Alternatives to Hitachi Streaming Data Platform

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

Oracle Stream Analytics empowers users to handle and evaluate vast amounts of real-time data through advanced correlation techniques, enrichment capabilities, and machine learning integration. This platform delivers immediate, actionable insights for businesses dealing with streaming information, facilitating automated responses that support the needs of modern agile enterprises. It features Visual GEOProcessing with GEOFence relationship spatial analytics, enhancing location-based decision-making. Additionally, the introduction of a new Expressive Patterns Library encompasses various categories, such as Spatial, Statistical, General industry, and Anomaly detection, alongside streaming machine learning functionalities. With an intuitive visual interface, users can seamlessly explore live streaming data, enabling effective in-memory analytics that enhance real-time business strategies. Overall, this powerful tool significantly improves operational efficiency and decision-making processes in fast-paced environments.

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.

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.

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

Real-time analytics on raw data. Live ingest from S3, DynamoDB, DynamoDB and more. Raw data can be accessed as SQL tables. In minutes, you can create amazing data-driven apps and live dashboards. Rockset is a serverless analytics and search engine that powers real-time applications and live dashboards. You can directly work with raw data such as JSON, XML and CSV. Rockset can import data from real-time streams and data lakes, data warehouses, and databases. You can import real-time data without the need to build pipelines. Rockset syncs all new data as it arrives in your data sources, without the need to create a fixed schema. You can use familiar SQL, including filters, joins, and aggregations. Rockset automatically indexes every field in your data, making it lightning fast. Fast queries are used to power your apps, microservices and live dashboards. Scale without worrying too much about servers, shards or pagers.

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

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.

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.

Insigna - The complete Platform for Real-time Analytics and Data Management. Insigna offers integration, automated processing, transformation, data preparation and real-time analytics to derive and deliver intelligence to various stakeholders. Insigna enables connectivity with the most popular network communication protocols, data stores, enterprise applications, and cloud platforms. Coupled with a rich set of out-of-the-box data transformation capabilities, enterprises greatly benefit from the opportunities offered by operations data generated in real-time.

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.

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

IBM 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.

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 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.

Evam's Continuous Intelligence Platform integrates various products aimed at the processing and visualization of real-time data streams. It operates machine learning models in real time while enhancing the data with an advanced in-memory caching system. By doing so, EVAM allows companies in telecommunications, financial services, retail, transportation, and travel sectors to fully leverage their business potential. This platform's machine learning capabilities facilitate the processing of live data, enabling the visual design and orchestration of customer journeys through sophisticated analytical models and AI algorithms. Furthermore, EVAM helps businesses connect with their customers across various channels, including legacy systems, in real time. With the ability to collect and process billions of events instantaneously, companies can gain valuable insights into each customer’s preferences, allowing them to attract, engage, and retain clients more efficiently. The effectiveness of such a system not only enhances operational capabilities but also fosters deeper customer relationships.

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.

Timeplus is an efficient, user-friendly stream processing platform that is both powerful and affordable. It comes packaged as a single binary, making it easy to deploy in various environments. Designed for data teams across diverse sectors, it enables the quick and intuitive processing of both streaming and historical data. With a lightweight design that requires no external dependencies, Timeplus offers comprehensive analytic capabilities for streaming and historical data. Its cost is just a fraction—1/10—of what similar open-source frameworks charge. Users can transform real-time market and transaction data into actionable insights seamlessly. The platform supports both append-only and key-value streams, making it ideal for monitoring financial information. Additionally, Timeplus allows the creation of real-time feature pipelines effortlessly. It serves as a unified solution for managing all infrastructure logs, metrics, and traces, which are essential for maintaining observability. Timeplus also accommodates a broad array of data sources through its user-friendly web console UI, while providing options to push data via REST API or to create external streams without the need to copy data into the platform. Overall, Timeplus offers a versatile and comprehensive approach to data processing for organizations looking to enhance their operational efficiency.

Apama Streaming Analytics empowers businesses to process and respond to IoT and rapidly changing data in real-time, enabling them to react intelligently as events unfold. The Apama Community Edition serves as a freemium option from Software AG, offering users the chance to explore, develop, and deploy streaming analytics applications in a practical setting. Meanwhile, the Software AG Data & Analytics Platform presents a comprehensive, modular, and cohesive suite of advanced capabilities tailored for managing high-velocity data and conducting analytics on real-time information, complete with seamless integration to essential enterprise data sources. Users can select the features they require, including streaming, predictive, and visual analytics, alongside messaging capabilities that facilitate straightforward integration with various enterprise applications and an in-memory data store that ensures rapid access. Additionally, by incorporating historical data for comparative analysis, organizations can enhance their models and enrich critical customer and operational data, ultimately leading to more informed decision-making. This level of flexibility and functionality makes Apama an invaluable asset for companies aiming to leverage their data effectively.

