Alternatives to DeltaStream

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.

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.

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.

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.

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.

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.

Apache Flink serves as a powerful framework and distributed processing engine tailored for executing stateful computations on both unbounded and bounded data streams. It has been engineered to operate seamlessly across various cluster environments, delivering computations with impressive in-memory speed and scalability. Data of all types is generated as a continuous stream of events, encompassing credit card transactions, sensor data, machine logs, and user actions on websites or mobile apps. The capabilities of Apache Flink shine particularly when handling both unbounded and bounded data sets. Its precise management of time and state allows Flink’s runtime to support a wide range of applications operating on unbounded streams. For bounded streams, Flink employs specialized algorithms and data structures optimized for fixed-size data sets, ensuring remarkable performance. Furthermore, Flink is adept at integrating with all previously mentioned resource managers, enhancing its versatility in various computing environments. This makes Flink a valuable tool for developers seeking efficient and reliable stream processing solutions.

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.

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.

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.

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.

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.

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.

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.

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

IBM Streams analyzes a diverse array of streaming data, including unstructured text, video, audio, geospatial data, and sensor inputs, enabling organizations to identify opportunities and mitigate risks while making swift decisions. By leveraging IBM® Streams, users can transform rapidly changing data into meaningful insights. This platform evaluates various forms of streaming data, empowering organizations to recognize trends and threats as they arise. When integrated with other capabilities of IBM Cloud Pak® for Data, which is founded on a flexible and open architecture, it enhances the collaborative efforts of data scientists in developing models to apply to stream flows. Furthermore, it facilitates the real-time analysis of vast datasets, ensuring that deriving actionable value from your data has never been more straightforward. With these tools, organizations can harness the full potential of their data streams for improved outcomes.

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.

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.

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.

A vast number of users leverage Amazon Managed Service for Apache Flink to execute their stream processing applications. This service allows you to analyze and transform streaming data in real-time through Apache Flink while seamlessly integrating with other AWS offerings. There is no need to manage servers or clusters, nor is there a requirement to establish computing and storage infrastructure. You are billed solely for the resources you consume. You can create and operate Apache Flink applications without the hassle of infrastructure setup and resource management. Experience the capability to process vast amounts of data at incredible speeds with subsecond latencies, enabling immediate responses to events. With Multi-AZ deployments and APIs for application lifecycle management, you can deploy applications that are both highly available and durable. Furthermore, you can develop solutions that efficiently transform and route data to services like Amazon Simple Storage Service (Amazon S3) and Amazon OpenSearch Service, among others, enhancing your application's functionality and reach. This service simplifies the complexities of stream processing, allowing developers to focus on building innovative solutions.

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.

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.

Explore Azure Stream Analytics, a user-friendly real-time analytics solution tailored for essential workloads. Create a comprehensive serverless streaming pipeline effortlessly within a matter of clicks. Transition from initial setup to full production in mere minutes with SQL, which can be easily enhanced with custom code and integrated machine learning features for complex use cases. Rely on the assurance of a financially backed SLA as you handle your most challenging workloads, knowing that performance and reliability are prioritized. This service empowers organizations to harness real-time data effectively, ensuring timely insights and informed decision-making.

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

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.

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

In the field of IoT stream processing and analytics, SQLstream ranks #1 according to ABI Research. Used by Verizon, Walmart, Cisco, and Amazon, our technology powers applications on premises, in the cloud, and at the edge. SQLstream enables time-critical alerts, live dashboards, and real-time action with sub-millisecond latency. Smart cities can reroute ambulances and fire trucks or optimize traffic light timing based on real-time conditions. Security systems can detect hackers and fraudsters, shutting them down right away. AI / ML models, trained with streaming sensor data, can predict equipment failures. Thanks to SQLstream's lightning performance -- up to 13 million rows / second / CPU core -- companies have drastically reduced their footprint and cost. Our efficient, in-memory processing allows operations at the edge that would otherwise be impossible. Acquire, prepare, analyze, and act on data in any format from any source. Create pipelines in minutes not months with StreamLab, our interactive, low-code, GUI dev environment. Edit scripts instantly and view instantaneous results without compiling. Deploy with native Kubernetes support. Easy installation includes Docker, AWS, Azure, Linux, VMWare, and more

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.

Baidu Stream Computing (BSC) offers the ability to process real-time streaming data with minimal latency, impressive throughput, and high precision. It seamlessly integrates with Spark SQL, allowing for complex business logic to be executed via SQL statements, which enhances usability. Users benefit from comprehensive lifecycle management of their streaming computing tasks. Additionally, BSC deeply integrates with various Baidu AI Cloud storage solutions, such as Baidu Kafka, RDS, BOS, IOT Hub, Baidu ElasticSearch, TSDB, and SCS, serving as both upstream and downstream components in the stream computing ecosystem. Moreover, it provides robust job monitoring capabilities, enabling users to track performance indicators and establish alarm rules to ensure job security, thereby enhancing the overall reliability of the system. This level of integration and monitoring makes BSC a powerful tool for businesses looking to leverage real-time data processing effectively.

