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Strata Singapore 2017 business use case section "Big Telco Real-Time Network Analytics" https://conferences.oreilly.com/strata/strata-sg/public/schedule/detail/62797
"Real-Time Analytics with Spark and MemSQL" by Neil Dahlke, engineer at MemSQL for Orange County Roadshow March 17, 2017.
Snowflake is a cloud-based data warehouse that runs entirely on cloud infrastructure like AWS or Azure. It uses a shared-disk and shared-nothing architecture. Data is stored in an optimized columnar format in cloud storage. Queries are executed using virtual warehouses that are independent compute clusters. Snowflake provides a SQL interface and connectors to load, query, and analyze data without having to manage any hardware or software.
This document provides an agenda and summary for a Data Analytics Meetup (DAM) on March 27, 2018. The agenda covers topics such as disruption opportunities in a changing data landscape, transitioning from traditional to modern BI architectures using Azure, Azure SQL Database vs Data Warehouse, data integration with Azure Data Factory and SSIS, Analysis Services, Power BI reporting, and a wrap-up. The document discusses challenges around data growth, digital transformation, and the shrinking time for companies to adapt to disruption. It provides overviews and comparisons of Azure SQL Database, Data Warehouse, and related Azure services to help modernize analytics architectures.
This document discusses MS SQL Server 2019's capabilities for big data processing through PolyBase and Big Data Clusters. PolyBase allows SQL queries to join data stored externally in sources like HDFS, Oracle and MongoDB. Big Data Clusters deploy SQL Server on Linux in Kubernetes containers with separate control, compute and storage planes to provide scalable analytics on large datasets. Examples of using these technologies include data virtualization across sources, building data lakes in HDFS, distributed data marts for analysis, and integrated AI/ML tasks on HDFS and SQL data.