Let’s talk about the world’s currently trending topic for a second: AI. Now, before you click out of this blog, sighing to yourself that this is yet another blog that wants to tell you how to write code with ChatGPT; bear with us. As almost everyone has used some form of AI – especially ChatGpt – to help them with some form of a task, we can all agree that it’s quite an interactive experience.
Performance. Reliability. Cost-effectiveness. Unravel is a data observability platform that provides cost intelligence, warehouse optimization, query optimization, and automated alerting and actions for high-volume users of the Snowflake Data Cloud. Unravel leverages AI and automation to deliver realtime, user-level and query-level cost reporting, code-level optimization recommendations, and automated spend controls to empower and unify DataOps and FinOps teams.
Chad Sanderson is passionate about data quality, and fixing the muddy relationship between data producers and consumers. He is a former Head of Data at Convoy, a LinkedIn writer, and a published author. He lives in Seattle, Washington, and is the Chief Operator of the Data Quality Camp. Without data quality, your data initiatives will fail. Despite that, data teams still struggle to gain buy-in on quality initiatives from executive teams. Here's why: 1.
A paradigm shift is overdue in the realm of software observability. While Site Reliability Engineers (SREs) have been having fun with metrics, traces, and logs, software developers have been left in the lurch, shackled to the conventional, low-fidelity tool of logs. Why should SREs have all the fun, right? Welcome to the dawn of a new era. An era where developers, too, can enjoy superior observability engineering. That’s where the fourth pillar of observability comes in: Snapshots.
Increased costs and wasted resources are on the rise as software systems have moved from monolithic applications to distributed, service-oriented architectures. As a result, over the past few years, interest in observability has seen a marked rise. Observability, borrowed from its control theory context, has found a real sweet spot for organizations looking to answer the question “why,” that monitoring alone is unable to answer.
Since leaving university, I have always been involved with data, learning so much during my time as an automation engineer and big data engineer working for an automotive tier 1 manufacturing company and as a data architect for an IT consultancy in the SAP and BI domain. And today, I count myself very fortunate to have a job at Kensu that I am truly passionate about. As a Technical Solution Architect, I get to help organizations with data health and data engineering problems on a daily basis!
