Alternatives to Astra Streaming

StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, scalable upserts, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark. StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.

Amazon EventBridge serves as a serverless event bus that simplifies the integration of applications by utilizing data from your own systems, various Software-as-a-Service (SaaS) offerings, and AWS services. It provides a continuous flow of real-time data from event sources like Zendesk, Datadog, and PagerDuty, efficiently directing that information to targets such as AWS Lambda. By establishing routing rules, you can dictate the destination of your data, enabling the creation of application architectures that respond instantaneously to all incoming data sources. EventBridge facilitates the development of event-driven applications by managing essential aspects like event ingestion, delivery, security, authorization, and error handling on your behalf. As your applications grow increasingly interconnected through events, you may find that greater effort is required to discover and comprehend the structure of these events in order to effectively code responses to them. This can enhance the overall efficiency and responsiveness of your application ecosystem.

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.

EMQX is the world's most scalable and reliable MQTT messaging platform designed by EMQ. It supports 100M concurrent IoT device connections per cluster while maintaining extremely high throughput and sub-millisecond latency. EMQX boasts more than 20,000 global users from over 50 countries, connecting more than 100M IoT devices worldwide, and is trusted by over 300 customers in mission-critical IoT scenarios, including well-known brands like HPE, VMware, Verifone, SAIC Volkswagen, and Ericsson. Our edge-to-cloud IoT data solutions are flexible to meet the demands of various industries towards digital transformation, including connected vehicles, Industrial IoT, oil & gas, carrier, finance, smart energy, and smart cities. EMQX Enterprise: The World’s # 1 Scalable MQTT Messaging Platform -100M concurrent MQTT connections -1M/s messages throughput under 1ms latency -Business-critical reliability, Up to 99.99% SLA -Integrate IoT data seamlessly with over 40 cloud services and enterprise systems EMQX Cloud: Fully Managed MQTT Service for IoT - Scale as you need, pay as you go - Flexible and rich IoT data integration up to 40+ choices - Run in 19 regions across AWS, GCP, and Microsoft Azure - 100% MQTT

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.

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.

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.

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.

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.

Ably is the definitive realtime experience platform. We power more WebSocket connections than any other pub/sub platform, serving over a billion devices monthly. Businesses trust us with their critical applications like chat, notifications and broadcast - reliably, securely and at serious scale.

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.

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.

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.

Apache Pulsar is a cutting-edge, distributed platform for messaging and streaming that was initially developed at Yahoo! and has since become a prominent project under the Apache Software Foundation. It boasts straightforward deployment, a lightweight computing process, and APIs that are user-friendly, eliminating the necessity of managing your own stream processing engine. For over five years, it has been utilized in Yahoo!'s production environment, handling millions of messages each second across a vast array of topics. Designed from the outset to function as a multi-tenant system, it offers features like isolation, authentication, authorization, and quotas to ensure secure operations. Additionally, Pulsar provides configurable data replication across various geographic regions, ensuring data resilience. Its message storage relies on Apache BookKeeper, facilitating robust performance, while maintaining IO-level separation between read and write operations. Furthermore, a RESTful admin API is available for effective provisioning, administration, and monitoring tasks, enhancing operational efficiency. This combination of features makes Apache Pulsar an invaluable tool for organizations seeking scalable and reliable messaging solutions.

Axual provides a Kafka-as-a-Service tailored for DevOps teams, empowering them to extract insights and make informed decisions through our user-friendly Kafka platform. For enterprises seeking to effortlessly incorporate data streaming into their essential IT frameworks, Axual presents the perfect solution. Our comprehensive Kafka platform is crafted to remove the necessity for deep technical expertise, offering a ready-made service that allows users to enjoy the advantages of event streaming without complications. The Axual Platform serves as an all-encompassing solution, aimed at simplifying and improving the deployment, management, and use of real-time data streaming with Apache Kafka. With a robust suite of features designed to meet the varied demands of contemporary businesses, the Axual Platform empowers organizations to fully leverage the capabilities of data streaming while reducing complexity and minimizing operational burdens. Additionally, our platform ensures that your team can focus on innovation rather than getting bogged down by technical challenges.

