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In-Stream Big Data Processing

Highly Scalable

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. It is clear that distributed in-stream data processing has something to do with query processing in distributed relational databases. Basics of Distributed Query Processing.

Big Data 154
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Fast key-value stores: an idea whose time has come and gone

The Morning Paper

Coupled with stateless application servers to execute business logic and a database-like system to provide persistent storage, they form a core component of popular data center service archictectures. We’ve seen similar high marshalling overheads in big data systems too.) Fetching too much data in a single query (i.e.,

Cache 79
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Expanding the Cloud: Introducing Amazon QuickSight

All Things Distributed

However, the data infrastructure to collect, store and process data is geared toward developers (e.g., In AWS’ quest to enable the best data storage options for engineers, we have built several innovative database solutions like Amazon RDS, Amazon RDS for Aurora, Amazon DynamoDB, and Amazon Redshift. Big data challenges.

Cloud 137