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.

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.

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.

HarperDB is an innovative platform that integrates database management, caching, application development, and streaming capabilities into a cohesive system. This allows businesses to efficiently implement global-scale back-end services with significantly reduced effort, enhanced performance, and cost savings compared to traditional methods. Users can deploy custom applications along with pre-existing add-ons, ensuring a high-throughput and ultra-low latency environment for their data needs. Its exceptionally fast distributed database offers vastly superior throughput rates than commonly used NoSQL solutions while maintaining unlimited horizontal scalability. Additionally, HarperDB supports real-time pub/sub communication and data processing through protocols like MQTT, WebSocket, and HTTP. This means organizations can leverage powerful data-in-motion functionalities without the necessity of adding extra services, such as Kafka, to their architecture. By prioritizing features that drive business growth, companies can avoid the complexities of managing intricate infrastructures. While you can’t alter the speed of light, you can certainly minimize the distance between your users and their data, enhancing overall efficiency and responsiveness. In doing so, HarperDB empowers businesses to focus on innovation and progress rather than getting bogged down by technical challenges.

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.

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.

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.

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.

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.

A cloud database that can scale to handle large streaming data sets. Kinetica harnesses modern vectorized processors to perform orders of magnitude faster for real-time spatial or temporal workloads. In real-time, track and gain intelligence from billions upon billions of moving objects. Vectorization unlocks new levels in performance for analytics on spatial or time series data at large scale. You can query and ingest simultaneously to take action on real-time events. Kinetica's lockless architecture allows for distributed ingestion, which means data is always available to be accessed as soon as it arrives. Vectorized processing allows you to do more with fewer resources. More power means simpler data structures which can be stored more efficiently, which in turn allows you to spend less time engineering your data. Vectorized processing allows for incredibly fast analytics and detailed visualizations of moving objects at large scale.

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.

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.

Cumulocity IoT stands out as the premier low-code, self-service IoT platform, uniquely offering pre-integration with essential tools for rapid outcomes, including device connectivity and management, application enablement, integration, and advanced analytics for both streaming and predictive insights. Break free from restrictive proprietary technology ecosystems, as this platform is entirely open, allowing you to connect any device today or in the future. Customize your setup by bringing your own hardware and selecting the components that suit your needs best. You can quickly jump into the IoT world within minutes by connecting a device, monitoring its data, and crafting an interactive dashboard in real-time. Additionally, you can establish rules to oversee and respond to events—all without needing IT assistance or writing any code! Effortlessly integrate fresh IoT data into the existing core enterprise systems, applications, and processes that have supported your business for years, again without the need for coding, ensuring seamless data flow. This capability enhances your understanding, providing you with richer context to make informed decisions and improve overall business outcomes.

BlackLynx's accelerators offer analytics capabilities exactly where they are required, eliminating the need for specialized expertise. Regardless of the components of your analytics framework, you can harness data-driven insights through robust and user-friendly heterogeneous computing solutions. The integration of BlackStack software with electronic systems significantly enhances processing speeds for sensors utilized across various platforms, including terrestrial, maritime, aerospace, and aerial assets. Our innovative software empowers clients to optimize essential AI/ML algorithms and other computational tasks, specifically targeting real-time sensor data processing, which encompasses signal detection, video analytics, missile tracking, radar operations, thermal imaging, and other object detection functionalities. Additionally, BlackStack software substantially improves the speed of processing for real-time data analytics. We enable our clients to delve into enterprise-level unstructured data, providing the tools necessary to gather, filter, and systematically arrange extensive intelligence or cybersecurity forensic data sets, ultimately transforming how they manage and respond to vast streams of information. This capability allows organizations to make informed decisions that drive efficiency and innovation.

