How to Build Multi-Tenant Environments with Yellowfin BI

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.

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.

The Evolution of Search: How Multi-Modal LLMs Transcend Vector Databases

As we venture deeper into the data-driven era, the traditional systems we have employed to store, search, and analyze data are being challenged by revolutionary advancements in Artificial Intelligence. One such groundbreaking development is the notable advent of Large Language Models (LLMs), specifically those with Multi-Mod[a]l abilities (e.g., Image & Audio).

Hevo Data vs. Talend vs. Integrate.io: Key Features & More

In the realm of data management, Hevo Data, Talend, and Integrate.io stand out, each bringing distinct data integration capabilities. While all three platforms prioritize integrations and 24/7 customer support, they differ in their offerings and pricing structures. Hevo Data is budget-friendly, Talend is enterprise-centric, and Integrate.io provides a balanced approach for various business sizes. Your choice will depend on your specific needs, budget, and preferred features.

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.

Snowflake and Partners Develop Award-Winning Solution to Give Telecoms and Consumers the Power to Reduce Carbon Emissions with Generative AI

In the age of climate consciousness, industries worldwide are grappling with the urgent need to reduce their carbon footprints. One industry that has come under increased scrutiny is telecommunications, where Scope 3 emissions, or the indirect emissions that occur in a company’s value chain that the company has no direct control over, alone account for a staggering 85% of a typical telecom company’s carbon footprint.