Alternatives to Radicalbit

Cloud-based solution for observability that helps businesses manage and track workload and performance through a single dashboard. Monitor all the services you run on your cloud without compromising cost, granularity or scale. Groundcover is a cloud-native APM solution that makes observability easy so you can focus on creating world-class products. Groundcover's proprietary sensor unlocks unprecedented granularity for all your applications. This eliminates the need for costly changes in code and development cycles, ensuring monitoring continuity.

MongoDB Atlas stands out as the leading cloud database service available, offering unparalleled data distribution and seamless mobility across all major platforms, including AWS, Azure, and Google Cloud. Its built-in automation tools enhance resource management and workload optimization, making it the go-to choice for modern application deployment. As a fully managed service, it ensures best-in-class automation and adheres to established practices that support high availability, scalability, and compliance with stringent data security and privacy regulations. Furthermore, MongoDB Atlas provides robust security controls tailored for your data needs, allowing for the integration of enterprise-grade features that align with existing security protocols and compliance measures. With preconfigured elements for authentication, authorization, and encryption, you can rest assured that your data remains secure and protected at all times. Ultimately, MongoDB Atlas not only simplifies deployment and scaling in the cloud but also fortifies your data with comprehensive security features that adapt to evolving requirements.

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.

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.

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.

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.

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.

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.

InfinyOn has developed a cutting-edge platform for continuous intelligence that operates on data as it flows. Different from conventional event streaming platforms that utilize Java, Infinyon Cloud leverages Rust to provide exceptional scalability and security for applications requiring real-time processing. The platform offers readily available programmable connectors that manipulate data events instantaneously. Users can establish intelligent analytics pipelines to enhance, secure, and correlate events in real-time. Furthermore, these programmable connectors facilitate the dispatch of events and keep relevant stakeholders informed. Each connector functions either as a source to bring in data or as a sink to send out data. These connectors can be implemented in two primary configurations: as a Managed Connector, where the Fluvio cluster handles provisioning and management, or as a Local Connector, which requires users to launch the connector manually as a Docker container in their preferred environment. Moreover, connectors are organized into four distinct stages, each with specific roles and responsibilities that contribute to the overall efficiency of data handling. This multi-stage approach enhances the adaptability and effectiveness of the platform in addressing diverse data needs.

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.

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.

Effortlessly merge information from older systems into modern cloud and data platforms using a single solution. Connect empowers you to manage your data transition from mainframe to cloud environments. It facilitates data integration through both batch processing and real-time ingestion, enabling sophisticated analytics, extensive machine learning applications, and smooth data migration processes. Drawing on years of experience, Connect harnesses Precisely's leadership in mainframe sorting and IBM i data security to excel in the complex realm of data access and integration. The solution guarantees access to all essential enterprise data for crucial business initiatives by providing comprehensive support for a variety of data sources and targets tailored to meet all your ELT and CDC requirements. This ensures that organizations can adapt and evolve their data strategies in a rapidly changing digital landscape.

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.

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.

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.

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.

Scalable Python framework designed to build real-time intelligent applications, data pipelines, and integrate AI/ML models

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.

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.

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.

IBM Cloud Pak for Integration® serves as a comprehensive hybrid integration platform that employs an automated, closed-loop strategy to facilitate various integration styles within a cohesive interface. It allows businesses to unlock their data and assets as APIs, seamlessly connect cloud and on-premises applications, and ensure reliable data movement through enterprise messaging systems. Additionally, it enables real-time event interactions, facilitates cross-cloud data transfers, and allows for scalable deployment using cloud-native architecture alongside shared foundational services, all while maintaining robust enterprise-grade security and encryption. By leveraging this platform, organizations can optimize their integration processes using a multi-faceted approach that is both automated and efficient. Moreover, innovations such as natural language-driven integration flows, AI-enhanced mapping, and robotic process automation (RPA) can be implemented to further streamline integrations and utilize specific operational data for ongoing enhancements, including improved API test generation and workload management. Ultimately, this comprehensive suite empowers businesses to achieve superior integration outcomes and adapt to evolving demands effectively.

