehouse platforms. These crucial tools help businesses store, manage, and analyze data in one central location. In addition, a data warehouse platform makes accessing and processing large amounts of data easier, enabling businesses to gain valuable insights and improve their operations.

Deploying models is becoming easier every day, especially thanks to excellent tutorials like Transformers-Deploy. It talks about how to convert and optimize a Hugging face model and deploy it on the Nvidia Triton inference server. Nvidia Triton is an exceptionally fast and solid tool and should be very high on the list when searching for ways to deploy a model. If you haven’t read the blogpost yet, do it now first, I will be referencing it quite a bit in this blogpost.