TechPrufer
Get started
Confluent
Confluent logoConfluent · Feature trail

Hybrid and Multicloud Architecture Strategy

18 posts · Nov 2016 – Aug 2026

What this trail covers

Confluent Cloud is now accessible through major cloud provider marketplaces (AWS, Azure, Google Cloud), enabling unified billing, utilization of pre-committed cloud spend, and a consistent experience across different cloud environments. This simplifies procurement and adoption for enterprises and emphasizes the portability offered by Confluent Cloud's presence across multiple clouds. Confluent Platform has also been validated on AWS. This post further elaborates on designing for continuous availability and adaptability in hybrid and multicloud environments by emphasizing portable interfaces, clear service boundaries, and separation of concerns, moving beyond simple disaster recovery to true resilience.

All Confluent trails
  1. New in Confluent Cloud and WarpStream: Evolving the Data Streaming Platform for AI, Scale, and ControlThis post details Confluent Cloud's expanded availability through AWS, Azure, and Google Cloud marketplaces, enabling unified billing and utilization of pre-committed cloud spend. It highlights the consistent experience across cloud environments and the portability offered by multi-cloud presence. The post also mentions the validation of Confluent Platform on AWS.
  2. Future-Proof Your Architectures for Hybrid and Multicloud EnvironmentsThis post elaborates on designing for continuous availability and adaptability in hybrid and multicloud environments. It emphasizes moving beyond disaster recovery to true resilience by focusing on portable interfaces, clear service boundaries, and separation of concerns. It contrasts hybrid and multicloud strategies, detailing their tradeoffs and ideal use cases, and highlights common pitfalls in cloud architectures such as vendor-specific service contamination and environment-specific configurations. The post also discusses data gravity and the challenges of consistent data management in distributed systems.
  3. Secure Cross-Cloud Data Replication with ConfluentIntroduces secure cross-cloud data replication over private networks using Confluent Cluster Linking. This feature enables the creation of a global private streaming mesh by replicating data between Confluent Cloud clusters across AWS, Azure, and Google Cloud, even when clusters are in private networks. It leverages Cluster Linking's offset-preserving replication and supports various networking patterns (private-to-private, private-to-public, public-to-private). The post details a demo scenario involving replication between AWS and Azure Enterprise clusters, highlighting the use of Terraform for infrastructure provisioning and the Confluent CLI for creating cluster links and mirrored topics. It also emphasizes the security benefits, including IP filtering and mTLS authentication, and discusses use cases like data sharing, disaster recovery, aggregated analytics, and data residency.
  4. Confluent Cloud Q3 '25 Unlocks Cost-Effective Data StreamingIntroduces Private Network Interface (PNI) for Confluent Cloud Enterprise and Freight clusters on AWS, leveraging AWS Elastic Network Interfaces (ENIs) for secure, cost-effective private networking. PNI allows direct attachment of ENIs within customer VPCs, enabling the application of existing security groups and reducing networking costs by 20-50% at scale compared to PrivateLink. Throughput prices are reduced for clusters using PNI (20% for Enterprise, 40% for Freight). Also announces increased client connection limits for Enterprise and Freight clusters (18,000 total connections per eCKU, 500 connection attempts per second per eCKU), which are automatically applied.
  5. Introducing Private Network Interface (PNI) on AWS: Lower Kafka Networking Costs by 50%Introduces Private Network Interface (PNI) on AWS as a new networking option for Confluent Cloud, available on Freight and Enterprise clusters. PNI leverages AWS Elastic Network Interfaces (ENI) for secure, cost-effective private connectivity directly within customer VPCs. It offers centralized security policy enforcement via Security Groups, eliminates PrivateLink endpoint charges, and significantly reduces data transfer costs. For Enterprise clusters with PNI, throughput costs are reduced by 20% ($0.04/GB), and for Freight clusters, they are reduced by 40% ($0.03/GB). The post quantifies savings for a logging workload, showing up to 50% reduction compared to Enterprise clusters with PrivateLink. It highlights how PNI eliminates cross-AZ networking costs through zone-aligned architecture and follower fetching, and reduces data transfer and processing costs.
