New Product Streamlines and Optimizes Spatial Analysis for Big Data Workflows
Globally, organizations across industries are using big data to improve operations, manage supply chains, and enhance customer service experiences so that they can gain competitive advantage and boost profits. Spatial analysis of big data provides critical intelligence about where, when, and why things happen—intelligence that can’t be obtained from other types of data analysis. To ensure that these insights are accessible in the decision-making process, Esri, the global leader in location intelligence, has released ArcGIS GeoAnalytics Engine.
“Industries, from insurance and financial services to defense and intelligence, must increasingly use big data to drive decision-making”
This new product provides an intuitive, cloud-of-choice capability that works within existing analysis workflows without requiring additional technology or training. Data scientists can perform spatial analysis wherever their data is stored—in a data lake, a data warehouse, or ArcGIS software—saving the time and cost of moving volumes of data out of cloud environments. The product is a comprehensive library of spatial analytics that is native to Spark, a unified analytics engine for big data and machine learning.
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“Industries, from insurance and financial services to defense and intelligence, must increasingly use big data to drive decision-making,” said Lauren Bennett, head of spatial analysis and data science at Esri. “The more data an organization is analyzing, the more critical it is to make real-world sense of it. Geospatial analytics uses location as a connective thread to detect patterns, uncover hidden relationships, and improve predictive modeling.”
Data scientists face several obstacles incorporating spatial analysis into big data analysis workflows. They often lack specialized knowledge and must utilize multiple disconnected packages, and big data processing environments are not built to support spatial analytics.
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ArcGIS GeoAnalytics Engine allows data scientists to bridge these operational and expertise challenges while also enabling them to quickly provide stakeholders with results. The product runs in Spark environments commonly used for big data analysis. This includes Databricks, Amazon EMR, and Dataproc, which are Spark environments available from major cloud providers such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud.
“The combined power of the Amazon EMR big data platform and Esri’s ArcGIS GeoAnalytics Engine allows our customers, regardless of spatial expertise, to seamlessly leverage ArcGIS GeoAnalytics Engine spatial tools and functions in their big data analysis and models,” said Abhishek Ram, head of products, worldwide public sector, at Amazon Web Services.
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