Data Ecosystem

Understanding the Differences Between Data Lake Engines, Data Virtualization, and Data Fabric

With the appearance of cloud object storage services like AWS S3 or Azure ADLS, the data lake has seen an upturn in usage as some of the challenges of the original idea were addressed. However, companies across the globe still find it challenging to adopt data lakes into the corporate data ecosystem. While almost infinite in storage, data retrieval from these sources and integration of the data with the corporate ecosystem is still an arduous task for data engineers. This leads to data lakes becoming either a silo or a secondary form of storage instead of feeding business processes and...

Organizations continue to collect mounds of data and it is spread over different locations and in different formats. The challenge is navigating the vastness and complexity of the modern data ecosystem to find the right data to suit your specific business purpose. Data is an important corporate asset and it needs to be leveraged but also protected.By adopting an alternate approach to data management and adapting a logical data architecture, data can be democratized while providing centralized control within a distributed data landscape. The web-based Data Catalog tool a single access point...

Presented at Data Analytics for Enterprise (Australia)Today most organizations rely on data-driven insights to make critical operational and strategic decisions. But given the complexity and vastness of the modern enterprise data ecosystem, finding the right data set and extracting actionable insights is an extremely complicated and time-consuming process.That’s where data virtualization can be used to abstract that complexity and introduce simplicity. Data Virtualization is a modern data integration technique that uses a single unified semantic layer to give you visibility into all of your...

Denodo customer presentation by Kurt Jackson, Platform Architect at Autodesk.