Alternatives to Axual

Amazon Managed Streaming for Apache Kafka (Amazon MSK) simplifies the process of creating and operating applications that leverage Apache Kafka for handling streaming data. As an open-source framework, Apache Kafka enables the construction of real-time data pipelines and applications. Utilizing Amazon MSK allows you to harness the native APIs of Apache Kafka for various tasks, such as populating data lakes, facilitating data exchange between databases, and fueling machine learning and analytical solutions. However, managing Apache Kafka clusters independently can be quite complex, requiring tasks like server provisioning, manual configuration, and handling server failures. Additionally, you must orchestrate updates and patches, design the cluster to ensure high availability, secure and durably store data, establish monitoring systems, and strategically plan for scaling to accommodate fluctuating workloads. By utilizing Amazon MSK, you can alleviate many of these burdens and focus more on developing your applications rather than managing the underlying infrastructure.

Data integration for hybrid clouds Modern, reliable data integration across both your private cloud and public cloud. All this in real-time, with change data capture and streams. Striim was developed by the executive and technical team at GoldenGate Software. They have decades of experience in mission critical enterprise workloads. Striim can be deployed in your environment as a distributed platform or in the cloud. Your team can easily adjust the scaleability of Striim. Striim is fully secured with HIPAA compliance and GDPR compliance. Built from the ground up to support modern enterprise workloads, whether they are hosted in the cloud or on-premise. Drag and drop to create data flows among your sources and targets. Real-time SQL queries allow you to process, enrich, and analyze streaming data.

Apache Kafka® is a robust, open-source platform designed for distributed streaming. It can scale production environments to accommodate up to a thousand brokers, handling trillions of messages daily and managing petabytes of data with hundreds of thousands of partitions. The system allows for elastic growth and reduction of both storage and processing capabilities. Furthermore, it enables efficient cluster expansion across availability zones or facilitates the interconnection of distinct clusters across various geographic locations. Users can process event streams through features such as joins, aggregations, filters, transformations, and more, all while utilizing event-time and exactly-once processing guarantees. Kafka's built-in Connect interface seamlessly integrates with a wide range of event sources and sinks, including Postgres, JMS, Elasticsearch, AWS S3, among others. Additionally, developers can read, write, and manipulate event streams using a diverse selection of programming languages, enhancing the platform's versatility and accessibility. This extensive support for various integrations and programming environments makes Kafka a powerful tool for modern data architectures.

Event Hubs provides a fully managed service for real-time data ingestion that is easy to use, reliable, and highly scalable. It enables the streaming of millions of events every second from various sources, facilitating the creation of dynamic data pipelines that allow businesses to quickly address challenges. In times of crisis, you can continue data processing thanks to its geo-disaster recovery and geo-replication capabilities. Additionally, it integrates effortlessly with other Azure services, enabling users to derive valuable insights. Existing Apache Kafka clients can communicate with Event Hubs without requiring code alterations, offering a managed Kafka experience while eliminating the need to maintain individual clusters. Users can enjoy both real-time data ingestion and microbatching on the same stream, allowing them to concentrate on gaining insights rather than managing infrastructure. By leveraging Event Hubs, organizations can rapidly construct real-time big data pipelines and swiftly tackle business issues as they arise, enhancing their operational efficiency.

WarpStream serves as a data streaming platform that is fully compatible with Apache Kafka, leveraging object storage to eliminate inter-AZ networking expenses and disk management, while offering infinite scalability within your VPC. The deployment of WarpStream occurs through a stateless, auto-scaling agent binary, which operates without the need for local disk management. This innovative approach allows agents to stream data directly to and from object storage, bypassing local disk buffering and avoiding any data tiering challenges. Users can instantly create new “virtual clusters” through our control plane, accommodating various environments, teams, or projects without the hassle of dedicated infrastructure. With its seamless protocol compatibility with Apache Kafka, WarpStream allows you to continue using your preferred tools and software without any need for application rewrites or proprietary SDKs. By simply updating the URL in your Kafka client library, you can begin streaming immediately, ensuring that you never have to compromise between reliability and cost-effectiveness again. Additionally, this flexibility fosters an environment where innovation can thrive without the constraints of traditional infrastructure.

Achieve limitless data retention for Apache Kafka® with Confluent, empowering you to be infrastructure-enabled rather than constrained by outdated systems. Traditional technologies often force a choice between real-time processing and scalability, but event streaming allows you to harness both advantages simultaneously, paving the way for innovation and success. Have you ever considered how your rideshare application effortlessly analyzes vast datasets from various sources to provide real-time estimated arrival times? Or how your credit card provider monitors millions of transactions worldwide, promptly alerting users to potential fraud? The key to these capabilities lies in event streaming. Transition to microservices and facilitate your hybrid approach with a reliable connection to the cloud. Eliminate silos to ensure compliance and enjoy continuous, real-time event delivery. The possibilities truly are limitless, and the potential for growth is unprecedented.

