Redis 4.0 Compatibility in Amazon ElastiCache

Amazon ElastiCache makes it easy for you to set up a fully managed in-memory data store and cache with Redis or Memcached. Today we’re pleased to launch compatibility with Redis 4.0 in ElastiCache. You can now launch Redis 4.0 compatible ElastiCache nodes or clusters, in all commercial AWS regions. ElastiCache Redis clusters can scale to terabytes of memory and millions of reads / writes per second to serve the most demanding needs of games, IoT devices, financial applications, and web applications.

https://aws.amazon.com/blogs/aws/new-redis-4-0-compatibility-in-amazon-elasticache

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Machine Learning on AWS

Why machine learning on AWS?

Machine Learning for everyone

Whether you are a data scientist, ML researcher, or developer, AWS offers machine learning services and tools tailored to meet your needs and level of expertise.

API-driven ML services

Developers can easily add intelligence to any application with a diverse selection of pre-trained services that provide computer vision, speech, language analysis, and chatbot functionality.

Broad framework support

AWS supports all the major machine learning frameworks, including TensorFlow, Caffe2, and Apache MXNet, so that you can bring or develop any model you choose.

Breadth of compute options

AWS offers a broad array of compute options for training and inference with powerful GPU-based instances, compute and memory optimized instances, and even FPGAs.

Deep platform integrations

ML services are deeply integrated with the rest of the platform including the data lake and database tools you need to run ML workloads. A data lake on AWS gives you access to the most complete platform for big data.

Comprehensive analytics

Choose from a comprehensive set of services for data analysis including data warehousing, business intelligence, batch processing, stream processing, data workflow orchestration.

Secure

Control access to resources with granular permission policies. Storage and database services offer strong encryption to keep your data secure. Flexible key management options allow you to choose whether you or AWS will manage the encryption keys.

Pay-as-you-go

Consume services as you need them and only for the period you use them. AWS pricing has no upfront fees, termination penalties, or long term contracts. The AWS Free Tier helps you get started with AWS.

State of React Native 2018

It’s been a while since we last published a status update about React Native.

At Facebook, we’re using React Native more than ever and for many important projects. One of our most popular products is Marketplace, one of the top-level tabs in our app which is used by 800 million people each month. Since its creation in 2015, all of Marketplace has been built with React Native, including over a hundred full-screen views throughout different parts of the app.

https://facebook.github.io/react-native/blog/2018/06/14/state-of-react-native-2018

Why Uber Engineering Switched from Postgres to MySQL

The early architecture of Uber consisted of a monolithic backend application written in Python that used Postgres for data persistence. Since that time, the architecture of Uber has changed significantly, to a model of microservices and new data platforms. Specifically, in many of the cases where we previously used Postgres, we now use Schemaless, a novel database sharding layer built on top of MySQL. In this article, we’ll explore some of the drawbacks we found with Postgres and explain the decision to build Schemaless and other backend services on top of MySQL.

 

The Architecture of Postgres

We encountered many Postgres limitations:

  • Inefficient architecture for writes
  • Inefficient data replication
  • Issues with table corruption
  • Poor replica MVCC support
  • Difficulty upgrading to newer releases

https://eng.uber.com/mysql-migration/

 

AWS Workshop for Kubernetes

This is a self-paced workshop designed for Development and Operations teams who would like to leverage Kubernetes on Amazon Web Services (AWS).

This workshop provides instructions to create, manage, and scale a Kubernetes cluster on AWS, as well as how to deploy applications, scale them, run stateless and stateful containers, perform service discovery between different microservices, and other similar concepts.

It also shows deep integration with several AWS technologies.

We recommend at least 2 hours to complete the workshop.