Organizations of all sizes are dealing with exponentially increasing data volume and data sources, which creates challenges such as siloed information, increased technical complexities across various systems and slow reporting of important business metrics. Migrating to the cloud does not solve the problems associated with performing analytics and business intelligence on data stored in disparate systems. Also, the computing power needed to process large volumes of data consists of clusters of servers with hundreds or thousands of nodes that can be difficult to administer. Our Analytics and Data Benchmark Research shows that organizations have concerns about current analytics and BI technology. Findings include difficulty integrating data with other business processes, systems that are not flexible enough to scale operations and trouble accessing data from various data sources.
To overcome these challenges, a semantic layer approach can map complex and distributed data into one, consolidated view of business metrics and definitions, while controlling the complexity and cost of analytics. Semantic layers can help bridge the gap between data sources and line-of-business users, enabling a wide range of skilled workers to generate reports without creating IT requests, accelerating data-driven decisions at scale.
AtScale offers semantic layer software, a virtualized dimensional modeling and analytics platform that enables analysts to perform rapid, multidimensional analysis. It connects with various BI and cloud data platforms, including Tableau, Power BI, Excel, Snowflake, AWS, Microsoft Azure, Google Cloud and Databricks without manual data engineering. Its intelligent data virtualization platform provides Cloud OLAP, Autonomous Data Engineering and a Universal Semantic Layer for data-driven business intelligence and machine learning analysis at scale.
AtScale Query Engine acts as a query interface for business intelligence, artificial intelligence and machine learning tools and custom applications. Tools can connect to AtScale via one of the various protocols, including ODBC/JDBC, MDX, DAX, XMLA, Python and REST. Workers can interact with data using the same dimensions, hierarchies, and measures defined in its Design Center. AtScale delivers data as a service to all data users with permission to share and collaborate. AtScale’s Autonomous Data Engineering enables workers to build, manage and maintain data structures, identifying scenarios and applying multiple strategies. Its AI-driven optimizer learns from user behavior and data relationships to improve data agility, security and performance.
More and more organizations are migrating data and analytics infrastructures to the cloud. But many organizations still have data stored in on-premises, cloud and hybrid environments. Our research shows that 75% of organizations are still using spreadsheets for data preparation, 59% create custom scripts, and 50% use stand-alone data integration tools. By using a semantic layer software such as AtScale, organizations can connect to multiple data sources and create a self-service data analytics culture. Its semantic layer makes data stored in data lakes or data warehouses accessible with the same interface. Its data virtualization functionality provides access to enterprise data by functioning as an abstraction layer on top of a variety of data platforms, without manually moving data.
AtScale is developing more integrations with other BI tools and platforms. It recently announced successful integration with Amazon Redshift, which enables AWS customers to evaluate and use technology at scale and at varying levels of complexity. Another area for potential improvement for AtScale is to expand AI-Link to offer more direct support for AI/ML algorithms and modeling processes.
I recommend that organizations with big data on multiple systems looking to democratize and scale analytics and business intelligence in the cloud consider the capabilities of AtScale. Its Cloud OLAP makes data ready for analysis with no data movement or precomputation, eliminating the cost and bottlenecks associated with traditional OLAP solutions. Plus, it allows line-of-business workers to create virtual analytics cubes on top of Amazon Redshift, Azure Synapse Analytics, Google BigQuery, Snowflake and other cloud data warehouses.