Snowflake data engineers are under enormous pressure to deliver results. This data sheet provides more context about the challenges data engineers face and how Unravel helps them address these challenges. Specifically, it discusses: With Unravel, Snowflake data engineers can speed data pipeline development and analytics initiatives with granular and real-time cost visibility, predictive, predictive spend forecasting, and performance insights for their data cloud.

Organizations are transforming their industries through the power of data analytics and AI. A recent McKinsey survey finds that 75% expect generative AI (GenAI) to “cause significant or disruptive change in the nature of their industry’s competition in the next three years.” AI enables businesses to launch innovative new products, gain insights into their business, and boost profitability through technologies that help them outperform competitors.

Snowflake’s data cloud has expanded to become a top choice among organizations looking to leverage data and AI—including large language models (LLMs) and other types of generative AI—to deliver innovative new products to end users and customers. However, the democratization of AI often leads to inefficient usage that results in a cost explosion and decreases the business value of Snowflake. The inefficient usage of Snowflake can occur at various levels.

Hey there! Have you ever found yourself scratching your head over unpredictable cloud data costs? It’s no secret that accurately forecasting cloud data spend can be a real headache. Fluctuating costs make it challenging to plan and allocate resources effectively, leaving businesses vulnerable to budget overruns and financial challenges. But don’t worry, we’ve got you covered!

The data landscape is constantly evolving, and with it come new challenges and opportunities for data teams. While generative AI and large language models (LLMs) seem to be all everyone is talking about, they are just the latest manifestation of a trend that has been evolving over the past several years: organizations tapping into petabyte-scale data volumes and running increasingly massive data pipelines to deliver ever more data analytics projects and AI/ML models.

Google recently introduced significant changes to its existing BigQuery pricing models, affecting both compute and storage. They announced the end of sale for flat-rate and flex slots for all BigQuery customers not currently in a contract. Google announced an increase to the price of on-demand analysis by 25% across all regions, starting on July 5, 2023.

Data is a powerful force that can generate business value with immense potential for businesses and organizations across industries. Leveraging data and analytics has become a critical factor for successful digital transformation that can accelerate revenue growth and AI innovation.

New Functionality Delivers FinOps, AI-driven Cloud Cost Management and Performance Optimization for BigQuery Users.

With the dramatic increase in the volume, velocity, and variety of data analytics projects, understanding costs and optimizing expenditure is crucial for success. Data teams often face challenges in effectively managing costs, accurately attributing them, and finding ways to enhance cost efficiency.

DBS Bank leverages Unravel to identify inefficiencies across 10,000s of data applications/pipelines and guide individual developers and engineers on how, where, and what to improve—no matter what technology or platform they’re using.