IBM Event Streams is a comprehensive event streaming service based on Apache Kafka, aimed at assisting businesses in managing and reacting to real-time data flows. It offers features such as machine learning integration, high availability, and secure deployment in the cloud, empowering organizations to develop smart applications that respond to events in real time. The platform is designed to accommodate multi-cloud infrastructures, disaster recovery options, and geo-replication, making it particularly suitable for critical operational tasks. By facilitating the construction and scaling of real-time, event-driven solutions, IBM Event Streams ensures that data is processed with speed and efficiency, ultimately enhancing business agility and responsiveness. As a result, organizations can harness the power of real-time data to drive innovation and improve decision-making processes.

Experience Apache Kafka offered as a fully managed service that avoids vendor lock-in while providing comprehensive features for constructing your streaming pipeline. You can establish a fully managed Kafka instance in under 10 minutes using our intuitive web console or programmatically through our API, CLI, Terraform provider, or Kubernetes operator. Seamlessly integrate it with your current technology infrastructure using more than 30 available connectors, and rest assured with comprehensive logs and metrics that come standard through our service integrations. This fully managed distributed data streaming platform can be deployed in any cloud environment of your choice. It’s perfectly suited for applications that rely on event-driven architectures, facilitating near-real-time data transfers and pipelines, stream analytics, and any situation where swift data movement between applications is essential. With Aiven’s hosted and expertly managed Apache Kafka, you can effortlessly set up clusters, add new nodes, transition between cloud environments, and update existing versions with just a single click, all while keeping an eye on performance through a user-friendly dashboard. Additionally, this service enables businesses to scale their data solutions efficiently as their needs evolve.

Solace is a specialist in Event-Driven-Architecture (EDA), with two decades of experience providing enterprises with highly reliable, robust and scalable data movement technology based on the publish & subscribe (pub/sub) pattern. Solace technology enables the real-time data flow behind many of the conveniences you take for granted every day such as immediate loyalty rewards from your credit card, the weather data delivered to your mobile phone, real-time airplane movements on the ground and in the air, and timely inventory updates to some of your favourite department stores and grocery chains, not to mention that Solace technology also powers many of the world's leading stock exchanges and betting houses. Aside from rock solid technology, stellar customer support is one of the biggest reasons customers select Solace, and stick with them.

The Streaming service is a real-time, serverless platform for event streaming that is compatible with Apache Kafka, designed specifically for developers and data scientists. It is seamlessly integrated with Oracle Cloud Infrastructure (OCI), Database, GoldenGate, and Integration Cloud. Furthermore, the service offers ready-made integrations with numerous third-party products spanning various categories, including DevOps, databases, big data, and SaaS applications. Data engineers can effortlessly establish and manage extensive big data pipelines. Oracle takes care of all aspects of infrastructure and platform management for event streaming, which encompasses provisioning, scaling, and applying security updates. Additionally, by utilizing consumer groups, Streaming effectively manages state for thousands of consumers, making it easier for developers to create applications that can scale efficiently. This comprehensive approach not only streamlines the development process but also enhances overall operational efficiency.

Red Hat® OpenShift® Streams for Apache Kafka is a cloud-managed service designed to enhance the developer experience for creating, deploying, and scaling cloud-native applications, as well as for modernizing legacy systems. This service simplifies the processes of creating, discovering, and connecting to real-time data streams, regardless of their deployment location. Streams play a crucial role in the development of event-driven applications and data analytics solutions. By enabling seamless operations across distributed microservices and handling large data transfer volumes with ease, it allows teams to leverage their strengths, accelerate their time to value, and reduce operational expenses. Additionally, OpenShift Streams for Apache Kafka features a robust Kafka ecosystem and is part of a broader suite of cloud services within the Red Hat OpenShift product family, empowering users to develop a diverse array of data-driven applications. With its powerful capabilities, this service ultimately supports organizations in navigating the complexities of modern software development.

DeltaStream is an integrated serverless streaming processing platform that integrates seamlessly with streaming storage services. Imagine it as a compute layer on top your streaming storage. It offers streaming databases and streaming analytics along with other features to provide an integrated platform for managing, processing, securing and sharing streaming data. DeltaStream has a SQL-based interface that allows you to easily create stream processing apps such as streaming pipelines. It uses Apache Flink, a pluggable stream processing engine. DeltaStream is much more than a query-processing layer on top Kafka or Kinesis. It brings relational databases concepts to the world of data streaming, including namespacing, role-based access control, and enables you to securely access and process your streaming data, regardless of where it is stored.

