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Teradata Unified Data Architecture
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Native graph processing engine that makes it easy to perform powerful graph analysis with speed across big data sets
Gain an Unfair Competitive Advantage using powerful insights from Graph AnalysisDiscover high impact insights by easily combining Graph Analysis with other techniques such as Text and Statistical Analysis through an integrated solution optimized for multiple analytics on all data. Large scale graph processing with best price performanceEasily apply unmatched power and speed to perform complex graph analysis at big data scale. Achieve fastest time to value with ready-to-use Teradata Aster Big Analytics Appliance or use commodity hardware in an MPP architecture.Bring Graph Analytics to existing SQL audienceEmpower existing resources with SQL skills to discover insights from Graph Analysis with minimal effort. Business analysts can easily invoke pre-built graph functions in a single SQL statement to perform graph analysis, without having to learn any specialized programming skills or write cumbersome code.
Powerful Graph Analytics with ease
Integrated with SQL and MapReduce
Aster SQL-GR™ is a native graph processing engine for Graph Analysis that makes it easy to solve complex business problems such as social network/influencer analysis, fraud detection, supply chain management, network analysis and threat detection, and money laundering that are more impactful than simple graph navigation analysis. SQL-GR is based on the Bulk Synchronous Processing (BSP) model and uses massively iterative, distributed & parallel processing to solve complex graph problems.
SQL-GR is massively scalable as it is based on the BSP iterative processing model and takes advantage of Teradata Aster’s massively scalable parallel processing (MPP) architecture to distribute the graph processing across multiple servers/nodes. SQL-GR is not bound by memory limits or to a single server/node.
SQL-GR graph analytic engine integrates tightly with the SQL and SQL-MapReduce engines in Aster Discovery Platform and can be invoked through a single SQL interface. This empowers business analysts, data analysts and data scientists to discover high impact insights by easily combining Graph Analysis with other techniques such as Text and Statistical Analysis. Pre-built Graph functions in Aster Discovery Platform based on the SQL-GR engine which can be invoked from a single SQL interface reduce the complexity of big data analytics. In addition, APIs enable users to develop custom Graph Analysis functions based on the SQL-GR engine and run them in the Aster Discovery Platform.
Increasing upsell and cross sell of products drives direct impact on the bottom line and profitability of Retail companies. Using Aster Discovery Platform, a retailer can discover bridge products that act as a bridge between different categories of products and pull through sales of high margin products. For example, a retailer has high sales of salad and salad dressing across its customers. The retailer also sells another category of high margin products of wine and gourmet meat. By performing Graph Analysis using the Aster Discovery Platform on massive data sets of customer shopping behavior and purchases, the retailer identifies that olive and cheese act as bridge products between the separate categories of salads/dressings and wine/gourmet meat. If someone buying salad also buys olive and cheese, that customer will also end up purchasing the high margin wine and gourmet meat from a separate category of products than salad. Hence, a retailer can recoup such lost sales by strategically placing offers on bridge products and grow profitability.
This Ovum white paper introduces Connection Analytics, an emerging discipline that provides answers to persistent business questions based on causal relationships between nodes.
Learn how graph discovery enables analysts to perform out-of-the-box functions for graph-parallel analysis from the comfort of SQL by augmenting content with context.
Teradata Connection Analytics and Teradata Aster Graph Engine
Use big data analytics to retain customers.
Use Graph Analytics for relationship analysis and to identify influential customers.
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