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What is AWS Lambda?

Dynatrace

The 2014 launch of AWS Lambda marked a milestone in how organizations use cloud services to deliver their applications more efficiently, by running functions at the edge of the cloud without the cost and operational overhead of on-premises servers. What is AWS Lambda? Where does Lambda fit in the AWS ecosystem? Dynatrace news.

Lambda 179
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Cloudburst: stateful functions-as-a-service

The Morning Paper

The key ingredients of Cloudburst are a highly-scalable key-value store for persistent state ( Anna ), local caches co-located with function execution environments, and cache-consistency protocols to preserve developer sanity while data is moved in and out of those caches. Updates should be allowed at any function invocation site.

Lambda 98
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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. The network latency of fetching data over the network, even considering fast data center networks. Who knew! ;).

Cache 79
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Choosing a cloud DBMS: architectures and tradeoffs

The Morning Paper

Which I’m quite happy to see as my most recent data pipeline is based around Lambda, S3, and Athena, and it’s been working great for my use case. The experimental results focus on six main areas of comparison: query restrictions system initialisation time query performance cost data compatibility with other systems scalability.

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Embrace event-driven computing: Amazon expands DynamoDB with streams, cross-region replication, and database triggers

All Things Distributed

I am excited to share with you that today we are expanding DynamoDB with streams, cross-region replication, and database triggers. In traditional database architectures, database engines often run a small search engine or data warehouse engines on the same hardware as the database. Let me expand on each one of them.

Database 167
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In-Stream Big Data Processing

Highly Scalable

This article is an effort to explore techniques used by developers of in-stream data processing systems, trace the connections of these techniques to massive batch processing and OLTP/OLAP databases, and discuss how one unified query engine can support in-stream, batch, and OLAP processing at the same time.

Big Data 154
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A one size fits all database doesn't fit anyone

All Things Distributed

A common question that I get is why do we offer so many database products? To do this, they need to be able to use multiple databases and data models within the same application. Seldom can one database fit the needs of multiple distinct use cases. Seldom can one database fit the needs of multiple distinct use cases.

Database 167