Systems | Development | Analytics | API | Testing

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.

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.

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.

Enterprise Data Warehouses (EDW) have emerged as a pivotal component for businesses striving to harness the power of data analytics and business intelligence. As technology advances, the complexity and volume of data sets have surged, accentuating the role of an EDW. This guide offers a deep dive into the intricacies of the Enterprise Data Warehouse, its significance, functionality, and the considerations for its implementation.

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).

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

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.