Empower individuals to explore and analyze streaming data effectively. By sharing, documenting, and organizing your data, you can boost productivity by as much as 95%. Once you have your data, you can create applications tailored for real-world use cases. Implement a security model focused on data to address the vulnerabilities associated with open source technologies, ensuring data privacy is prioritized. Additionally, offer secure and low-code data pipeline functionalities that enhance usability. Illuminate all hidden aspects and provide unmatched visibility into data and applications. Integrate your data mesh and technological assets, ensuring you can confidently utilize open-source solutions in production environments. Lenses has been recognized as the premier product for real-time stream analytics, based on independent third-party evaluations. With insights gathered from our community and countless hours of engineering, we have developed features that allow you to concentrate on what generates value from your real-time data. Moreover, you can deploy and operate SQL-based real-time applications seamlessly over any Kafka Connect or Kubernetes infrastructure, including AWS EKS, making it easier than ever to harness the power of your data. By doing so, you will not only streamline operations but also unlock new opportunities for innovation.

We developed Conduktor, a comprehensive and user-friendly interface designed to engage with the Apache Kafka ecosystem seamlessly. Manage and develop Apache Kafka with assurance using Conduktor DevTools, your all-in-one desktop client tailored for Apache Kafka, which helps streamline workflows for your entire team. Learning and utilizing Apache Kafka can be quite challenging, but as enthusiasts of Kafka, we have crafted Conduktor to deliver an exceptional user experience that resonates with developers. Beyond merely providing an interface, Conduktor empowers you and your teams to take command of your entire data pipeline through our integrations with various technologies associated with Apache Kafka. With Conduktor, you gain access to the most complete toolkit available for working with Apache Kafka, ensuring that your data management processes are efficient and effective. This means you can focus more on innovation while we handle the complexities of your data workflows.

StreamNative transforms the landscape of streaming infrastructure by combining Kafka, MQ, and various other protocols into one cohesive platform, which offers unmatched flexibility and efficiency tailored for contemporary data processing requirements. This integrated solution caters to the varied demands of streaming and messaging within microservices architectures. By delivering a holistic and intelligent approach to both messaging and streaming, StreamNative equips organizations with the tools to effectively manage the challenges and scalability of today’s complex data environment. Furthermore, Apache Pulsar’s distinctive architecture separates the message serving component from the message storage segment, creating a robust cloud-native data-streaming platform. This architecture is designed to be both scalable and elastic, allowing for quick adjustments to fluctuating event traffic and evolving business needs, and it can scale up to accommodate millions of topics, ensuring that computation and storage remain decoupled for optimal performance. Ultimately, this innovative design positions StreamNative as a leader in addressing the multifaceted requirements of modern data streaming.

We know how simple Apache Kafka®, can be when you have the right tools. kPow was created to simplify the Kafka development experience and save businesses time and money. kPow makes it easy to find the root cause of production problems in a matter of clicks and not hours. With kPow's Data Inspect and kREPL functions, you can search tens of thousands messages per second. Are you new to Kafka kPow's Kafka UI is unique and allows developers to quickly understand the core Kafka concepts. You can upskill new members of your team and increase your Kafka knowledge. kPow offers a range of Kafka management features and monitoring capabilities in a single Docker Container. You can manage multiple clusters and schema registries. Connect installs with one instance.

Google Cloud's Managed Service for Apache Kafka is an efficient and scalable solution that streamlines the deployment, oversight, and upkeep of Apache Kafka clusters. By automating essential operational functions like provisioning, scaling, and patching, it allows developers to concentrate on application development without the burdens of managing infrastructure. The service guarantees high reliability and availability through data replication across various zones, thus mitigating the risks of potential outages. Additionally, it integrates effortlessly with other Google Cloud offerings, enabling the creation of comprehensive data processing workflows. Security measures are robust, featuring encryption for both stored and transmitted data, along with identity and access management, and network isolation to keep information secure. Users can choose between public and private networking setups, allowing for diverse connectivity options that cater to different requirements. This flexibility ensures that businesses can adapt the service to meet their specific operational needs efficiently.

