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Waking Up The World of Big Data

Sisense

All these devices funnel more and more bits of data into warehouses and lakes the world over and that data is bought, sold, shared, sliced, diced, and drilled into to reveal a wide array of insights (it also gets totally ignored until someone figures out what to do with it). What’s Next?

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How OLAP and AI can enable better business

IBM Big Data Hub

Initially, they were designed for handling large volumes of multidimensional data, enabling businesses to perform complex analytical tasks, such as drill-down , roll-up and slice-and-dice. Early OLAP systems were separate, specialized databases with unique data storage structures and query languages.

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The Data Journey: From Raw Data to Insights

Sisense

The trend has been towards using cloud-based applications and tools for different functions, such as Salesforce for sales, Marketo for marketing automation, and large-scale data storage like AWS or data lakes such as Amazon S3 , Hadoop and Microsoft Azure. Sisense provides instant access to your cloud data warehouses.

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Accelerating revenue growth with real-time analytics: Poshmark’s journey

AWS Big Data

The data from the Kinesis data stream is consumed by two applications: A Spark streaming application on Amazon EMR is used to write data from the Kinesis data stream to a data lake hosted on Amazon Simple Storage Service (Amazon S3) in a partitioned way.