Highly Scalable

AUGUST 20, 2013

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. In the previous section, we noted that many distributed query processing algorithms resemble message passing networks. Towards Unified Big Data Processing. Pipelining.

Big Data 154

Big Data Processing Lambda Database 154

All Things Distributed

DECEMBER 8, 2016

It adopted Amazon Redshift, Amazon EMR and AWS Lambda to power its data warehouse, big data, and data science applications, supporting the development of product features at a fraction of the cost of competing solutions. Some examples of how current customers use AWS are: Cost-effective solutions.

AWS 155

AWS Cloud Lambda Innovation 155

All Things Distributed

JUNE 26, 2016

AdiMap uses Amazon Kinesis to process real-time streaming online ad data and job feeds, and processes them for storage in petabyte-scale Amazon Redshift. Advanced problem solving that connects big data with machine learning. warehouses to glean business insights for jobs, ad spend, or financials for mobile apps.

AWS 90

AWS Cloud Healthcare Blockchain 90

All Things Distributed

DECEMBER 13, 2016

With the launch of the AWS Europe (London) Region, AWS can enable many more UK enterprise, public sector and startup customers to reduce IT costs, address data locality needs, and embark on rapid transformations in critical new areas, such as big data analysis and Internet of Things.

AWS 166

AWS Cloud Artificial Intelligence IoT 166

The Morning Paper

JUNE 23, 2019

I think it’s absolutely fine to use the local memory space or filesystem as a local cache of data that spans transactions so long as that doesn’t introduce stickiness, consistency or stale data issues. We’ve seen similar high marshalling overheads in big data systems too.) Fetching too much data in a single query (i.e.,

Cache 79

Cache Latency Google Lambda 79

All Things Distributed

NOVEMBER 30, 2016

in ML and neural networks) and access to vast amounts of data. automatic speech recognition, natural language understanding, image classification), collect and clean the training data, and train and tune the machine learning models. Developers can build, test, and deploy chatbots directly from the AWS Management Console.

AWS 166

AWS Lambda Artificial Intelligence Mobile 166