BigQuery data clean rooms can help organizations create and manage secure environments for privacy-centric data sharing, analysis, and collaboration.

Your team needs the latest, cutting-edge upgrades for reporting so they can communicate and collaborate via a central platform. How can you ensure they are enabled to access the real-time data and build the reports they need? Keep reading to see some benefits that upgrading to Hubble Enterprise can provide. Hubble Enterprise allows existing Hubble customers to achieve their business goals with easy, immediate access to business-critical data.

As announced at Snowflake Summit 2022, Iceberg Tables combines unique Snowflake capabilities with Apache Iceberg and Apache Parquet open source projects to support your architecture of choice. As part of the latest Iceberg release, we’ve added catalog support to the Iceberg project to ensure that engines outside of Snowflake can interoperate with Iceberg Tables.

The evolution of healthcare has come a long way since local physicians made house calls and homespun remedies were formulated using items from the kitchen spice rack. Today’s healthcare is driven as much by the promise of emerging technologies centered on data processing and advanced analytics as by developing new and specialized drugs.

When it comes to analytics capability that caters to diverse data needs across the entire business, Yellowfin provides specific advantages compared to Tableau in several areas. Tableau users may find the platform can be complex, or lacking, in areas such as dashboard design, data governance, or flexibility. Thankfully, many have found Yellowfin to be a great alternative, and even complementary solution, to their analytic needs.

It’s been a decade since “connected” objects—commonly referred to as “the internet of things” (IoT)— reached broad audiences. Connected toothbrushes, sensors embedded in sneakers, and smart watches have started to change consumer behavior through a data-driven, gamified approach. Technology has rapidly evolved to handle large data volumes at high velocities and big data analytics. AI has become more democratized.

The best description of untrusted data I’ve ever heard is, “We all attend the QBR – Sales, Marketing, Finance – and present quarterly results, except the Sales reports and numbers don’t match Marketing numbers and neither match Finance reports. We argue about where the numbers came from, then after 45 minutes of digging for common ground, we chuck our shovels and abandon the call in disgust.” How would you go about fixing that situation?

Cloudera SQL Stream Builder (SSB) gives the power of a unified stream processing engine to non-technical users so they can integrate, aggregate, query, and analyze both streaming and batch data sources in a single SQL interface. This allows business users to define events of interest for which they need to continuously monitor and respond quickly. There are many ways to distribute the results of SSB’s continuous queries to embed actionable insights into business processes.

Recently, I published an article on whether self-service BI is attainable, and spoiler alert: it certainly is. Of course, anything of value usually does require a bit of planning, collaboration, and effort. After the article was published, I began having conversations with technical leaders, analysts, and analytics engineers, and the topic of data modeling for self-service analytics came up repeatedly.