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Cloud Data Cost Q&A 2024: Your Top 7 Cloud Data Cost Questions Answered (In 30 minutes)

In 2023, cloud service usage was supercharged to enable the creation and scaling of new products and services, to gain faster business insight, and to leverage the emergence of new technologies such as Gen AI. However, with cloud spending expected to increase by 20.4% in 2024, how are YOU ensuring inefficient code and underutilized cloud resources are not contributing to your overall costs?

Unlocking Snowflake: Scale your data cloud efficiently with purpose-built AI

See what’s new for Snowflake and how you can take advantage of the latest innovation from Unravel to better manage your data cloud costs. Data analytics and AI enables organizations to transform their industries with innovative new products, faster business insights, and break-away technologies that help them outperform competitors.

Top 4 Challenges to Scaling Snowflake for AI

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.

Announcing Unravel for Snowflake: Faster Time to Business Value in the Data Cloud

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.

Transforming analytics on the cloud: Supercharge your data applications

Transforming analytics on the cloud: Supercharge your data applications with Databricks, AWS and Unravel Organizations are feeling pressure to launch new data applications faster to meet end-user demand. Cloud data platforms help accelerate launch times with on-demand delivery of infrastructure and pay-as-you-go pricing. Last year, 98% of the overall database management system (DBMS) market growth came from cloud database platform as a service (dbPaaS). 80% of organizations have adopted agile practices to increase their pace of innovation.

Unravel CI/CD Integration for Databricks

CI/CD, a software development strategy, combines the methodologies of Continuous Integration and Continuous Delivery/Continuous Deployment to safely and reliably deliver new versions of code in iterative short cycles. This practice bridges the gap between developers and operations team by streamlining the building, testing, and deployment of the code by automating the series of steps involved in this otherwise complex process.

How Unravel Enhances Airflow

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.

Accelerate the Data Analytics Life Cycle with Unravel

Organizations want to get faster value from AI/ML. In order to do that, they need to go through a data lifecycle -- from data ingestion, curation and refinement, to production data pipeline development and deployment, and then model creation and model deployment. With this in mind, Unravel is hosting a live event to help you quickly go from start to finish. This is your opportunity to learn how you can leverage Unravel’s purpose-built AI to accelerate your full data lifecycle.

Rev Up Your Lakehouse: Lap the Field with a Databricks Operating Model

In this fast-paced era of artificial intelligence (AI), the need for data is multiplying. The demand for faster data life cycles has skyrocketed, thanks to AI’s insatiable appetite for knowledge. According to a recent McKinsey survey, 75% expect generative AI (GenAI) to “cause significant or disruptive change in the nature of their industry’s competition in the next three years.” Next-gen AI craves unstructured, streaming, industry-specific data.