Facilitates seamless data exchange among components within microservice architectures. When utilized as a communication method for microservices, it not only streamlines integration but also enhances reliability and scalability. The system allows for reading and writing data in nearly real-time, while providing the flexibility to set data throughput and storage durations according to specific requirements. Users can finely configure resources for processing data streams, accommodating anything from small streams of 100 KB/s to more substantial ones at 100 MB/s. Additionally, Yandex Data Transfer enables the delivery of a single stream to various targets with distinct retention policies. Data is automatically replicated across multiple availability zones that are geographically distributed, ensuring redundancy and accessibility. After the initial setup, managing data streams can be done centrally through either the management console or the API, offering convenient oversight. It also supports continuous data collection from diverse sources, including website browsing histories and application logs, making it a versatile tool for real-time analytics. Overall, Yandex Data Streams stands out for its robust capabilities in handling various data ingestion needs across different platforms.

With your data now actively flowing, it's essential to extract meaningful insights from it. Stream processing allows for immediate analysis of your data streams, though establishing the necessary infrastructure can be a daunting task. To address this challenge, Confluent has introduced ksqlDB, a database specifically designed for applications that require stream processing. By continuously processing data streams generated across your organization, you can turn your data into actionable insights right away. ksqlDB features an easy-to-use syntax that facilitates quick access to and enhancement of data within Kafka, empowering development teams to create real-time customer experiences and meet operational demands driven by data. This platform provides a comprehensive solution for gathering data streams, enriching them, and executing queries on newly derived streams and tables. As a result, you will have fewer infrastructure components to deploy, manage, scale, and secure. By minimizing the complexity in your data architecture, you can concentrate more on fostering innovation and less on technical maintenance. Ultimately, ksqlDB transforms the way businesses leverage their data for growth and efficiency.

Apache Storm is a distributed computation system that is both free and open source, designed for real-time data processing. It simplifies the reliable handling of endless data streams, similar to how Hadoop revolutionized batch processing. The platform is user-friendly, compatible with various programming languages, and offers an enjoyable experience for developers. With numerous applications including real-time analytics, online machine learning, continuous computation, distributed RPC, and ETL, Apache Storm proves its versatility. It's remarkably fast, with benchmarks showing it can process over a million tuples per second on a single node. Additionally, it is scalable and fault-tolerant, ensuring that data processing is both reliable and efficient. Setting up and managing Apache Storm is straightforward, and it seamlessly integrates with existing queueing and database technologies. Users can design Apache Storm topologies to consume and process data streams in complex manners, allowing for flexible repartitioning between different stages of computation. For further insights, be sure to explore the detailed tutorial available.

Effortlessly capture, modify, and transfer streaming data in real time. You can create a delivery stream, choose your desired destination, and begin streaming data with minimal effort. The system automatically provisions and scales necessary compute, memory, and network resources without the need for continuous management. You can convert raw streaming data into various formats such as Apache Parquet and dynamically partition it without the hassle of developing your processing pipelines. Amazon Data Firehose is the most straightforward method to obtain, transform, and dispatch data streams in mere seconds to data lakes, data warehouses, and analytics platforms. To utilize Amazon Data Firehose, simply establish a stream by specifying the source, destination, and any transformations needed. The service continuously processes your data stream, automatically adjusts its scale according to the data volume, and ensures delivery within seconds. You can either choose a source for your data stream or utilize the Firehose Direct PUT API to write data directly. This streamlined approach allows for greater efficiency and flexibility in handling data streams.

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.

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.

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

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.

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.

Say goodbye to the complexities of low-level coding and integrating intricate systems. With SQL, you can effortlessly construct and deploy data pipelines in mere minutes. This data engineering service empowers both developers and data engineers to easily create and implement real-time data pipelines tailored for data-centric applications. The platform provides ready-made connectors for various messaging systems, storage solutions, and database engines, simplifying the process of connecting to and discovering available data. Each established connection generates a stream that facilitates data movement to or from the respective system. Utilizing Decodable, you can design your pipelines using SQL, where streams play a crucial role in transmitting data to and from your connections. Additionally, streams can be utilized to link pipelines, enabling the management of even the most intricate processing tasks. You can monitor your pipelines to ensure a steady flow of data and create curated streams for collaborative use by other teams. Implement retention policies on streams to prevent data loss during external system disruptions, and benefit from real-time health and performance metrics that keep you informed about the operation's status, ensuring everything is running smoothly. Ultimately, Decodable streamlines the entire data pipeline process, allowing for greater efficiency and quicker results in data handling and analysis.

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

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.

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.

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

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.