Change Data Capture (CDC) can be used to track row-level changes in database tables in response to create, update and delete operations. It is a powerful technique, but useful only when there is a way to leverage these events and make them available to other services.
Using Apache Kafka, it is possible to convert traditional batched ETL processes into real-time, streaming mode. You can do-it-yourself (DIY) and write good old Kafka producer/consumer using a client SDK of your choice. …
In this blog, we will go over how to ingest data into Azure Data Explorer using the open source Kafka Connect Sink connector for Azure Data Explorer running on Kubernetes using Strimzi. Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other systems using source and sink connectors and Strimzi provides a “Kubernetes-native” way of running Kafka clusters as well as Kafka Connect workers.
Azure Data Explorer is a fast and scalable data exploration service that lets you collect, store, and analyze large volumes of data from any diverse sources, such as websites, applications, IoT devices, and more. It has a rich connector ecosystem that supports ingestion into Azure Data Explorer as detailed here. One of the supported sources is Apache Kafka and the sink connector allows you to move data from Kafka topics into Azure Data Explorer tables which you can later query and analyse. …
Originally published here: https://devblogs.microsoft.com/cosmosdb/build-fault-tolerant-applications-cassandra/
Azure Cosmos DB is a resource governed system that allows you to execute a certain number of operations per second based on the provisioned throughput you have configured. If clients exceed that limit and consume more request units than what was provisioned, it leads to rate limiting of subsequent requests and exceptions being thrown — they are also referred to as 429 errors.
With the help of a practical example, I’ll demonstrate how to incorporate fault-tolerance in your Go applications by handling and retrying operations affected by these rate limiting errors. …
This blog provides a practical example of how to use Azure Stream Analytics to process streaming data from Azure Event Hubs. You should be able to go through this tutorial using the Azure Portal (or Azure CLI), without writing any code. There are also other resources for exploring stream processing with Azure Stream Analytics at the end of this blog post.
With traditional architectures, it’s quite hard to counter challenges imposed by real-time streaming data — one such use case is joining streams of data from disparate sources. For example, think about a system that accepts processed orders from customers (real time, high velocity data source) and the requirement is to enrich these “raw” orders with additional customer info such as name, email, location etc. A possible solution is to build a service that fetches customer data for each customer ID from an external system (for example, a database), perform a join (in-memory) and stores the enriched data in another database perhaps (materialized view). …
Azure Data Explorer is a fast and scalable data exploration service that lets you collect, store, and analyze large volumes of data from any diverse sources, such as websites, applications, IoT devices, and more. Kafka Connect platform allows you to stream data between Apache Kafka and external systems in a scalable and reliable manner. The Kafka Connect Sink connector for Azure Data Explorer allows you to move data in Kafka topics to Azure Data Explorer tables which you can later query and analyze.
Set up a Change Data Capture architecture on Azure using Debezium, Postgres and Kafka was a tutorial on how to use Debezium for change data capture from Azure PostgreSQL and send them to Azure Event Hubs for Kafka — it used the
This blog will provide a quick walk through of how to
pgoutputplugin. I will not be repeating a lot of details and use containerized versions (using Docker Compose) for Kafka connect, Kafka (and Zookeeper) to keep things simple. …
Getting started with Azure Data Explorer using the Go SDK covered how to use the Azure Data Explorer Go SDK to ingest and query data from azure data explorer to ingest and query data. In this blog you will the Azure Go SDK to manage Azure Data Explorer clusters and databases.
Azure Data Explorer (also known as Kusto) is a fast and scalable data exploration service for analyzing large volumes of diverse data from any data source, such as websites, applications, IoT devices, and more. …
Welcome to this blog series about running Kafka on Kubernetes:
So far, we have a Kafka single-node cluster with TLS encryption on top of which we configured different authentication modes (
SASL SCRAM-SHA-512), defined users with the User Operator, connected to the cluster using CLI and Go clients and saw how easy it is to manage Kafka topics with the Topic Operator. …
With the help of an example, this blog post will walk you through how to use the Azure Data explorer Go SDK to ingest data from a Azure Blob storage container and query it programmatically using the SDK. After a quick overview of how to setup Azure Data Explorer cluster (and a database), we will explore the code to understand what’s going on (and how) and finally test the application using a simple CLI interface
The sample data is a CSV file that can be downloaded from here
The code is available on GitHub https://github.com/abhirockzz/azure-dataexplorer-go
Azure Data Explorer (also known as Kusto) is a fast and scalable data exploration service for analyzing large volumes of diverse data from any data source, such as websites, applications, IoT devices, and more. This data can then be used for diagnostics, monitoring, reporting, machine learning, and additional analytics capabilities. …