Survey: Enterprise data leaders see value in modern infrastructure
IT decision-makers offer insights into their modernization plans, which include automating data integration and adopting lakehouse technology.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Expert Panel: Challenges with Modern Data Pipelines
Modern data pipelines have become more business-critical than ever. Every company today is a data company, looking to leverage data analytics as a competitive advantage. But the complexity of the modern data stack imposes some significant challenges that are hindering organizations from realizing their goals and realizing the value of data.
The History of BI Dashboards
Technology is always evolving, and so is the way we use it to collect and analyze data. From humble spreadsheet beginnings to fully automated business monitoring and AI-powered analysis, the range of analytics tools on offer today is astounding to consider. The business intelligence dashboard is one such option. It has existed for decades as a tool for organizations to monitor and analyze operational data - and it's not quite dead yet.
How the modern data stack helps analysts avoid dashboard hell
The modern data stack promises greater agility for data teams, best of breed capabilities, and gaster time to market. But does it elevate the analyst's & analytics engineer career and bring joy back to their daily job? Dead end dashboards on a modern cloud data platform pigeon-holes analysts into repetitive, low-value work. Join ThoughtSpot co-founder and CTO Amit Prakash and Chief Data Strategy Officer, Cindi Howson for a real-time deep dive on how the modern data stack is elevating the analyst and empowering business leaders.
Five Reasons for Migrating HBase Applications to Cloudera Operational Database in the Public Cloud
Apache HBase has long been the database of choice for business-critical applications across industries. This is primarily because HBase provides unmatched scale, performance, and fault-tolerance that few other databases can come close to. Think petabytes of data spread across trillions of rows, ready for consumption in real-time.
5 Insights from Gartner's Hype Cycle for Data Management 2022 Report
As a global leader in technology research, Gartner supports enterprise organizations, non-profits, and government agencies by sharing information and in-depth analysis of emerging technological trends, tools, and products. With the continued growth of big data over the past decade, Gartner has been especially invested in helping data and analytics (D&A) leaders make the right decisions for managing and generating value from data within their organizations.
15 Ways to Use Google Analytics to Track and Improve Your SEO Performance
Want to learn how to harness the power of Google Analytics and properly measure your SEO performance? 50+ SEOs share their best hacks.
TDWI Webinar Expert Panel Modern Data Pipelines
Expert Panel: Modern Data Pipelines TDWI Research VP Fern Halper hosts an expert panel with leading vendors to discuss modern data pipelines.
10 Customer Success Tools That Work Well For Ecommerce Businesses
Customer service has always been the underdog that significantly affects a business’s performance but is always overlooked due to a lack of time and resources. While businesses focused on generating more revenue, they missed an important contributor to that revenue- a positive customer experience. Using the customer’s perspective to stay aligned with the evolving customer needs is essential for your business to foster a customer-centric culture.
A DataOps Observability Dialogue: Empowering DevOps for Data Teams
A DataOps Observability Dialogue: Empowering DevOps for Data Teams It used to be said that software is eating the world, but now data is running things. And it’s high-functioning data teams who make it all happen. But data teams are facing several obstacles that prevent them from delivering innovative analytics at today’s increased speed and scale. Software teams have been facing the same challenges for 10+ years and have tackled them with DevOps. So why are DataOps teams struggling when DevOps teams aren’t? They’re using the same tools to solve basically the same problem. . . .