Systems | Development | Analytics | API | Testing

8 years ago, when I founded Iguazio together with my co-founders Yaron Haviv, Yaron Segev & Orit Nissan-Messing, I never thought I would be making this announcement on our company blog: McKinsey acquired Iguazio! When we first embarked on this journey, we realized that while AI has the ability to transform any industry - from banking to retail to manufacturing - in reality most data science projects fail.

Five things to know about how to get data from multiple sources: These days, organizations have more data at their fingertips than ever before and collect an incredible number of data sets from various sources. This creates a paradox for businesses such as e-commerce retailers struggling to deal with data complexity. With a deluge of information (and more arriving every day), how can you get data from multiple sources efficiently and unlock the hidden insights that it contains?

Do you look at spreadsheets with analysis paralysis? This article will help you simplify the entire process — forever.

Built with BigQuery: How Tamr delivers Master Data Management at scale and what this means for a data product strategy

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.

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.

Multivariate time series forecasting allows BigQuery users to use external covariate along with target metric for forecasting.

There are few topics in the world right now that are hotter than data and its related fields. As technology, machine learning, and computer algorithms continue to expand, so does the way that companies can use this information to benefit their business. Extract, Transform, and Load (ETL) remains one of the most important processes in the area of big data. This area is absolutely booming, and so is the demand to learn more about its various processes and components.

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.