Fortune 50 Retailer Modernizes Analytics with AtScale
公司规模
1,000+
地区
- America
国家
- United States
产品
- AtScale’s semantic layer platform
- Google Big Query
- Excel
- Tableau
技术栈
- Google BigQuery
- SQL Server Analysis Services (SSAS)
- Teradata
- Hadoop
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Productivity Improvements
技术
- 分析与建模 - 大数据分析
- 基础设施即服务 (IaaS) - 云计算
适用行业
- 电子商务
- 零售
适用功能
- 商业运营
服务
- 云规划/设计/实施服务
- 数据科学服务
关于客户
The customer is a Fortune 50 retailer that operates on a large scale. They have thousands of internal and external analytics consumers who rely on their data for various purposes. The retailer has a diverse set of legacy platforms, including SQL Server Analysis Services (SSAS), Teradata, and Hadoop, which were proving to be expensive and unable to scale at the rate of their business. The retailer's primary goal was to modernize their analytics infrastructure to increase the flow of data-driven insights that could lead to improved margins, optimization of product mix, and better inventory management. They needed a solution that could scale, support security and access control policies, and support their migration from on-premise legacy data platforms to a cloud data warehouse.
挑战
A Fortune 50 retailer launched an initiative to modernize their analytics infrastructure with the primary goal of increasing the flow of data-driven insights that could lead to improved margins, optimization of product mix and better inventory management. Their challenge was to enable better analytics at scale while ensuring efficiency and consistency across a broad audience of data consumers. With thousands of users performing analytics using a diverse set of legacy platforms, including SQL Server Analysis Services (SSAS), Teradata, and Hadoop, the existing infrastructure was expensive and could not scale at the rate of their business. To empower their users, the data team needed a scalable semantic layer solution that could serve the needs of internal users as well as suppliers that rely on a shared view of inventory. The solution needed to scale, needed to support security and access control policies, and needed to support the organization’s migration from on-premise legacy data platforms to a cloud data warehouse.
解决方案
The retailer partnered with AtScale to replace traditional SQL Server Analysis Services (SSAS) OLAP instances. The AtScale semantic layer delivered the analytics performance of SSAS without the complex data engineering and the need to extract and transform data to maintain traditional OLAP “cubes.” This initial implementation was done with on-premise Hadoop data. As the organization transitioned to Google BigQuery, they were able to leverage AtScale’s virtualization-based approach to seamlessly transition analytics with no interruption to the business. Within a single weekend, the data team was able to redirect existing AtScale models to the new cloud data repository, enabling existing reports, dashboards, and applications that were based on AtScale to continue operating with no changes.
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