Introducing revolutionary data streaming features that enable unparalleled customer experiences. The Kafka API and its ecosystem are fully compatible with Redpanda, which boasts predictable low latencies and ensures zero data loss. Redpanda is designed to outperform Kafka by up to ten times, offering enterprise-level support and timely hotfixes. It also includes automated backups to S3 or GCS, providing a complete escape from the routine operations associated with Kafka. Additionally, it supports both AWS and GCP environments, making it a versatile choice for various cloud platforms. Built from the ground up for ease of installation, Redpanda allows for rapid deployment of streaming services. Once you witness its incredible capabilities, you can confidently utilize its advanced features in a production setting. We take care of provisioning, monitoring, and upgrades without requiring access to your cloud credentials, ensuring that sensitive data remains within your environment. Your streaming infrastructure will be provisioned, operated, and maintained seamlessly, with customizable instance types available to suit your specific needs. As your requirements evolve, expanding your cluster is straightforward and efficient, allowing for sustainable growth.

Engaging applications captivate users while motivating developers to innovate. To meet the growing demands of the digital landscape, consider utilizing the DataStax Astra Streaming service platform. This cloud-native platform for messaging and event streaming is built on the robust foundation of Apache Pulsar. With Astra Streaming, developers can create streaming applications that leverage a multi-cloud, elastically scalable architecture. Powered by the advanced capabilities of Apache Pulsar, this platform offers a comprehensive solution that encompasses streaming, queuing, pub/sub, and stream processing. Astra Streaming serves as an ideal partner for Astra DB, enabling current users to construct real-time data pipelines seamlessly connected to their Astra DB instances. Additionally, the platform's flexibility allows for deployment across major public cloud providers, including AWS, GCP, and Azure, thereby preventing vendor lock-in. Ultimately, Astra Streaming empowers developers to harness the full potential of their data in real-time environments.

TIBCO Streaming is an advanced analytics platform focused on real-time processing and analysis of fast-moving data streams, which empowers organizations to make swift, data-informed choices. With its low-code development environment found in StreamBase Studio, users can create intricate event processing applications with ease and minimal coding requirements. The platform boasts compatibility with over 150 connectors, such as APIs, Apache Kafka, MQTT, RabbitMQ, and databases like MySQL and JDBC, ensuring smooth integration with diverse data sources. Incorporating dynamic learning operators, TIBCO Streaming allows for the use of adaptive machine learning models that deliver contextual insights and enhance automation in decision-making. Additionally, it provides robust real-time business intelligence features that enable users to visualize current data alongside historical datasets for a thorough analysis. The platform is also designed for cloud readiness, offering deployment options across AWS, Azure, GCP, and on-premises setups, thereby ensuring flexibility for various organizational needs. Overall, TIBCO Streaming stands out as a powerful solution for businesses aiming to harness real-time data for strategic advantages.

Data processing that integrates both streaming and batch operations while being serverless, efficient, and budget-friendly. It offers a fully managed service for data processing, ensuring seamless automation in the provisioning and administration of resources. With horizontal autoscaling capabilities, worker resources can be adjusted dynamically to enhance overall resource efficiency. The innovation is driven by the open-source community, particularly through the Apache Beam SDK. This platform guarantees reliable and consistent processing with exactly-once semantics. Dataflow accelerates the development of streaming data pipelines, significantly reducing data latency in the process. By adopting a serverless model, teams can devote their efforts to programming rather than the complexities of managing server clusters, effectively eliminating the operational burdens typically associated with data engineering tasks. Additionally, Dataflow’s automated resource management not only minimizes latency but also optimizes utilization, ensuring that teams can operate with maximum efficiency. Furthermore, this approach promotes a collaborative environment where developers can focus on building robust applications without the distraction of underlying infrastructure concerns.

Superstream: An AI Solution That Lowers Expenses and Boosts Kafka Performance by 75%, With Zero Modifications to Your Current Infrastructure.

Xeotek accelerates the development and exploration of data applications and streams for businesses through its robust desktop and web applications. The Xeotek KaDeck platform is crafted to cater to the needs of developers, operations teams, and business users equally. By providing a shared platform for business users, developers, and operations, KaDeck fosters a collaborative environment that minimizes misunderstandings, reduces the need for revisions, and enhances overall transparency for the entire team. With Xeotek KaDeck, you gain authoritative control over your data streams, allowing for significant time savings by obtaining insights at both the data and application levels during projects or routine tasks. Easily export, filter, transform, and manage your data streams in KaDeck, simplifying complex processes. The platform empowers users to execute JavaScript (NodeV4) code, create and modify test data, monitor and adjust consumer offsets, and oversee their streams or topics, along with Kafka Connect instances, schema registries, and access control lists, all from a single, user-friendly interface. This comprehensive approach not only streamlines workflow but also enhances productivity across various teams and projects.