HiveMQ is the most trusted enterprise MQTT platform, purpose-built to connect anything via MQTT, communicate reliably, and control IoT data. The platform can be deployed anywhere, on-premise or in the cloud, giving developers the flexibility and freedom they need to evolve as their IoT deployment grows. HiveMQ is reliable under real-world stress, scales without limits, and provides enterprise-grade security to meet the needs of organizations at any stage of digital transformation. The extensible platform provides seamless connectivity to the leading data streaming, databases, and data analytics platforms, plus offers a custom SDK for a perfect fit in any stack.

Scale from zero to millions of events per second effortlessly. Arroyo is delivered as a single, compact binary, allowing for local development on MacOS or Linux, and seamless deployment to production environments using Docker or Kubernetes. As a pioneering stream processing engine, Arroyo has been specifically designed to simplify real-time processing, making it more accessible than traditional batch processing. Its architecture empowers anyone with SQL knowledge to create dependable, efficient, and accurate streaming pipelines. Data scientists and engineers can independently develop comprehensive real-time applications, models, and dashboards without needing a specialized team of streaming professionals. By employing SQL, users can transform, filter, aggregate, and join data streams, all while achieving sub-second response times. Your streaming pipelines should remain stable and not trigger alerts simply because Kubernetes has chosen to reschedule your pods. Built for modern, elastic cloud infrastructures, Arroyo supports everything from straightforward container runtimes like Fargate to complex, distributed setups on Kubernetes, ensuring versatility and robust performance across various environments. This innovative approach to stream processing significantly enhances the ability to manage data flows in real-time applications.

Streamline your event-driven applications with Event Grid, a unified service that efficiently handles the routing of events from any source to any endpoint. Built for exceptional availability, reliable performance, and flexible scalability, Event Grid allows you to concentrate on your application's functionality instead of the underlying infrastructure. It removes the need for polling, thereby cutting down on both costs and delays. Utilizing a pub/sub architecture with straightforward HTTP-based event transmission, Event Grid separates event producers from consumers, enabling the creation of scalable serverless solutions, microservices, and distributed architectures. You can achieve significant scalability on demand while receiving almost instantaneous notifications about the changes that matter to you. Enhance your application development with reactive programming principles, leveraging assured event delivery and the robust uptime provided by cloud technologies. Furthermore, you can create more complex application scenarios by integrating a variety of potential event sources and destinations, enhancing the overall capability of your solutions. Ultimately, Event Grid empowers developers to innovate and respond to changing requirements swiftly and efficiently.

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.

Introducing a versatile, open-source multi-cloud platform for contemporary data applications, built on Apache Cassandra™. Achieve global-scale performance with guaranteed 100% uptime while avoiding vendor lock-in. You have the flexibility to deploy on multi-cloud environments, on-premises infrastructures, or use Kubernetes. The platform is designed to be elastic and offers a pay-as-you-go pricing model to enhance total cost of ownership. Accelerate your development process with Stargate APIs, which support NoSQL, real-time interactions, reactive programming, as well as JSON, REST, and GraphQL formats. Bypass the difficulties associated with managing numerous open-source projects and APIs that lack scalability. This solution is perfect for various sectors including e-commerce, mobile applications, AI/ML, IoT, microservices, social networking, gaming, and other highly interactive applications that require dynamic scaling based on demand. Start your journey of creating modern data applications with Astra, a database-as-a-service powered by Apache Cassandra™. Leverage REST, GraphQL, and JSON alongside your preferred full-stack framework. This platform ensures that your richly interactive applications are not only elastic but also ready to gain traction from the very first day, all while offering a cost-effective Apache Cassandra DBaaS that scales seamlessly and affordably as your needs evolve. With this innovative approach, developers can focus on building rather than managing infrastructure.

