Complimentary Analyst Report

Where does a semantic layer fit in with your Data Virtualization needs?

GigaOm Radar Report for Data Virtualization capabilities

We live in the era of diverse and disparate data. The pure volume and speed with which data is generated is beyond the capacity and economic bounds of today’s typical enterprise infrastructures.

Download this complimentary Analyst Report for research conducted by Andrew Brust and Yiannis Antoniou to help Enterprises evaluate technologies that aid with Data Virtualization.

A semantic layer offers data virtualization capabilities with a focus on business intelligence (BI) and analytics support with additional levels of features and engineering on top – making it easier to meet self-service BI demands, and more quickly satisfy new reporting and analysis requirements over source data.

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Spoiler Alert!

Enterprises that implement a semantic layer powered by data virtualization get instant access to their structured and unstructured data from relational data warehouses, data lakes, and enterprise SaaS applications to create logical data warehouses, accessible with SQL, MDX, DAX, Python and REST. This provides access to data from a broader set of distributed sources and storage formats – without requiring users to know where the data resides or how it’s formatted.

A ‘Semantic Layer’ – Why Now?

A semantic layer provides business friendly data access wherever the data may lay. Whether its data at rest in a data lake or warehouse, or data that lives behind a SaaS application’s APIs, the semantic layer should hide the physical location and form of the data from consumers.

Data virtualization is a key ingredient of a semantic layer because it presents a “logical” view of the data. By minimizing manual data movement (ETL/ELT), a semantic layer powered by data virtualization improves business agility by removing dependencies on IT and providing fresh, up to date data.

“AtScale’s strong support for a business-friendly semantic layer and its intelligent data virtualization are probably its most compelling features.

Coupled with an easy-to-use web-based design interface, multi cloud support, and strong data source and tool integration, the offering should have wide appeal, especially for organizations wanting to create or enhance a self-service data culture.

In addition, the decoupling between physical infrastructure and logical layer should make AtScale a great fit for data modernization and migration initiatives.”

Yiannis Antoniou

Gigaom

“The growth of data, and increased accessibility of self-service Business Intelligence (BI) tools such as Microsoft’s Power BI, Tableau, and Looker, are creating more concurrent queries against a broad set of data sources.

Compounding this trend is the growing use and capabilities of cloud services, and the evolution of Big Data solutions such as Apache Hadoop and Spark.

Besides these technical trends, more organizations are hiring Chief Data Officers (CDOs) to figure out how to make this new data a competitive differentiator – and, to make smarter data-driven decisions at scale.

The traditional approach of moving and transforming data to meet these needs and power these analytic capabilities is becoming less feasible with each passing requirement…

Andrew Brust

Gigaom

Who should read this report?

Data-driven leaders and practitioners (e.g., Chief Data Officers, data scientists, business intelligence, and analytics professionals) looking to benefit from diverse and disparate datasets.