Blog

Introduction

A novel approach to solving analytical data challenges, data mesh is rapidly gaining ground—and for good reason.  

You may have seen the news that Snowflake announced the intent to acquire Streamlit to “empower developers and data scientists to mobilize the world’s data”. This sounds amazing and great news for Data Engineers and developers alike!

By now, it is probably a well-known fact among our clients that before developing our DataOps for Snowflake data orchestration platform, we set about to define the principles that we felt were critical to ensuring that we laid the proper foundations for developing a data platform that would change the global enterprise data management solutions.  

Latest round of investment will enable this innovative start-up to scale-up rapidly, attract even more talent, expand further into North America, and build on current project successes.

Integrated solution ensures optimal speed and automation of Snowflake workloads while improving data governance and security with enhanced access controls.

The challenges posed by the ever-increasing and ever-pervasive nature of data, as well as the increase in prominence of the cloud-based data storage paradigm, are driving the mandate to control access to enterprise data, namely, sensitive, regulated, and Personally Identifiable Information (PII).

Throughout 2021, data volumes have continued to explode unabated, thereby driving the need to adopt a robust data management/orchestration system to manage this enterprise data. Over the last few years, the term “DataOps” has become somewhat of a buzzword, albeit justified with global searches on the word and its derivatives up over 500% since 2018.

As enterprise data continues to explode in volume, velocity, and variety, executive leaders must implement mechanisms to ensure that this data is always governed and secure. Therefore, the question that begs is how do organizations make sure their enterprise data is governed and protected? 

The title’s questions: What is metadata, and can you have too much of it, are fundamental to answering the overarching question: What role does metadata play in the modern data stack?