Google Cloud Pub/Sub offers a robust solution for scalable message delivery, allowing users to choose between pull and push modes. It features auto-scaling and auto-provisioning capabilities that can handle anywhere from zero to hundreds of gigabytes per second seamlessly. Each publisher and subscriber operates with independent quotas and billing, making it easier to manage costs. The platform also facilitates global message routing, which is particularly beneficial for simplifying systems that span multiple regions. High availability is effortlessly achieved through synchronous cross-zone message replication, coupled with per-message receipt tracking for dependable delivery at any scale. With no need for extensive planning, its auto-everything capabilities from the outset ensure that workloads are production-ready immediately. In addition to these features, advanced options like filtering, dead-letter delivery, and exponential backoff are incorporated without compromising scalability, which further streamlines application development. This service provides a swift and dependable method for processing small records at varying volumes, serving as a gateway for both real-time and batch data pipelines that integrate with BigQuery, data lakes, and operational databases. It can also be employed alongside ETL/ELT pipelines within Dataflow, enhancing the overall data processing experience. By leveraging its capabilities, businesses can focus more on innovation rather than infrastructure management.

Astra DB from DataStax is a real-time vector database as a service for developers that need to get accurate Generative AI applications into production, fast. Astra DB gives you a set of elegant APIs supporting multiple languages and standards, powerful data pipelines and complete ecosystem integrations. Astra DB enables you to quickly build Gen AI applications on your real-time data for more accurate AI that you can deploy in production. Built on Apache Cassandra, Astra DB is the only vector database that can make vector updates immediately available to applications and scale to the largest real-time data and streaming workloads, securely on any cloud. Astra DB offers unprecedented serverless, pay as you go pricing and the flexibility of multi-cloud and open-source. You can store up to 80GB and/or perform 20 million operations per month. Securely connect to VPC peering and private links. Manage your encryption keys with your own key management. SAML SSO secure account accessibility. You can deploy on Amazon, Google Cloud, or Microsoft Azure while still compatible with open-source Apache Cassandra.

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.

Lightstreamer acts as an event broker that is finely tuned for the internet, providing a smooth and instantaneous flow of data across online platforms. In contrast to conventional brokers, it adeptly manages the challenges posed by proxies, firewalls, disconnections, network congestion, and the inherent unpredictability of web connectivity. Its advanced streaming capabilities ensure that real-time data delivery is maintained, always finding efficient and reliable pathways for your information. Lightstreamer's technology is not only well-established but also at the cutting edge, continually adapting to remain a leader in the field of innovation. With a solid history and extensive practical experience, it guarantees dependable and effective data transmission. Users can count on Lightstreamer to provide unmatched reliability in any situation, making it an invaluable tool for real-time communication needs. In an ever-evolving digital landscape, Lightstreamer stands out as a trusted partner for delivering data seamlessly.

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.

Amazon Simple Notification Service (SNS) is a comprehensive messaging platform designed for both system-to-system and app-to-person (A2P) communications. It facilitates interaction between systems through a publish/subscribe (pub/sub) model, allowing messages to flow seamlessly between independent microservice applications or directly to users via SMS, mobile push notifications, and email. The pub/sub capabilities for system-to-system interactions support topics that enable high-throughput, push-based, many-to-many messaging. By leveraging Amazon SNS topics, your publishing systems can efficiently distribute messages to a wide array of subscriber systems or customer endpoints, including Amazon SQS queues, AWS Lambda functions, and HTTP/S, thus allowing for concurrent processing. Additionally, the A2P messaging feature empowers you to send messages to users on a large scale, utilizing either a pub/sub model or direct-publish messages through a unified API. This flexibility enhances communication strategies for businesses aiming to engage their users effectively.

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.

RabbitMQ is a lightweight solution that can be effortlessly deployed both on-premises and in cloud environments. It is compatible with various messaging protocols, making it versatile for different use cases. Furthermore, RabbitMQ can be configured in distributed and federated setups, which cater to demanding scalability and high availability needs. With a vast user base, it stands out as one of the leading open-source message brokers available today. Organizations ranging from T-Mobile to Runtastic leverage RabbitMQ, showcasing its adaptability for both startups and large enterprises. Additionally, RabbitMQ is compatible with numerous operating systems and cloud platforms, offering a comprehensive suite of development tools for popular programming languages. Users can deploy RabbitMQ using tools like Kubernetes, BOSH, Chef, Docker, and Puppet, facilitating seamless integration into their existing workflows. Developers can also create cross-language messaging solutions using their preferred programming languages, such as Java, .NET, PHP, Python, JavaScript, Ruby, and Go, enhancing its utility across various projects.

