Drag and drop analytics are more interactive and user-friendly compared to traditional, high code business intelligence (BI) solutions. They allow users without programming experience to easily explore the data and don't require coding knowledge, with a drag and drop user interface to conveniently enhance functionality of any dashboard report. In this post, you will find the importance of drag and drop analytics for more seamless reporting and user experiences when analyzing business-critical data.

Tuning Hive on Tez queries can never be done in a one-size-fits-all approach. The performance on queries depends on the size of the data, file types, query design, and query patterns. During performance testing, evaluate and validate configuration parameters and any SQL modifications.

Hitachi Vantara recently commissioned Forrester Consulting to conduct a Total Economic Impact (TEI) study to examine the value that customers could achieve using cloud and application modernization services from Hitachi Vantara. To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed four decision-makers at companies with experience using cloud and app modernization services from Hitachi Vantara.

The idea of running compute and storing data in the cloud is no longer a novel concept. With the evolution of 5G and Internet of Things (IoT), this brings along the next evolution of edge storage demands. Today, around 10% of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. By 2025, Gartner predicts this figure will reach 75%.

Yellowfin’s goal is to be the best choice for building business intelligence (BI) and analytics modules for custom applications. In this blog, GM Dev Tools for Idera and Yellowfin, Atanas Popov shares the company's ambitious vision for 2022 and beyond.

Nearly a decade after Amazon announced Redshift, its competitor to Apache’s Hadoop, the Hadoop vs. Redshift debate continues. For companies seeking a reliable data warehousing solution, both are worthy contenders, but examining the following aspects will help you decide the winner for yourself.

Customers who work with data warehouses, running BI on large datasets used to have to pick low latency but trading off freshness of data. With BigQuery BI Engine, they can accelerate their dashboards and reports that connect to BigQuery without having to sacrifice freshness of the data. Using the latest insights helps them make better decisions for the business.

We like to keep things light at Unravel. In a recent event, we hosted a group of industry experts for a night of laughs and drinks as we discussed cloud migration and heard from our friends at Don’t Tell Comedy. Unravel VP of Solutions Engineering Chris Santiago and AWS Sr. Worldwide Business Development Manager for Analytics Kiran Guduguntla moderated a discussion with data professionals from Black Knight, TJX Companies, AT&T Systems, Georgia Pacific, and IBM, among others.

The term DataOps (like its older cousin, DevOps) means different things to different people. But one thing that everyone agrees on is its objective: accelerate the delivery of data-driven insights to the business quickly and reliably.

Business intelligence empowers businesses to get the most out of their data by providing tools to analyze information, streamline operations, track performance, and inform decision-making. In the Microsoft Dynamics ecosystem, Power BI generates easy-to-read visualizations that help stakeholders perform key analysis. But finance professionals can encounter roadblocks when seeking deeper analysis than their technical knowledge of Power BI permits.