We provide a globally distributed real-time database, along with stream processing and computing capabilities for event-driven applications, utilizing as many as 175 edge data centers around the world. Developers and API creators appreciate our platform because it addresses the complex challenges of managing shared mutable state across hundreds of locations with both strong consistency and minimal latency. Macrometa empowers you to seamlessly enhance your existing infrastructure, allowing you to reposition portions of your application or the entire setup closer to your end users. This strategic placement significantly boosts performance, enhances user experiences, and ensures adherence to international data governance regulations. Serving as a serverless, streaming NoSQL database, Macrometa encompasses integrated pub/sub features, stream data processing, and a compute engine. You can establish a stateful data infrastructure, create stateful functions and containers suitable for prolonged workloads, and handle data streams in real time. While you focus on coding, we manage all operational tasks and orchestration, freeing you to innovate without constraints. As a result, our platform not only simplifies development but also optimizes resource utilization across global networks.

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.

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.

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.

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.

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.

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.

Upsolver makes it easy to create a governed data lake, manage, integrate, and prepare streaming data for analysis. Only use auto-generated schema on-read SQL to create pipelines. A visual IDE that makes it easy to build pipelines. Add Upserts to data lake tables. Mix streaming and large-scale batch data. Automated schema evolution and reprocessing of previous state. Automated orchestration of pipelines (no Dags). Fully-managed execution at scale Strong consistency guarantee over object storage Nearly zero maintenance overhead for analytics-ready information. Integral hygiene for data lake tables, including columnar formats, partitioning and compaction, as well as vacuuming. Low cost, 100,000 events per second (billions every day) Continuous lock-free compaction to eliminate the "small file" problem. Parquet-based tables are ideal for quick queries.

Achieve seamless Continuous Intelligence solutions with greater speed, efficiency, and cost-effectiveness, all while minimizing engineering effort. The Cogility Cogynt platform offers a cloud-scalable event stream processing solution that is enriched by sophisticated, AI-driven analytics. With a comprehensive and unified toolset, organizations can efficiently and rapidly implement continuous intelligence solutions that meet their needs. This all-encompassing platform simplifies the deployment process by facilitating the construction of model logic, tailoring the intake of data sources, processing data streams, analyzing, visualizing, and disseminating intelligence insights, as well as auditing and enhancing outcomes while ensuring integration with other applications. Additionally, Cogynt’s Authoring Tool provides an intuitive, no-code design environment that allows users to create, modify, and deploy data models effortlessly. Moreover, the Data Management Tool from Cogynt simplifies the publishing of your model, enabling immediate application to stream data processing and effectively abstracting the complexities of Flink job coding for users. By leveraging these tools, organizations can transform their data into actionable insights with remarkable agility.

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.

Begin by opening a straightforward link or utilize our client libraries equipped with batching capabilities. Monitor signups, response times, monthly recurring revenue, or any other relevant metrics, and showcase your findings on an aesthetically pleasing dashboard. Arrange your key performance indicators into personalized dashboards that offer an attractive TV mode view. You can also transmit extra data to explore variations across different categories. Seamlessly integrate with NodeJS and Go using our efficient and user-friendly libraries. All information is securely stored and available at a resolution of one minute. We maintain this one-minute resolution for the duration of your subscription. Encourage your team members to collaborate and enjoy the visually appealing dashboards together. We have meticulously designed the system to ensure rapid data loading. Additionally, we believe that data visualization should be engaging and attractive, which is why we put effort into making it visually appealing.

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.

Microservices architecture enables efficient streaming and batch data processing specifically designed for platforms like Cloud Foundry and Kubernetes. By utilizing Spring Cloud Data Flow, users can effectively design intricate topologies for their data pipelines, which feature Spring Boot applications developed with the Spring Cloud Stream or Spring Cloud Task frameworks. This powerful tool caters to a variety of data processing needs, encompassing areas such as ETL, data import/export, event streaming, and predictive analytics. The Spring Cloud Data Flow server leverages Spring Cloud Deployer to facilitate the deployment of these data pipelines, which consist of Spring Cloud Stream or Spring Cloud Task applications, onto contemporary infrastructures like Cloud Foundry and Kubernetes. Additionally, a curated selection of pre-built starter applications for streaming and batch tasks supports diverse data integration and processing scenarios, aiding users in their learning and experimentation endeavors. Furthermore, developers have the flexibility to create custom stream and task applications tailored to specific middleware or data services, all while adhering to the user-friendly Spring Boot programming model. This adaptability makes Spring Cloud Data Flow a valuable asset for organizations looking to optimize their data workflows.

