Hybrid and Multicloud Architecture Strategy

2/19/2026

This post defines and advocates for a hybrid and multicloud architecture strategy focused on continuous availability and adaptability. It outlines key requirements for future-proofing, such as separation of concerns, clear service/data boundaries, and portable interfaces. The post contrasts hybrid and multicloud approaches, details common pitfalls like proprietary API contamination and hidden dependencies, and discusses data gravity challenges in distributed systems. It positions event streaming and Apache Kafka as foundational for decoupling systems in these environments.

10/3/2023

This 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.

1/24/2023

This post details the integration of tcVISION, a mainframe data connector, with Confluent Platform/Cloud. It highlights the use of tcVISION for real-time synchronization of data from mainframe sources (IBM DB2 z/OS, DB2 z/VSE, VSAM, IMS/DB, CA IDMS, CA DATACOM, Software AG ADABAS) to Confluent, enabling data movement to target platforms on AWS, Azure, and Google Cloud. The architecture described uses Confluent as a central data hub for aggregating data from multiple sources into Kafka topics, facilitating hybrid and multicloud data management and avoiding complex point-to-point integrations. It also mentions the use of Confluent for overseeing data pipelines and managing policy and governance.

10/11/2021

This post details Confluent's solution for modernizing data warehouses in hybrid and multi-cloud environments. It emphasizes building real-time ETL pipelines using Kafka and ksqlDB for data preprocessing, reducing TCO and time to value. The solution leverages Confluent's extensive connector ecosystem to integrate with various data sources and cloud data warehouses like Snowflake, Databricks, BigQuery, Redshift, and Synapse. It highlights the performance benefits of Kafka for high throughput and low latency, and introduces Cluster Linking as a mechanism to bridge on-premises Kafka clusters with Confluent Cloud. Specific reference architectures for Azure (Synapse, Databricks) and Google Cloud (BigQuery) are provided, showcasing the integration of Confluent Cloud with these platforms.

10/20/2020

This post details the integration of Confluent Platform with Krake, an open-source multi-cluster workload scheduler, to enable flexible workload placement and improve data processing efficiency in multi-cloud and decentralized datacenter environments. It explains how Confluent Platform provides a global, distributed event streaming fabric as the data plane, while Krake orchestrates applications as the application plane. The post discusses overcoming limitations of traditional cloud computing (legal, technical, energy) through a decentralized approach and highlights the synergy of combining Confluent's global capabilities (Multi-Region Clusters, Cluster Linking) with Krake's scheduling for optimized energy utilization, cost savings, and data sovereignty. It also defines customer-bound and resource-bound workloads in the context of cloud-native applications and distributed streaming.

9/23/2020

This 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.

9/15/2020

This post details the validation of Confluent Platform on AWS Outposts, enabling on-premises deployment of event-driven applications with a consistent hybrid experience. It covers connectivity requirements, custom AMI building, CloudFormation template updates, and verification steps for deploying Confluent Platform on AWS Outposts, extending hybrid cloud capabilities.

9/3/2020

This post details how to build a persistent bridge from on-premises or other clouds to Microsoft Azure using Confluent Replicator. It illustrates the use of Confluent's prebuilt connectors to integrate with Azure services such as Azure Data Lake Storage (ADLS) and Power BI. The post outlines a four-phase deployment process: setting up an order processing application and datastores, streaming database changes to Confluent Platform using the Debezium MySQL CDC Connector, establishing the Bridge to Azure architecture with Confluent Replicator, and connecting Confluent Cloud to Azure services. It provides specific examples of MySQL database tables and the configuration for the Debezium connector.

8/5/2020

This post details the availability of Confluent Cloud on AWS, Microsoft Azure, and Google Cloud marketplaces. It explains the benefits of this integration, including simplified procurement through existing billing accounts and enterprise agreements, unified billing, and enhanced portability across cloud providers. The post also outlines the purchasing mechanisms (Pay As You Go and Commitment) and the sign-up process through these marketplaces.

2/18/2020

This post details the integration of Confluent Cloud with Azure Marketplace, enabling unified billing and procurement for Azure customers. It explains how developers can leverage their existing Azure credits and billing services to consume Confluent Cloud, simplifying the adoption process and reducing the friction of managing separate bills. The post also highlights how Confluent Cloud complements Azure services like Event Hubs and Stream Analytics, offering a richer development model for event streaming applications.

1/7/2020

This post details the integration of Confluent Cloud with Google Cloud Platform (GCP) Marketplace. It explains how this integration allows users to leverage their existing GCP billing and credits for Confluent Cloud consumption, simplifying procurement and management by consolidating billing into a single GCP bill. The post provides a step-by-step guide for users to discover, purchase, and enable Confluent Cloud through the GCP Marketplace, highlighting the benefits of unified billing and a seamless developer experience.

11/6/2019

This 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 focusing on application development, leveraging cloud-native capabilities, and integrating with Azure services. A customer testimonial from Viewpoint Construction Software is included, detailing their use of Confluent Cloud on Azure for real-time applications and data aggregation from legacy systems. The post reiterates key Confluent Cloud features like managed Kafka, Schema Registry, ksqlDB, elastic scaling, and consumption-based pricing, emphasizing its role as a fully managed event streaming platform.

4/9/2019

This post announces the availability of Confluent Cloud as a native service on Google Cloud Platform (GCP). It details the integration with Google Cloud Console and GCP Marketplace for a seamless sign-up experience, along with integrated billing and first-line support provided by Google Cloud. The post highlights the benefits of this partnership for customers seeking cloud-native, open-source data systems and positions it as a model for collaboration between open-source companies and cloud platforms.

3/4/2019

This 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.

11/20/2018

This post details how Apache Kafka can be leveraged for hybrid, cloud-only, and multi-cloud strategies, addressing challenges in migrating existing systems to the cloud and architecting for data movement across multiple applications and clouds in real-time. It introduces Confluent Replicator as a solution for replicating data across datacenters and public clouds, illustrating its use in hybrid, multi-cloud, and cloud-only environments. The post contrasts Confluent Replicator with open-source MirrorMaker, highlighting enterprise-critical features of Replicator. It also suggests using a hub-and-spoke implementation based on a central Kafka platform for hybrid architectures and discusses the benefits of a fully managed Kafka service like Confluent Cloud for simplifying cloud migrations.

5/22/2018

This 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.

11/21/2016

This post announces the integration of Confluent Platform into Microsoft's Azure Marketplace, enabling rapid deployment of a complete Confluent Platform cluster with a single click. It highlights key benefits for Azure users, including continuous availability, linear scalability of server and client layers, and an extensive library of Kafka Connectors for integration with Azure data services and other offerings. Customers can purchase software subscriptions directly from Confluent beyond the initial 30-day trial.