Materialize is an innovative reactive database designed to provide updates to views incrementally. It empowers developers to seamlessly work with streaming data through the use of standard SQL. One of the key advantages of Materialize is its ability to connect directly to a variety of external data sources without the need for pre-processing. Users can link to real-time streaming sources such as Kafka, Postgres databases, and change data capture (CDC), as well as access historical data from files or S3. The platform enables users to execute queries, perform joins, and transform various data sources using standard SQL, presenting the outcomes as incrementally-updated Materialized views. As new data is ingested, queries remain active and are continuously refreshed, allowing developers to create data visualizations or real-time applications with ease. Moreover, constructing applications that utilize streaming data becomes a straightforward task, often requiring just a few lines of SQL code, which significantly enhances productivity. With Materialize, developers can focus on building innovative solutions rather than getting bogged down in complex data management tasks.

ScaleOut Digital Twin Streaming Service™ allows for the seamless creation and deployment of real-time digital twins for advanced streaming analytics. With the ability to connect to numerous data sources such as Azure and AWS IoT hubs, Kafka, and others, it enhances situational awareness through live, aggregate analytics. This innovative cloud service is capable of tracking telemetry from millions of data sources simultaneously, offering immediate and in-depth insights with state-tracking and focused real-time feedback for a multitude of devices. The user-friendly interface streamlines deployment and showcases aggregate analytics in real time, which is essential for maximizing situational awareness. It is suitable for a diverse array of applications, including the Internet of Things (IoT), real-time monitoring, logistics, and financial services. The straightforward pricing structure facilitates a quick and easy start. When paired with the ScaleOut Digital Twin Builder software toolkit, the ScaleOut Digital Twin Streaming Service paves the way for the next generation of stream processing, empowering users to leverage data like never before. This combination not only enhances operational efficiency but also opens new avenues for innovation across various sectors.

Informatica's AI-driven Data Engineering Streaming empowers data engineers to efficiently ingest, process, and analyze real-time streaming data, offering valuable insights. The advanced serverless deployment feature, coupled with an integrated metering dashboard, significantly reduces administrative burdens. With CLAIRE®-enhanced automation, users can swiftly construct intelligent data pipelines that include features like automatic change data capture (CDC). This platform allows for the ingestion of thousands of databases, millions of files, and various streaming events. It effectively manages databases, files, and streaming data for both real-time data replication and streaming analytics, ensuring a seamless flow of information. Additionally, it aids in the discovery and inventorying of all data assets within an organization, enabling users to intelligently prepare reliable data for sophisticated analytics and AI/ML initiatives. By streamlining these processes, organizations can harness the full potential of their data assets more effectively than ever before.

The significance of streaming data derived from operations, transactions, sensors, and IoT devices becomes apparent when it is thoroughly comprehended. SAS's event stream processing offers a comprehensive solution that encompasses streaming data quality, analytics, and an extensive selection of SAS and open source machine learning techniques alongside high-frequency analytics. This integrated approach facilitates the connection, interpretation, cleansing, and comprehension of streaming data seamlessly. Regardless of the velocity at which your data flows, the volume of data you manage, or the diversity of data sources you utilize, you can oversee everything effortlessly through a single, user-friendly interface. Moreover, by defining patterns and addressing various scenarios across your entire organization, you can remain adaptable and proactively resolve challenges as they emerge while enhancing your overall operational efficiency.

Create advanced applications that streamline workflows, provide ongoing operational monitoring, and manage processes in real-time. Your solutions are designed to operate continuously on cloud servers as well as edge devices. Utilizing a familiar spreadsheet interface, you don't need to be a programmer; instead, you can simply drag and drop data, enter formulas into cells, and create charts in an intuitive manner. All the essential protocols required for connecting to sensors and machinery, such as MQTT, REST, and OPC UA, are readily available. Streamsheets specializes in processing streaming data, including formats like MQTT and Kafka. You can select a topic stream, modify it as needed, and send it back into the vast world of streaming data. With REST, you gain access to a multitude of web services, while Streamsheets enables seamless connections both ways. Not only do Streamsheets operate in the cloud and on your servers, but they can also be deployed on edge devices, including Raspberry Pi, expanding their versatility to various environments. This flexibility allows businesses to adapt their systems according to their specific operational needs.

