Serverless

A combination of AWS Lambda and Amazon API Gateway is a widely-used architecture for serverless microservices and API based solutions. They enable developers to focus on their applications, instead of spending time provisioning and managing servers.

API Gateway is a feature rich offering that includes with support for different API types (HTTP, REST, WebSocket), multiple authentication schemes, API versioning, canary deployments and much more! However, if your requirements are simpler and all you need is an HTTP(S) endpoint for your Lambda function (for example, to serve as a webhook), you can use Lambda Function URLs! When you create a function URL, Lambda automatically generates a unique HTTP(S) endpoint that is dedicated for your Lambda function.

Welcome to this series of blog posts which covers Redis Streams with the help of a practical example. We will use a sample application to make Twitter data available for search and query in real-time. RediSearch and Redis Streams serve as the backbone of this solution that consists of several co-operating components, each of which will we covered in a dedicated blog post.

The code is available in this GitHub repo - https://github.com/abhirockzz/redis-streams-in-action

We will continue from where we left off in the previous blog post and see how to build a monitoring app to make the overall system more robust in the face of high load or failure scenarios. This is because our very often, data processing applications either slow down (due to high data volumes) or may even crash/stop due to circumstances beyond our control. If this happens with our Tweets processing application, the messages that were assigned to a specific instance will be left unprocessed. The monitoring component covered in this blog post, checks pending Tweets (using XPENDING), claims (XCLAIM), processes (store them as HASH using HSET) and finally acknowledges them (XACK).

Welcome to this series of blog posts which covers Redis Streams with the help of a practical example. We will use a sample application to make Twitter data available for search and query in real-time. RediSearch and Redis Streams serve as the backbone of this solution that consists of several co-operating components, each of which will we covered in a dedicated blog post.

The code is available in this GitHub repo - https://github.com/abhirockzz/redis-streams-in-action

This blog post will cover a Java based Tweets processor application whose role is to pick up tweets from Redis Streams and store them (as a HASH) so that they can be queried using RediSearch (the accurate term for this is “indexing documents” in RediSearch). You will deploy the application to Azure, validate it, run a few RediSearch queries to search tweets. Finally, there is a section where we will walk through the code to understand “how things work”.

Welcome to this series of blog posts which covers Redis Streams with the help of a practical example. We will use a sample application to make Twitter data available for search and query in real-time. RediSearch and Redis Streams serve as the backbone of this solution that consists of several co-operating components, each of which will we covered in a dedicated blog post.

The code is available in this GitHub repo - https://github.com/abhirockzz/redis-streams-in-action

In this part, we look at the service which interacts with the Twitter Streaming API to consume tweets and move them on to the next part in the processing pipeline.

Welcome to this series of blog posts which covers Redis Streams with the help of a practical example. We will use a sample application to make Twitter data available for search and query in real-time. RediSearch and Redis Streams serve as the backbone of this solution that consists of several co-operating components, each of which will we covered in a dedicated blog post.

The code is available in this GitHub repo - https://github.com/abhirockzz/redis-streams-in-action

This is the first part which explores the use case, motivations and provides a high level overview of the Redis features used in the solution.

This talk covers a bunch of common Serverless use cases like Backend APIs, Real-Time systems, Integration and Automation (uses Fn and Oracle Functions as the serverless platform).

The GitHub repo for the talk contains the ppt and links to other demos (source code etc.) - https://github.com/abhirockzz/oraclecode2019

Cheers!

Image classification is a canonical example used to demonstrate machine learning techniques. This post shows you how to run a TensorFlow-based image classification application on the recently announced cloud service Oracle Functions.

Photo by Franck V. on Unsplash

Oracle Functions

Oracle Functions which is a fully managed, highly scalable, on-demand, function-as-a-service platform built on enterprise-grade Oracle Cloud Infrastructure. It’s a serverless offering that enables you to focus on writing code to meet business needs without worrying about the underlying infrastructure, and get billed only for the resources consumed during the execution. You can deploy your code and call it directly or in response to triggers — Oracle Functions does all the work required to ensure that your application is highly available, scalable, secure, and monitored.