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.

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.

Analyze and utilize your data at the Edge to transform Big Data into manageable, pertinent insights. Gather sensor information from all your equipment and establish connections with various devices like sensors, PLCs, DCS, MES, or historians. Implement condition monitoring for assets located remotely, aligning with Industry 4.0 standards for effective data collection and integration. Merge real-time streaming data with enterprise data for seamless data flows, and utilize your preferred Cloud Provider or your own data center for data storage solutions. Leverage Crosser Edge's MLOps capabilities to bring, manage, and deploy your custom machine learning models, with the Crosser Edge Node supporting any machine learning framework. Access a centralized library for your trained models hosted in Crosser Cloud, and streamline your data pipeline using a user-friendly drag-and-drop interface. Easily deploy machine learning models to multiple Edge Nodes with a single operation, fostering self-service innovation through Crosser Flow Studio. Take advantage of an extensive library of pre-built modules to facilitate collaboration among teams across different locations, effectively reducing reliance on individual team members and enhancing organizational efficiency. With these capabilities, your workflow will promote collaboration and innovation like never before.

Elevate your integration capabilities with the most extensive, microservices-oriented, API-centric, and AI-enhanced enterprise iPaaS available. Utilizing the advanced CLAIRE engine, IICS accommodates a wide array of cloud-native integration needs, including data, application, API integration, and Master Data Management (MDM). Our global reach and support for multiple cloud environments extend to major platforms like Microsoft Azure, AWS, Google Cloud Platform, and Snowflake. With unmatched enterprise scalability and a robust security framework backed by numerous certifications, IICS stands as a pillar of trust in the industry. This enterprise iPaaS features a suite of cloud data management solutions designed to boost efficiency while enhancing speed and scalability. Once again, Informatica has been recognized as a Leader in the Gartner 2020 Magic Quadrant for Enterprise iPaaS, reinforcing our commitment to excellence. Experience firsthand insights and testimonials about Informatica Intelligent Cloud Services, and take advantage of our complimentary cloud offerings. Our customers remain our top priority in all facets, including products, services, and support, which is why we've consistently achieved outstanding customer loyalty ratings for over a decade. Join us in redefining integration excellence and discover how we can help transform your business operations.

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.

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.

In-Memory Computing Platform. Digital world is different. Microseconds are important. The world's most important organizations rely on us for powering their most sensitive applications at scale. If they meet the current requirement for immediate access, new data-enabled apps can transform your business. Hazelcast solutions can be used to complement any database and deliver results that are much faster than traditional systems of record. Hazelcast's distributed architecture ensures redundancy and continuous cluster up-time, as well as always available data to support the most demanding applications. The capacity grows with demand without compromising performance and availability. The cloud delivers the fastest in-memory data grid and third-generation high speed event processing.

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.

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.

Akka serves as a powerful toolkit designed for creating highly concurrent, distributed, and resilient applications driven by messages, specifically tailored for Java and Scala. Complementing this is Akka Insights, a sophisticated monitoring and observability solution specifically engineered for Akka environments. With the use of Actors and Streams, developers can create systems that efficiently utilize server resources and expand across multiple servers. Grounded in the tenets of The Reactive Manifesto, Akka empowers the development of systems capable of self-repairing and maintaining responsiveness despite encountering failures. It facilitates the creation of distributed systems free from single points of failure, incorporates load balancing and adaptive routing among nodes, and supports Event Sourcing and CQRS in conjunction with Cluster Sharding. Furthermore, it enables Distributed Data to ensure eventual consistency through CRDTs, while also providing asynchronous, non-blocking stream processing equipped with backpressure mechanisms. Its fully asynchronous and streaming HTTP server and client capabilities make it an excellent foundation for microservice architecture, and the integration with Alpakka enhances its streaming capabilities for various applications. As a result, Akka stands out as a comprehensive solution for modern application development.

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.