  7. Telco Use Cases With Streaming Data Mesh at DISH WirelessThis post details how DISH Wireless is building a programmable 5G network using Confluent Cloud and data mesh principles. It highlights the technical implementation of a data mesh on a cloud-native data streaming platform, focusing on decentralizing data ownership, federated governance, and democratizing data access through telco APIs. The post explains how streaming data pipelines are used to ingest, process, and share massive volumes of network data in real time, enabling event-driven architectures and connecting various systems. It also discusses the benefits of Kora, Confluent Cloud's Kafka engine, and the advantages of a cloud-native approach for flexibility, performance, and reliability. Specific use cases in retail, manufacturing, and automotive are outlined, demonstrating the application of streaming data for Industry 4.0, including real-time analytics, machine learning, and edge computing. The post also mentions the use of Cluster Linking for data replication and integration with AWS services like Wavelength and Sagemaker.
  8. Modern Data Management for Hybrid and Multi-Cloud ArchitecturesThis post details the integration of Treehouse Software's tcVISION mainframe data connector with Confluent Cloud/Platform. It explains how tcVISION enables real-time synchronization from various mainframe data sources (DB2 z/OS, VSAM, IMS/DB, etc.) to Confluent, facilitating migration to modern data sinks like DynamoDB, Cosmos DB, and Snowflake. The architecture described replaces point-to-point integrations with a unified, real-time data plane, enabling centralized data pipeline oversight and governance. It highlights the use of Confluent as a central nervous system for business-critical data propagation and the ability to manage data in motion under a simplified hybrid and multi-cloud architecture.
  9. Simplify Cloud Data Warehouse Migrations with Confluent's Modern Data SolutionsThis post details how Confluent's hybrid and multi-cloud data streaming solutions can simplify cloud data warehouse migrations. It highlights the ability to create a bridge across clouds (Azure, AWS, Google Cloud) and on-prem environments to move data immediately, reducing TCO and time to value for data pipelines powering real-time ETL. It also emphasizes unlocking next-gen event streaming capabilities at cloud scale and connecting to any app, data warehouse, or data store. Specific examples are provided for Azure (Synapse and Databricks) and Google Cloud (BigQuery), detailing connector usage and reference architectures. It also mentions Amazon Redshift.
  10. Multi-Cloud Streaming Data Integration with Confluent and KrakeThis post explores the integration of Confluent Platform with Krake, an open-source workload scheduler, to enable flexible workload placement and data transparency across distributed and heterogeneous datacenters. It highlights how this combination addresses legal, technical, and energy limitations of traditional centralized cloud computing by allowing applications to be run on datacenters that fit their specific requirements. The post details how Confluent Platform's global capabilities (Multi-Region Clusters, Cluster Linking) create a globally distributed event streaming fabric, while Krake orchestrates containerized and virtualized workloads based on user requirements and platform characteristics, optimizing for energy efficiency and cost. It introduces two workload types: 'customer-bound' (stateless services) and 'resource-bound' (batch processing like ML training), and discusses a third type, 'distributed streaming application,' which leverages variable cloud computing costs and energy prices for flexible workload placement across regions.
  11. Modernize Apps and Infrastructure with Anthos, Confluent, and KafkaThis post details the integration of Confluent Platform and Confluent Cloud with Google Anthos to enable hybrid and multi-cloud application modernization. It explains how Anthos provides a consistent infrastructure layer across environments, while Confluent unifies data infrastructure on top of it, creating a hybrid data plane with Cluster Linking. The post also discusses refactoring applications into microservices using event-driven architectures and Kafka, and explores edge scenarios where Kafka can be deployed for local aggregation, processing, and resilient data ingestion, citing a manufacturing plant example.
  12. Confluent Platform is Now Certified Ready on AWS OutpostsThis post details the validation of Confluent Platform on AWS Outposts, a hybrid cloud solution. It outlines the connectivity requirements for Outposts instances, the process of building a custom Ubuntu AMI with necessary tools (Ansible, awscli, boto3, CloudFormation helper scripts), and updating the CloudFormation template for deployment. It also describes the deployment process using CloudFormation and Ansible, and provides steps for verifying the cluster deployment, including creating a topic and producing messages.
