End to end solution for ingesting and analysing IoT data in near real-time
It’s possible to build seamless device-to-cloud experience with the integrations between Azure and Confluent Cloud. The example provided in this blog post showcases how the following capabilities of Azure and Confluent Cloud are leveraged:
- A fully managed platform for data in motion and self-managed connectors from Confluent
- A secure connection from Confluent to Azure
- A highly distributed and scalable data management solution from Azure
- A limitless analytics service offered by Azure that brings together data integration, enterprise data warehousing, and big data analytics
- A fully managed microservice development platform on Azure
The following diagram provides high-level architecture of the end-to-end solution:
You can check out the blog post in its entirety here — https://www.confluent.io/blog/real-time-iot-data-integration-and-analytics-with-confluent-cloud-and-azure/
Title says it all! 👆
This blog post provides a quick overview and demo of how you can use the Azure Cosmos DB Linux Emulator on Docker (in preview at the time of writing) along with Visual Studio Code in order to enhance your local development experience.
Access your Kubernetes Deployment over the Internet
Say you have a web service running as a Kubernetes Deployment. There are a bunch of ways to access it over a public URL, but Kubexpose makes it easy to do so. It’s a Kubernetes Operator backed by a Custom Resource Definition and the corresponding controller implementation.
With practical examples, learn how to leverage integration between these services for processing data with Apache Spark
Azure Data Explorer is a fully managed data analytics service that can handle large volumes of diverse data from any data source, such as websites, applications, IoT devices, and more. Azure Data Explorer makes it simple to ingest this data and enables you to do complex ad hoc queries on…
Use Azure Key Vault for secure access from Spark pools in Azure Synapse
The Apache Spark connector for Azure SQL Database (and SQL Server) enables these databases to be used as input data sources and output data sinks for Apache Spark jobs. …
A local dev environment for learning Kafka and Cosmos DB — no cost involved!
Having a local development environment is quite handy when trying out a new service or technology. Docker has emerged as the de-facto choice in such cases. It is especially useful in scenarios where you’re trying to integrate multiple services and gives you the ability to start fresh before each run.
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In the final part, build a Serverless Go app for monitoring the Tweets processor app
Welcome to this series of blog posts that 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. …
A practical example of how to use Kafka, Redis and Spring on Azure to process Time Series data
RedisTimeSeries is a Redis Module that brings native Time Series data structure to Redis. Time Series solutions which were earlier built on top of Sorted Sets (or Redis Streams) can benefit from RedisTimeSeries features such as high volume inserts, low latency reads, flexible query language, down-sampling and much more!
Generally…
Java app to process tweets from Redis Streams
Welcome to this series of blog posts that 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. …
Build a Rust app to consume from the Twitter Streaming API
Welcome to this series of blog posts that 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. …