Waterstream transforms your Kafka-compatible system into a robust MQTT broker, enabling seamless connections for millions of clients without the need for coding, integration pipelines, or extra storage requirements. It establishes a bidirectional interface that bridges Kafka with MQTT clients, eliminating the hassles of overseeing external MQTT clusters and reducing data redundancy. With Waterstream, scalability is straightforward, as its nodes operate independently for most tasks, allowing you to easily add more instances to accommodate a growing client base. The solution relies solely on Kafka, reaping the advantages of its built-in persistence features, which ensure high availability, impressive throughput, and minimal latency for optimal performance. Additionally, this means that you can focus on your applications while Waterstream efficiently manages the complexities of data streaming.

Nussknacker allows domain experts to use a visual tool that is low-code to help them create and execute real-time decisioning algorithm instead of writing code. It is used to perform real-time actions on data: real-time marketing and fraud detection, Internet of Things customer 360, Machine Learning inferring, and Internet of Things customer 360. A visual design tool for decision algorithm is an essential part of Nussknacker. It allows non-technical users, such as analysts or business people, to define decision logic in a clear, concise, and easy-to-follow manner. With a click, scenarios can be deployed for execution once they have been created. They can be modified and redeployed whenever there is a need. Nussknacker supports streaming and request-response processing modes. It uses Kafka as its primary interface in streaming mode. It supports both stateful processing and stateless processing.

IBM® StreamSets allows users to create and maintain smart streaming data pipelines using an intuitive graphical user interface. This facilitates seamless data integration in hybrid and multicloud environments. IBM StreamSets is used by leading global companies to support millions data pipelines, for modern analytics and intelligent applications. Reduce data staleness, and enable real-time information at scale. Handle millions of records across thousands of pipelines in seconds. Drag-and-drop processors that automatically detect and adapt to data drift will protect your data pipelines against unexpected changes and shifts. Create streaming pipelines for ingesting structured, semistructured, or unstructured data to deliver it to multiple destinations.

SiteWhere utilizes Kubernetes for deploying its infrastructure and microservices, making it versatile for both on-premise setups and virtually any cloud service provider. The system is supported by robust configurations of Apache Kafka, Zookeeper, and Hashicorp Consul, ensuring a reliable infrastructure. Each microservice is designed to scale individually while also enabling seamless integration with others. It presents a comprehensive multitenant IoT ecosystem that encompasses device management, event ingestion, extensive event storage capabilities, REST APIs, data integration, and additional features. The architecture is distributed and developed using Java microservices that operate on Docker, with an Apache Kafka processing pipeline for efficiency. Importantly, SiteWhere CE remains open source, allowing free use for both personal and commercial purposes. Additionally, the SiteWhere team provides free basic support along with a continuous flow of innovative features to enhance the platform's functionality. This emphasis on community-driven development ensures that users can benefit from ongoing improvements and updates.

IBM Event Automation is an entirely flexible, event-driven platform that empowers users to identify opportunities, take immediate action, automate their decision-making processes, and enhance their revenue capabilities. By utilizing Apache Flink, it allows organizations to react swiftly in real time, harnessing artificial intelligence to forecast essential business trends. This solution supports the creation of scalable applications that can adapt to changing business requirements and manage growing workloads effortlessly. It also provides self-service capabilities, accompanied by approval mechanisms, field redaction, and schema filtering, all governed by a Kafka-native event gateway through policy administration. IBM Event Automation streamlines and speeds up event management by implementing policy administration for self-service access, which facilitates the definition of controls for approval workflows, field-level redaction, and schema filtering. Various applications of this technology include analyzing transaction data, optimizing inventory levels, identifying suspicious activities, improving customer insights, and enabling predictive maintenance. This comprehensive approach ensures that businesses can navigate complex environments with agility and precision.

Samza enables the development of stateful applications that can handle real-time data processing from various origins, such as Apache Kafka. Proven to perform effectively at scale, it offers versatile deployment choices, allowing execution on YARN or as an independent library. With the capability to deliver remarkably low latencies and high throughput, Samza provides instantaneous data analysis. It can manage multiple terabytes of state through features like incremental checkpoints and host-affinity, ensuring efficient data handling. Additionally, Samza's operational simplicity is enhanced by its deployment flexibility—whether on YARN, Kubernetes, or in standalone mode. Users can leverage the same codebase to seamlessly process both batch and streaming data, which streamlines development efforts. Furthermore, Samza integrates with a wide range of data sources, including Kafka, HDFS, AWS Kinesis, Azure Event Hubs, key-value stores, and ElasticSearch, making it a highly adaptable tool for modern data processing needs.