Massive amounts data can be moved as messages between services, applications and systems at any one time. If an application isn’t available or a service interruption occurs, messages and transactions may be lost or duplicated. This can cost businesses time and money. IBM has refined IBM MQ over the past 25 years. MQ allows you to hold a message in a queue until it is delivered. MQ moves data once, even file data, to avoid competitors delivering messages twice or not at the right time. MQ will never lose a message. IBM MQ can be run on your mainframe, in containers, in public or private clouds or in containers. IBM offers an IBM-managed cloud service (IBM MQ Cloud), hosted on Amazon Web Services or IBM Cloud, as well as a purpose-built Appliance (IBM MQ Appliance), to simplify deployment and maintenance.

Spark Streaming extends the capabilities of Apache Spark by integrating its language-based API for stream processing, allowing you to create streaming applications in the same manner as batch applications. This powerful tool is compatible with Java, Scala, and Python. One of its key features is the automatic recovery of lost work and operator state, such as sliding windows, without requiring additional code from the user. By leveraging the Spark framework, Spark Streaming enables the reuse of the same code for batch processes, facilitates the joining of streams with historical data, and supports ad-hoc queries on the stream's state. This makes it possible to develop robust interactive applications rather than merely focusing on analytics. Spark Streaming is an integral component of Apache Spark, benefiting from regular testing and updates with each new release of Spark. Users can deploy Spark Streaming in various environments, including Spark's standalone cluster mode and other compatible cluster resource managers, and it even offers a local mode for development purposes. For production environments, Spark Streaming ensures high availability by utilizing ZooKeeper and HDFS, providing a reliable framework for real-time data processing. This combination of features makes Spark Streaming an essential tool for developers looking to harness the power of real-time analytics efficiently.

AWS IoT Core enables seamless connectivity between IoT devices and the AWS cloud, eliminating the need for server provisioning or management. Capable of accommodating billions of devices and handling trillions of messages, it ensures reliable and secure processing and routing of communications to AWS endpoints and other devices. This service empowers applications to continuously monitor and interact with all connected devices, maintaining functionality even during offline periods. Furthermore, AWS IoT Core simplifies the integration of various AWS and Amazon services, such as AWS Lambda, Amazon Kinesis, Amazon S3, Amazon SageMaker, Amazon DynamoDB, Amazon CloudWatch, AWS CloudTrail, Amazon QuickSight, and Alexa Voice Service, facilitating the development of IoT applications that collect, process, analyze, and respond to data from connected devices without the burden of infrastructure management. By utilizing AWS IoT Core, you can effortlessly connect an unlimited number of devices to the cloud and facilitate communication among them, streamlining your IoT solutions. This capability significantly enhances the efficiency and scalability of your IoT initiatives.

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.

Red Hat® Integration offers a robust suite of integration and messaging tools designed to link applications and data within hybrid infrastructures. This solution is characterized by its agility, distribution, containerization, and API-centric design. It encompasses service composition and orchestration, connectivity for applications, data transformation, real-time message streaming, change data capture, and comprehensive API management, all within a cloud-native framework and toolset that caters to the complete range of contemporary application development needs. Users can implement enterprise integration patterns (EIPs) with over 200 pluggable connectors, effectively bridging both new and existing data sources across hybrid cloud environments. Furthermore, it enables the creation, deployment, monitoring, and governance of APIs throughout their entire lifecycle. With an emphasis on an API-first methodology, integrations can be seamlessly extended across hybrid and multi-cloud settings. Additionally, services can be developed and managed using widely accepted container standards, while lightweight containers can be efficiently packaged and deployed across distributed environments, ensuring flexibility and scalability. This comprehensive approach not only enhances operational efficiency but also accelerates time-to-market for applications.