  13. Deploy Event-Driven Architectures Everywhere Using Azure & Confluent CloudThis post demonstrates a hybrid cloud architecture strategy by detailing how to build a persistent bridge from on-premises or other clouds to Microsoft Azure using Confluent Replicator. It illustrates integrating with Azure services like Azure Data Lake Storage (ADLS) and Power BI using Confluent's prebuilt connectors. The post outlines a four-phase deployment for an order processing system, starting with an on-premises MySQL database, streaming changes to Confluent Platform via Debezium MySQL CDC Connector, replicating data to Confluent Cloud in Azure using Confluent Replicator, and finally connecting Confluent Cloud to Azure services like ADLS using a sink connector.
  14. Confluent Cloud for Apache Kafka Available EverywhereThis post announces the availability of Confluent Cloud on AWS, Azure, and Google Cloud marketplaces. It details the benefits of this multi-cloud strategy, including unified billing, utilization of pre-committed cloud spend, simplified procurement, and enhanced portability. The mechanics of purchasing through marketplaces are explained, along with the different pricing models (Pay As You Go and Commitment). Future integrations with cloud providers for unified identity, resource management, and monitoring are also mentioned.
  15. Apache Kafka as a Service with Confluent Cloud on GCP MarketplaceThis post announces the availability of Confluent Cloud on Google Cloud Platform (GCP) Marketplace, enabling unified billing and a streamlined developer experience. It details the steps for purchasing and enabling Confluent Cloud within the GCP console, leveraging existing GCP billing accounts and credits. The post highlights the serverless nature of Confluent Cloud, its elastic scaling, and pay-as-you-stream pricing model, making Kafka a first-class citizen on GCP.
  16. Introducing Confluent Cloud - Managed Kafka on AzureThis post announces the availability of Confluent Cloud on Microsoft Azure, expanding its multi-cloud presence alongside AWS and GCP. It highlights the benefits for Azure developers, including seamless integration with Azure services, cloud-native capabilities for event-driven applications, and consumption-based pricing. The post also features a customer testimonial from Viewpoint Construction Software, detailing their use of Confluent Cloud on Azure for real-time application development and data aggregation from legacy systems.
  17. CloudBank's Journey from Mainframe to Streaming with Confluent Cloud | ConfluentThis post details CloudBank's journey from mainframe to a multi-cloud core banking software (Genesis) using Confluent Cloud. It highlights the challenges of data synchronization in traditional banking systems and the benefits of adopting Apache Kafka as a central data backbone for real-time processing and a single source of truth. The adoption of Confluent Cloud enabled them to achieve this, supporting a multi-cloud strategy with abstractions like Terraform, Docker, and Kubernetes. The post emphasizes the shift from batch processing to event streaming for near real-time semantics and the operational challenges of self-managing Kafka in the cloud, which Confluent Cloud addressed.
  18. Data Streaming in the Clouds: Where to Start | ConfluentThis post introduces Apache Kafka as a key enabler for hybrid, cloud-only, and multi-cloud strategies, highlighting its role in building streaming data pipelines and bridging to the cloud. It presents Confluent Replicator as a solution for replicating data across datacenters and public clouds for disaster recovery and migration, contrasting it with open-source MirrorMaker and detailing its enterprise features. The post also outlines a hub-and-spoke architecture for hybrid cloud migration using Kafka and discusses the benefits of a fully managed Kafka service like Confluent Cloud for simplifying cloud migrations.
  19. Introducing Self-Service Apache Kafka for DevelopersThis post announces the public availability of self-service Confluent Cloud and its expansion to Google Cloud Platform. It details the developer challenge of operating Kafka infrastructure and introduces Confluent Cloud as a fully managed, low-cost, self-service, Kafka-based event streaming platform. Key features include self-service provisioning, elastic scaling, world-wide availability, compatibility with open-source Kafka APIs, consumption-based pricing, and cloud independence. The post illustrates how developers can leverage Confluent Cloud on GCP with services like BigQuery and TensorFlow, and describes how Confluent Replicator can keep GCP clusters in sync with other cloud providers.
  20. Confluent Enterprise Available in Microsoft Azure Marketplace | ConfluentThis post announces the integration of Confluent Platform into Microsoft's Azure Marketplace, allowing for one-click deployment of Confluent Platform clusters. It highlights key benefits for Azure users: continuous availability leveraging Kafka's distributed architecture and Azure's reliability, linear scalability of server and client layers across Azure VM instance types, and extensive Kafka Connectors for integration with Azure data services and other systems. It also directs users to a white paper for more detailed configuration options.