Thought Leadership

In the past year, words like recession, business slowdown and monetary cuttings are being heard more and more often. Not just in the economic press and in the media, these discussions are very much heard also in almost all companies – within boardrooms, in management meetings and when engaging with potential investors and customers. As

As the Covid-19 pandemic loosens its grip on the world, we’re all eager to start travelling and meeting in person again. The great news is that in persone conferences are back, so it’s time to make up for lost time! Attending conferences is a great way to learn, network, and engage with like-minded people.  Not

A few weeks ago, I was looking at a dashboard in our internal BI system. It’s a simple system. Redash over PostgreSQL that has just a few hundreds of thousands of rows.  I noticed a change in one of my favourite metrics that calculates the number of new installations since the beginning of the quarter.

Organizations have practically always needed data analytics, and they jumped on the analytics bandwagon as soon as the first computers appeared on the scene. In the 80s, businesses built data warehouses using relational databases as their decision-support systems (DSS). However, as companies generated more diverse data at high velocity, relational databases showed their limitations.  This

Data Versioning at Scale: Solutions Overview Back when I was a 23-year-old student, I worked at an Israeli networking company as a BI analyst in the Operations department. My job revolved around modeling the company’s inventory which was quite costly and needed optimization.  At some point, I attended a meeting of the company’s management. When

A year has passed since we shared the State of Data Engineering 2021. And since we released that article last May, not much has changed in the data landscape. In fact, we had discussions internally about whether we should even do an update for 2022. We kid. It was another year worthy of its own

Let’s start with the obvious: the lakeFS project doesn’t exist in isolation. It belongs to a larger ecosystem of data engineering tools and technologies adjacent and complementary to the problems we are solving. What better way to visualize our place in this ecosystem, I thought, than by creating a cross-sectional LUMAscape to depict it. What’s

The demand for infrastructure that contributes to the collection, storage, and analysis of data is growing with the increasing amounts of data managed by organizations. Every organization that manages data pipelines to extract insights from data encounters the need for reproducibility, safe experimentation, and means to ensure data quality. The path to answering these needs