In this blog series, Yellowfin Chief Technology Officer (CTO) Brad Scarff breaks down some of the coolest and most unique features of the Yellowfin embedded analytics suite. What if you could automate the delivery of reports on set schedules? What if you could create save states of all your report filters and drill-downs? Yellowfin allows just that.

Learn how to use BigQuery analytics hub to set up a functional dataset share across organizations.

Telecommunications companies are currently executing on ambitious digital transformation, network transformation, and AI-driven automation efforts. While navigating so many simultaneous data-dependent transformations, they must balance the need to level up their data management practices—accelerating the rate at which they ingest, manage, prepare, and analyze data—with that of governing this data.

In the ever-evolving landscape of the financial services Industry, change is a constant and transformation is a requirement—to stay at pace with new regulations, risk mitigation, and the technological developments that support transformation. And just as financial services experiences its cycles, this time of year I find myself returning to the topic of cost reduction.

Product-led growth (PLG) is a business model that emerged in the last decade with the enormous success of vendors like Slack and Datadog. Unlike traditional sales-led models, PLG models cut out the middlemen (sales reps, for example) and let customers just download and use the product without third-party onboarding. The relative novelty of the pricing model and its demonstrably successful application in growing these companies attracted a lot of attention.

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.

Cloudera recently released a fully featured Open Data Lakehouse, powered by Apache Iceberg in the private cloud, in addition to what’s already been available for the Open Data Lakehouse in the public cloud since last year. This release signified Cloudera’s vision of Iceberg everywhere. Customers can deploy Open Data Lakehouse wherever the data resides—any public cloud, private cloud, or hybrid cloud, and port workloads seamlessly across deployments.

Multi-tenancy is almost a prerequisite to provide a secure environment for each of your customers when using business intelligence (BI) tools embedded in external services. Although it is possible to control the access rights by granting individual access to user accounts without separating tenants, it is obvious that the management will become more complicated as the number of customers grows. In a previous blog, we covered what multi-tenancy means in the context of embedded analytics.

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.

Learn how to perform analytics on BigQuery data using BigQuery DataFrames and its bigframes.pandas and bigframes.ml APIs.