Democratize data via both SQL and MDX access while reducing query times with intelligent acceleration structures.
Simplify data management for availability, usability, and application integration.
Leverage existing data sources’ security models while adding additional logical and physical controls to maximize security and governance.
Extend BI, AI and Machine Learning initiatives to include structured and semi-structured data within lakes and RDBMS platforms.
Companies with siloed data face challenges providing all the data needed for business insights to data analysts.
Data analysts don’t know where to find trusted data and build their own local data stores in an ad hoc fashion which can create problems with out-of-date extracts.
Data analysts building their own local data stores in an ad hoc fashion may violate security policies and privacy regulations.
A dataset with billions of records may take days to query, making it difficult to preform data analysis and ability to explore data.
Huge datasets are expensive and slow to query.
Distributed, dynamic and diverse Big Data is difficult to consolidate AI or machine learning.
AtScale’s Adaptive Analytics (A3) Fabric virtualizes data sources and saves data models that can be published for other users to find helps customers eliminate stale and inaccurate copies of data, provide a central point to find and work with data, and promote a culture of self-service data analysis.
AtScale applies machine learning to develop optimized acceleration structures that will return query results with atomic-level granularity quickly.
AtScale’s focus on easy, reliable access to data through virtualization and its virtual OLAP cube design center makes AtScale the best company to rationalize a company’s data access methods and provide expanded access to data models.
AtScale is built for business people and data architects looking to leverage their enterprise’s data efficiently.
Provide convenient, centralized access to consolidated company data and fast query results to data analysts.
Reduce the challenges caused by fragmented data, huge datasets, and slow data repositories by virtualizing data and accelerating queries.
Protect data by limiting direct access to data and providing a secure and governed online workspace where analysts can work with data.
A Fortune 50 retailer was using Microsoft SSAS cubes on Teradata, which was unscalable from both a cost and performance perspective. Analysts could view just 1 month of data at a time when they wanted to review ranges of 2.5 years. Analysts were also unable to benchmark stores in the same region or benchmark different regions against each other. Compounding the problem further, cube rebuilds took four days, and analysts could not use data during this manual process.
First, the retailer implemented Hadoop to replace Teradata and AtScale’s Adaptive Analytics (A3) Fabric to replace Microsoft SSAS. However, while moving to Teradata, the retailer decided to move to the cloud instead managing a large Hadoop cluster. The retailer was able to seamlessly switch from Hadoop to Google BigQuery by repointing their AtScale virtual cubes in a single weekend without disrupting business users.
Now that they are using A3, analysts can look at data in their preferred data range of 2.5 years, enabling deeper insights on key business questions. They can benchmark stores and regions against one another. Data refreshes in 20 minutes, meaning that analysts are able to work with current data as opposed to information that could be a week old.
AtScale A3 is delivering over 17,000 queries per day, in under 1 second, to 4,000 business users.
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