Artificial intelligence (AI) is the next significant technological frontier, poised to revolutionize the tech sector, particularly through its massive impact on cloud infrastructures. By 2024, this transformation is expected to be as widespread as managed Kubernetes services, with an estimated 70% of organizations utilizing managed AI services in their cloud setups.

Web analytics dashboards are essential tools for businesses looking to enhance their online presence, optimize user experience, and achieve their wider business objectives. By supplying actionable insights and facilitating data-driven decision-making, these dashboards help businesses stay competitive in today's digital landscape.

Snowflake is an AI data cloud platform that breaks down silos within an organization to enable wider collaboration with partners and customers for storing, managing, and analyzing data. With Snowpark and Snowpark Container Services (SPCS), organizations can leverage a set of libraries and execution environments directly in Snowflake to build applications and pipelines with familiar programming languages like Python and Java, all without having to move data across tools or platforms.

For data-critical and data-intense sectors, like energy and renewables, access to data can be a make-or-break situation. As the complexity of the systems underpinning energy operations increases, collecting and analyzing that data is more challenging than ever before. Therefore, understanding what data sources are necessary, where they sit in the tech stack, and how they scale across an organization ‌is crucial for obtaining the insights energy companies need to maintain and optimize operations.

In 2020, we introduced the Snowflake Enterprise data source plugin for Grafana, allowing users to seamlessly pull data from the Snowflake cloud-based data storage and analytics service into Grafana dashboards. Available for Grafana Enterprise and Grafana Cloud users, it’s a powerful way to not only query and visualize Snowlake data, but to do so alongside other data sources, so you can discover correlations and other meaningful insights within minutes.

In this blog series we’ll be examining the Five Myths of Apache Spark Optimization. (Stay tuned for the entire series!) The first myth examines a common assumption of many Spark users: Observing and monitoring your Spark environment means you’ll be able to find the wasteful apps and tune them.