In today’s data-driven enterprises, the intersection of fiscal responsibility and technical innovation has never been more critical. As data processing costs continue to scale with business growth, building a FinOps culture within your Data Engineering team isn’t just about cost control, it’s about creating a mindset that views cost optimization as an integral part of technical excellence.

Effective cost management becomes crucial as organizations increasingly rely on Google BigQuery for their data warehousing and analytics needs. This checklist delves into the intricacies of cost management and FinOps for BigQuery, exploring strategies to inform, govern, and optimize usage while taking a holistic approach that considers queries, datasets, infrastructure, and more.

In the world of big data, configuration management is often the unsung hero of platform performance and cost-efficiency. Whether you’re working with Snowflake, Databricks, BigQuery, or any other modern data platform, effective configuration management can mean the difference between a sluggish, expensive system and a finely-tuned, cost-effective one.

Unravel Data is the DataOps observability platform for the modern data stack. Unravel provides AI-powered recommendations for performance, cost governance, and quality so data teams spend less time firefighting and more time innovating. Let’s see how it works.

Modern data analytics platforms like Databricks have become indispensable for many companies. However Databricks costs can become unpredictable. This article gives four steps to help companies like yours predict Databricks costs and save your company time and money.

Companies are under pressure to deliver faster innovation, enabled by cloud-based data analytics and AI. In order to deliver faster business value, data teams are looking to achieve speed and scale through data and AI pipeline performance and efficiency. A recent MIT Technology Review Insights report finds that 72% of technology leaders agree that data challenges are the most likely factor to jeopardize AI/ML goals.

Data is the driving force of the world’s modern economies, but data teams are struggling to meet demand to support generative AI (GenAI), including rapid data volume growth and the increasing complexity of data pipelines. More than 88% of software engineers, data scientists, and SQL analysts surveyed say they are turning to AI for more effective bug-fixing and troubleshooting. And 84% of engineers who use AI said it frees up their time to focus on high-value activities.

Privacy and security are top priorities for Unravel and our customers. At Unravel, we help organizations better understand and improve the performance, quality, and cost efficiency of their data and AI pipelines. As a data business, we appreciate the scope and implications of privacy and security threats. This data sheet provides details to help information security (InfoSec) teams make informed decisions. Specifically, it includes: For additional details, please reach out to our security experts.

Unravel Data, the first AI-enabled data actionability and FinOps platform built to address the speed and scale of modern data platforms, today announced it has been named in the 2024 Gartner Market Guide for Data Observability Tools. According to Gartner, “By 2026, 50% of enterprises implementing distributed data architectures will have adopted data observability tools to improve visibility over the state of the data landscape, up from less than 20% in 2024.

In the data-driven world where real-time decision-making and innovation are not just goals but necessities, global data analytics and technology companies like Equifax must navigate a complex environment to achieve success. Equifax sets the standard for operational excellence by enabling real-time decision-making, accelerating innovation, scaling efficiently, consistently achieving service level agreements (SLAs), and building reliable data pipelines.