In this video interview with Ashwin Rajeeva, co-founder and CTO of Acceldata, we talk about the company’s data observability platform – what “data observability” is all about and why it’s critically important in big data analytics and machine learning development environments.
Acceldata and its Data Observability Platform – Solving Big Data Management Challenges
March 28, 2023 by
Cloudera Continues Rapid Pace of Data Fabric and Data Lakehouse Innovation to Extend Data Management Leadership
October 15, 2022 by
Cloudera, the hybrid data company, announced new hybrid data capabilities that enable organizations to more efficiently move data, metadata, data workloads and data applications across clouds and on premises to optimize for performance, cost and security. Cloudera’s portable data services enable simple, low-risk data workload and data application movement for ultimate data lakehouse optionality.
Report: New Data Management Models Are Essential To Operate In The Cloud
August 18, 2022 by
As organizations increasingly embrace cloud-first principles and the quantity and variety of their data exponentially increases, Capital One’s new Forrester study finds the vast majority of data management decision-makers are deeply concerned about controlling and forecasting data costs, leveraging data at scale, addressing data quality and consistency, and better protecting data.
Benefits of Automation for Enterprise Data Management
July 18, 2022 by
In this article we’ll take a look at how’s and why’s that organizations from many industries are jumping on the automation for data management movement. It’s important that stakeholders understand how well automation performs repetitive data management responsibilities, what tasks still require a human in the loop, and how to evaluate data management automation capabilities.
Looking Ahead | Observability Data Management Modernization
May 13, 2022 by
In this contributed article, Karen Pieper, VP of engineering at Era Software, discusses how organizations today use real-time data streams to keep up with evolving business requirements. Setting up data pipelines is easy. Handling the errors at each stage of the pipeline and not losing data is hard.
Surpassing Decentralized Data Management Woes with Data Virtualization
June 11, 2021 by
In this contributed article, editorial consultant Jelani Harper discusses how data virtualization enables organizations to surmount obstacles (i.e. data quality, schema, and data integrations that are foundational to data management) and to focus on benefits (i.e. remote collaborations characteristic of working from home, the takeoff of the cloud as the de facto means of deploying applications, and the shift to external sources of unstructured and semi-structured data). Supplementing it with mutable graph data models boosts its applicability to data of all types.
The State of Data Management – Why Data Warehouse Projects Fail
September 8, 2020 by
Based on new research commissioned by SnapLogic and conducted by Vanson Bourne, who surveyed 500 IT Decision Makers (ITDMs) at medium and large enterprises across the US and UK, this whitepaper explores the data management challenges organizations are facing, the vital role data warehouses play, and the road to success.
The State of Data Management – Why Data Warehouse Projects Fail
September 4, 2020 by Leave a Comment
Based on new research commissioned by SnapLogic and conducted by Vanson Bourne, who surveyed 500 IT Decision Makers (ITDMs) at medium and large enterprises across the US and UK, this whitepaper explores the data management challenges organizations are facing, the vital role data warehouses play, and the road to success.
83% of IT Leaders are Not Fully Satisfied with their Data Warehousing Initiatives, According to New Research from SnapLogic
August 9, 2020 by
New research published by SnapLogic, provider of the Intelligent Integration Platform, reveals that 83% of organizations are not fully satisfied with the performance and output of their data management and data warehousing initiatives. IT leaders cite a growing number of disconnected applications and data sources, outdated legacy systems, and slow and manual data movement as reasons for their frustration, all of which are stalling progress and costing them millions.
Interview: Steve Yurko, CEO of APEX Analytix
January 28, 2020 by
I recently caught up with Steve Yurko, CEO of APEX Analytix to discuss his views on the big data technology landscape in 2020. in order to become true big data winners, companies need to prioritize quality data as the foundation of predictive analytics that ultimately answer real business questions.
