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.

Download a sample report that includes insights into the health of a Snowflake data estate.

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.

In today’s data-driven world, there is a huge amount of data flowing into the business. Engineers spend a large part of their time in building pipelines—to collect the data from different sources, process it, and transform it to useful datasets that can be sent to business intelligence applications or machine learning models. Tools like Airflow are used to orchestrate complex data pipelines by programmatically authoring, scheduling, and monitoring the workflow pipelines.

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.