![Accelerating Analytics with Alteryx and Snowflake](/img/ad9e8532d.png?width="600&height=400&fit=crop&q=60)
![Amplify Business Analytics with Your Operational Data, CDC, and a Real-Time Data Lake](/img/01eb2ccaa.png?width="600&height=400&fit=crop&q=60)
![Streaming Operational Data to Cloud Data Lakes](/img/105db5ee3.jpg?width="600&height=400&fit=crop&q=60)
Latest Research
Accelerating Analytics with Alteryx and Snowflake
The strengths of Alteryx and Snowflake combine to optimize analytics while improving user experience
Amplify Business Analytics with Your Operational Data, CDC, and a Real-Time Data Lake
With a few clever steps in technology architecture, organizations can deliver timely data to analysts, BI teams, and data scientists without needing to hire teams of data engineers or push deadlines
Streaming Operational Data to Cloud Data Lakes
This white paper clarifies the importance and value of populating a cloud-based data lake with streaming operational data, leveraging database replication and modern data integration techniques
![Reference Architecture for Alteryx on Azure](/img/10f8375df.png?width="600&height=400&fit=crop&q=60)
Reference Architecture for Alteryx on Azure
This technical white paper presents the process, platform components and architecture patterns for an analytics environment
![How Alteryx on Azure Accelerates the Modern Analytics Lifecycle](/img/36d464cd7.png?width="600&height=400&fit=crop&q=60)
How Alteryx on Azure Accelerates the Modern Analytics Lifecycle
The Modern Analytics Lifecycle with Alteryx on Azure can transform companies into data-driven organizations that empower business analysts and data scientists
![Reference Architecture for Alteryx on AWS](/img/840e62b21.png?width="600&height=400&fit=crop&q=60)
Reference Architecture for Alteryx on AWS
This paper provides a straightforward reference architecture for IT architects and senior personnel interested in deploying the self-service platform Alteryx on AWS
![Making the Modern Analytics Lifecycle a Reality with Alteryx and AWS](/img/ba7287992.png?width="600&height=400&fit=crop&q=60)
Making the Modern Analytics Lifecycle a Reality with Alteryx and AWS
The Modern Analytics Lifecycle on AWS can transform companies into data-driven organizations that empower their business analysts and data scientists
![The Six Pillars of Enterprise Self-Service Data](/img/9a347a19a.jpeg?width="600&height=400&fit=crop&q=60)
The Six Pillars of Enterprise Self-Service Data
These essential six pillars of self-service data serve the needs of three different groups within the organization: business users, IT development and the enterprise data management cult
The Platform Approach for Enterprise Self-Service Data Success
A single platform that incorporates all six pillars is the ideal approach for enterprise self-service data capabilities rather than an ecosystem of various tools and products.
Leading Business Applications for Self-Service Data
Several important initiatives clearly benefit from self-service data’s key value propositions, such as customer 360 programs, compliance and GDPR, and ERP consolidations
Achieving Enterprise Self-Service Data
A successful enterprise self-service data strategy is dependent on a comprehensive set of core capabilities deployed together to empower business users with data
![The Core Components of an Enterprise Self-Service Data Strategy](/img/bc204657b.jpg?width="600&height=400&fit=crop&q=60)
The Core Components of an Enterprise Self-Service Data Strategy
To implement an enterprise self-service data strategy, six pillars are the foundation required to fully empower users with data
![Why Data Sampling Is Essential to Data Wrangling](/img/eb95ececb.jpg?width="600&height=400&fit=crop&q=60)
Why Data Sampling Is Essential to Data Wrangling
Data sampling is essential to data wrangling and the future of working with data in every company striving to be more effective with their data assets
![The Essential Role of Data Sampling in Data Wrangling Best Practices](/img/9cff74286.jpg?width="600&height=400&fit=crop&q=60)
The Essential Role of Data Sampling in Data Wrangling Best Practices
Why data sampling is beneficial, statistically valid and necessary in the data wrangling process for faster results, an improved user experience and increased quality of resulting data outputs
![The Reality of IoT Adoption](/img/e529f3583.jpg?width="600&height=400&fit=crop&q=60)
The Reality of IoT Adoption
An overall positive sentiment is driving organizations’ desire to make IoT a reality, and early adopters are tackling challenges of data security, infrastructure, and skill sets
![Internet of Things Market Study](/img/1036035e2.png?width="600&height=400&fit=crop&q=60)
Internet of Things Market Study
Radiant Advisors and Unisphere Research publish a brand new study on the current state of IoT adoption and maturity
![Data Lake Adoption and Maturity Survey Findings Report](/img/4a8361efb.jpg?width="600&height=400&fit=crop&q=60)
Data Lake Adoption and Maturity Survey Findings Report
By surveying both current and potential adopters in the industry, this study documents key perceptions, challenges and successes
![Why Spark Matters](/img/5258e9883.jpg?width="600&height=400&fit=crop&q=60)
Why Spark Matters
This paper guides the reader through the hype surrounding Spark to distill salient points on why Spark matters.
Insight Series: Visual Design
This Visual Design series covers the science, artistic curation and various cognitive processes enlisted in building effective data visualizations
![Three Checkpoints for Enabling Governed Data Discovery](/img/66f5d64a6.jpg?width="600&height=400&fit=crop&q=60)
Three Checkpoints for Enabling Governed Data Discovery
Learn what governed data discovery is and how to institute checkpoints within the discovery process to reduce friction between analysts and IT and enable governance and sharing of trusted, valid data