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.

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.

Transform your data into a real-time stream, ensuring it is instantly accessible and ready for utilization. Leo simplifies the complexities of event sourcing, allowing you to effortlessly create, visualize, monitor, and sustain your data streams. By unlocking your data, you free yourself from the limitations imposed by outdated systems. The significant reduction in development time leads to higher satisfaction among both developers and stakeholders alike. Embrace microservice architectures to foster continuous innovation and enhance your agility. Ultimately, achieving success with microservices hinges on effective data management. Organizations need to build a dependable and repeatable data backbone to turn microservices into a tangible reality. You can also integrate comprehensive search functionality into your custom application, as the continuous flow of data makes managing and updating a search database a seamless task. With these advancements, your organization will be well-positioned to leverage data more effectively than ever before.

It is difficult, costly, and risky to connect systems to scale AI projects. Pandio's cloud native managed solution simplifies data pipelines to harness AI's power. You can access your data from any location at any time to query, analyze, or drive to insight. Big data analytics without the high cost Enable data movement seamlessly. Streaming, queuing, and pub-sub with unparalleled throughput, latency and durability. In less than 30 minutes, you can design, train, deploy, and test machine learning models locally. Accelerate your journey to ML and democratize it across your organization. It doesn't take months or years of disappointment. Pandio's AI driven architecture automatically orchestrates all your models, data and ML tools. Pandio can be integrated with your existing stack to help you accelerate your ML efforts. Orchestrate your messages and models across your organization.

Batch and streaming data processing can be streamlined effortlessly. With the capability to write once and run anywhere, it is ideal for mission-critical production tasks. Beam allows you to read data from a wide variety of sources, whether they are on-premises or cloud-based. It seamlessly executes your business logic across both batch and streaming scenarios. The outcomes of your data processing efforts can be written to the leading data sinks available in the market. This unified programming model simplifies operations for all members of your data and application teams. Apache Beam is designed for extensibility, with frameworks like TensorFlow Extended and Apache Hop leveraging its capabilities. You can run pipelines on various execution environments (runners), which provides flexibility and prevents vendor lock-in. The open and community-driven development model ensures that your applications can evolve and adapt to meet specific requirements. This adaptability makes Beam a powerful choice for organizations aiming to optimize their data processing strategies.

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.

Anypoint MQ enables sophisticated asynchronous messaging options like queueing and pub/sub through fully managed cloud message queues and exchanges. This service, part of the Anypoint Platform™, is designed to accommodate various environments and business groups while incorporating role-based access control (RBAC) for enhanced security and management. It offers enterprise-level features, making it suitable for diverse organizational needs and ensuring seamless communication across applications.

Streamkap is a modern streaming ETL platform built on top of Apache Kafka and Flink, designed to replace batch ETL with streaming in minutes. It enables data movement with sub-second latency using change data capture for minimal impact on source databases and real-time updates. The platform offers dozens of pre-built, no-code source connectors, automated schema drift handling, updates, data normalization, and high-performance CDC for efficient and low-impact data movement. Streaming transformations power faster, cheaper, and richer data pipelines, supporting Python and SQL transformations for common use cases like hashing, masking, aggregations, joins, and unnesting JSON. Streamkap allows users to connect data sources and move data to target destinations with an automated, reliable, and scalable data movement platform. It supports a broad range of event and database sources.

Middleware Observability & management software for Messaging, event processing, and Streaming Across Hybrid Clouds (MESH). - 360 degree situational awareness® with complete observability of Integration MESH - Manage configuration, administration and deployment in a secure manner and automate them. - Track and trace transactions, messages, and flows - Collect data, monitor performance, and benchmark it meshIQ provides granular controls for managing configurations in the MESH, reducing downtime and allowing quick recovery after outages. It allows you to search, browse, track and trace messages in order to detect bottlenecks, speed up root cause analysis, and detect bottlenecks. Unlocks integration blackbox for visibility across MESH infrastructure in order to visualize, analyse, report and predict. Delivers the capability to trigger automated action based on predefined criteria or intelligent AI/ML actions.

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.

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.