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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.

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Percentiles don’t work: Analyzing the distribution of response times for web services

Adrian Cockcroft

The mean and percentile measurements hide this structure, but the rest of this post will show how the structure can be measured and analyzed so that you can figure out a useful model of your system, understand what is driving the long tail of latencies and come up with better SLAs and measures of capacity.

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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. It is advantageous in the cloud to shut down compute resources when they are not being used, but there is then a query latency cost. The design space.

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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.

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What is serverless computing? Driving efficiency without sacrificing observability

Dynatrace

REST APIs, authentication, databases, email, and video processing all have a home on serverless platforms. AWS Lambda functions are an example of how a serverless framework works: Developers write a function in a supported language or platform. When an application is triggered, it can cause latency as the application starts.

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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.

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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.

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