Transform your customer service interactions and conversational AI applications by utilizing the open-source Airy platform. Airy Core stands out as a fully-featured conversational platform that is ready for production use. With Airy, you are equipped to handle conversational data sourced from multiple channels effortlessly. Powered by Apache Kafka, Airy's infrastructure allows for the simultaneous processing of numerous conversations and messages, efficiently streaming pertinent data wherever needed. You can easily integrate a variety of tools, from our complimentary open-source live chat plugin to popular messaging services like Facebook Messenger and Google’s Business Messages, all connected to your Airy Core. This seamless integration is made possible through an ingestion platform that leverages Apache Kafka to manage incoming webhook data from diverse sources. By effectively interpreting this data, we transform it into contacts, conversations, and messages that work independently of their origins, enhancing the versatility and capability of your conversational engagements. Ultimately, Airy empowers you to create a cohesive communication strategy across different platforms.

Effortlessly gather, manage, and scrutinize video and data streams as they occur. Amazon Kinesis simplifies the process of collecting, processing, and analyzing streaming data in real-time, empowering you to gain insights promptly and respond swiftly to emerging information. It provides essential features that allow for cost-effective processing of streaming data at any scale while offering the adaptability to select the tools that best align with your application's needs. With Amazon Kinesis, you can capture real-time data like video, audio, application logs, website clickstreams, and IoT telemetry, facilitating machine learning, analytics, and various other applications. This service allows you to handle and analyze incoming data instantaneously, eliminating the need to wait for all data to be collected before starting the processing. Moreover, Amazon Kinesis allows for the ingestion, buffering, and real-time processing of streaming data, enabling you to extract insights in a matter of seconds or minutes, significantly reducing the time it takes compared to traditional methods. Overall, this capability revolutionizes how businesses can respond to data-driven opportunities as they arise.

EMR serves as a comprehensive enterprise-grade big data platform, offering cluster, job, and data management functionalities that leverage various open-source technologies, including Hadoop, Spark, Kafka, Flink, and Storm. Alibaba Cloud Elastic MapReduce (EMR) is specifically designed for big data processing within the Alibaba Cloud ecosystem. Built on Alibaba Cloud's ECS instances, EMR integrates the capabilities of open-source Apache Hadoop and Apache Spark. This platform enables users to utilize components from the Hadoop and Spark ecosystems, such as Apache Hive, Apache Kafka, Flink, Druid, and TensorFlow, for effective data analysis and processing. Users can seamlessly process data stored across multiple Alibaba Cloud storage solutions, including Object Storage Service (OSS), Log Service (SLS), and Relational Database Service (RDS). EMR also simplifies cluster creation, allowing users to establish clusters rapidly without the hassle of hardware and software configuration. Additionally, all maintenance tasks can be managed efficiently through its user-friendly web interface, making it accessible for various users regardless of their technical expertise.

Equalum offers a unique continuous data integration and streaming platform that seamlessly accommodates real-time, batch, and ETL scenarios within a single, cohesive interface that requires no coding at all. Transition to real-time capabilities with an intuitive, fully orchestrated drag-and-drop user interface designed for ease of use. Enjoy the benefits of swift deployment, powerful data transformations, and scalable streaming data pipelines, all achievable in just minutes. With a multi-modal and robust change data capture (CDC) system, it enables efficient real-time streaming and data replication across various sources. Its design is optimized for exceptional performance regardless of the data origin, providing the advantages of open-source big data frameworks without the usual complexities. By leveraging the scalability inherent in open-source data technologies like Apache Spark and Kafka, Equalum's platform engine significantly enhances the efficiency of both streaming and batch data operations. This cutting-edge infrastructure empowers organizations to handle larger data volumes while enhancing performance and reducing the impact on their systems, ultimately facilitating better decision-making and quicker insights. Embrace the future of data integration with a solution that not only meets current demands but also adapts to evolving data challenges.

TIBCO provides robust solutions designed to fulfill your requirements for performance, throughput, reliability, and scalability, while also offering diverse technology and deployment alternatives to ensure real-time data accessibility in critical areas. The TIBCO Platform integrates a continuously developing array of your TIBCO solutions, regardless of their hosting environment—be it cloud-based, on-premises, or at the edge—into a cohesive, single experience that simplifies management and monitoring. By doing so, TIBCO supports the creation of solutions vital for the success of major enterprises around the globe, enabling them to thrive in a competitive landscape. This commitment to innovation positions TIBCO as a key player in the digital transformation journey of businesses.

Modern distributed messaging platforms like Kafka and Pulsar have established a robust Pub/Sub framework suitable for the demands of contemporary data-rich applications. Pravega takes this widely accepted programming model a step further by offering a cloud-native streaming infrastructure that broadens its applicability across various use cases. With features that ensure streams are durable, consistent, and elastic, Pravega also offers native support for long-term data retention. It addresses architectural challenges that earlier topic-centric systems such as Kafka and Pulsar have struggled with, including the automatic scaling of partitions and maintaining optimal performance despite a high volume of partitions. Additionally, Pravega expands the types of applications it can support by adeptly managing both small-scale events typical in IoT and larger data sets relevant to video processing and analytics. Beyond merely providing stream abstractions, Pravega facilitates the replication of application states and the storage of key-value pairs, making it a versatile choice for developers. This flexibility empowers users to create more complex and resilient data architectures tailored to their specific needs.

Cloudera DataFlow for the Public Cloud (CDF-PC) is a versatile, cloud-based data distribution solution that utilizes Apache NiFi, enabling developers to seamlessly connect to diverse data sources with varying structures, process that data, and deliver it to a wide array of destinations. This platform features a flow-oriented low-code development approach that closely matches the preferences of developers when creating, developing, and testing their data distribution pipelines. CDF-PC boasts an extensive library of over 400 connectors and processors that cater to a broad spectrum of hybrid cloud services, including data lakes, lakehouses, cloud warehouses, and on-premises sources, ensuring efficient and flexible data distribution. Furthermore, the data flows created can be version-controlled within a catalog, allowing operators to easily manage deployments across different runtimes, thereby enhancing operational efficiency and simplifying the deployment process. Ultimately, CDF-PC empowers organizations to harness their data effectively, promoting innovation and agility in data management.

The Baidu Messaging System (BMS) serves as a scalable and distributed message queue service renowned for its impressive throughput capabilities. It gathers extensive data from various sources such as websites, devices, and applications, allowing for immediate analysis of activities like user browsing behavior, clicks, and search queries. Built on the foundation of Apache Kafka, BMS leverages this robust, distributed, multi-partitioned, and multi-replica messaging platform. Producers and consumers communicate asynchronously through the message queue, enabling them to operate independently without waiting for one another. Unlike conventional messaging services, BMS simplifies the complexities of managing a Kafka cluster by offering it as a hosted solution. This means users can seamlessly integrate BMS with widely distributed applications without needing to manage cluster operations, paying only for what they use. Consequently, BMS provides an efficient and user-friendly approach to handling messaging needs in modern distributed systems.

Aiven takes the reins on your open-source data infrastructure hosted in the cloud, allowing you to focus on what you excel at: developing applications. While you channel your energy into innovation, we expertly handle the complexities of managing cloud data infrastructure. Our solutions are entirely open source, providing the flexibility to transfer data between various clouds or establish multi-cloud setups. You will have complete visibility into your expenses, with a clear understanding of costs as we consolidate networking, storage, and basic support fees. Our dedication to ensuring your Aiven software remains operational is unwavering; should any challenges arise, you can count on us to resolve them promptly. You can launch a service on the Aiven platform in just 10 minutes and sign up without needing to provide credit card information. Simply select your desired open-source service along with the cloud and region for deployment, pick a suitable plan—which includes $300 in free credits—and hit "Create service" to begin configuring your data sources. Enjoy the benefits of maintaining control over your data while leveraging robust open-source services tailored to your needs. With Aiven, you can streamline your cloud operations and focus on driving your projects forward.

One Platform for Realtime Communication: A platform to build and operate real-time interactivity for web, mobile, AI/ML, IoT, and Edge computing applications Faster & Easier Deployments: SDK support for 50+ mobile, web, server, and IoT environments (PubNub & community supported) and more than 65 pre-built integrations with external and third-party APIs to give you the features you need regardless of programming language or tech stack. Scalability: The industry’s most scalable platform capable of supporting millions of concurrent users for rapid growth with low latency, high uptime, and without financial penalties.

A streaming database is specifically designed to efficiently ingest, store, process, and analyze large volumes of data streams. This advanced data infrastructure integrates messaging, stream processing, and storage to enable real-time value extraction from your data. It continuously handles vast amounts of data generated by diverse sources, including sensors from IoT devices. Data streams are securely stored in a dedicated distributed streaming data storage cluster that can manage millions of streams. By subscribing to topics in HStreamDB, users can access and consume data streams in real-time at speeds comparable to Kafka. The system also allows for permanent storage of data streams, enabling users to replay and analyze them whenever needed. With a familiar SQL syntax, you can process these data streams based on event-time, similar to querying data in a traditional relational database. This functionality enables users to filter, transform, aggregate, and even join multiple streams seamlessly, enhancing the overall data analysis experience. Ultimately, the integration of these features ensures that organizations can leverage their data effectively and make timely decisions.