Esper Enterprise Edition offers a robust platform designed for both linear and elastic scalability, as well as reliable event processing that can withstand faults. It comes equipped with an EPL editor and debugger, supports hot deployment, and provides comprehensive reporting on metrics and memory usage, including detailed breakdowns per EPL. Additionally, it features Data Push capabilities for seamless multi-tier delivery from CEP to browsers and manages both logical and physical subscribers and their subscriptions effectively. Its web-based user interface allows users to oversee various distributed engine instances using JavaScript and HTML5, while also enabling the creation of composable and interactive displays for visualizing distributed event streams through charts, gauges, timelines, and grids. Furthermore, it includes JDBC-compliant client and server endpoints to ensure interoperability across systems. Notably, Esper Enterprise Edition is a proprietary commercial product developed by EsperTech, with source code accessibility granted solely for the support of customers. Such versatility and functionality make it a robust choice for enterprises seeking efficient event processing solutions.

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.

Smart DIH is a data management platform that quickly serves applications with accurate, fresh and complete data, delivering high performance, ultra-low latency, and an always-on digital experience. Smart DIH decouples APIs from SoRs, replicating critical data, and making it available using event-driven architecture. Smart DIH enables drastically shorter development cycles of new digital services, and rapidly scales to serve millions of concurrent users – no matter which IT infrastructure or cloud topologies it relies on. XAP Skyline is a distributed in-memory development platform that delivers transactional consistency, combined with extreme event-based processing and microsecond latency. The platform fuels core business solutions that rely on instantaneous data, including online trading, real-time risk management and data processing for AI and large language models.

SelectDB is an innovative data warehouse built on Apache Doris, designed for swift query analysis on extensive real-time datasets. Transitioning from Clickhouse to Apache Doris facilitates the separation of the data lake and promotes an upgrade to a more efficient lake warehouse structure. This high-speed OLAP system handles nearly a billion query requests daily, catering to various data service needs across multiple scenarios. To address issues such as storage redundancy, resource contention, and the complexities of data governance and querying, the original lake warehouse architecture was restructured with Apache Doris. By leveraging Doris's capabilities for materialized view rewriting and automated services, it achieves both high-performance data querying and adaptable data governance strategies. The system allows for real-time data writing within seconds and enables the synchronization of streaming data from databases. With a storage engine that supports immediate updates and enhancements, it also facilitates real-time pre-polymerization of data for improved processing efficiency. This integration marks a significant advancement in the management and utilization of large-scale real-time data.

Azure Data Explorer is an efficient and fully managed analytics service designed for swift analysis of vast amounts of data that originate from various sources such as applications, websites, and IoT devices. Users can pose questions and delve into their data in real-time, allowing for enhancements in product development, customer satisfaction, device monitoring, and overall operational efficiency. This service enables quick detection of patterns, anomalies, and emerging trends within the data landscape. Users can formulate and receive answers to new inquiries within minutes, and the framework allows for unlimited queries thanks to its cost-effective structure. With Azure Data Explorer, organizations can discover innovative ways to utilize their data without overspending. By prioritizing insights over infrastructure, users benefit from a straightforward, fully managed analytics platform. This service is adept at addressing the challenges posed by fast-moving and constantly evolving data streams, making analytics more accessible and efficient for all types of streaming information. Ultimately, Azure Data Explorer empowers businesses to leverage their data in transformative ways.

Monitoring and visualization of real-time operations, including KPIs and dashboards. Also includes trends, analytics, hierarchy, alerts, and analytics. All data sources (industrial and IoT, business, and external) are gathered. It displays data in real-time on any device, without the need to move it.

Estuary Flow, a new DataOps platform, empowers engineering teams with the ability to build data-intensive real-time applications at scale and with minimal friction. This platform allows teams to unify their databases, pub/sub and SaaS systems around their data without having to invest in new infrastructure or development.

A user-friendly, serverless service for change data capture and replication that provides access to streaming data from a variety of databases including MySQL, PostgreSQL, AlloyDB, SQL Server, and Oracle. This solution enables near real-time analytics in BigQuery, allowing for quick insights and decision-making. With a straightforward setup that includes built-in secure connectivity, organizations can achieve faster time-to-value. The platform is designed to scale automatically, eliminating the need for resource provisioning or management. Utilizing a log-based mechanism, it minimizes the load and potential disruptions on source databases, ensuring smooth operation. This service allows for reliable data synchronization across diverse databases, storage systems, and applications, while keeping latency low and reducing any negative impact on source performance. Organizations can quickly activate the service, enjoying the benefits of a scalable solution with no infrastructure overhead. Additionally, it facilitates seamless data integration across the organization, leveraging the power of Google Cloud services such as BigQuery, Spanner, Dataflow, and Data Fusion, thus enhancing overall operational efficiency and data accessibility. This comprehensive approach not only streamlines data processes but also empowers teams to make informed decisions based on timely data insights.

Navigating the challenges of your organization may seem daunting, but creating a solution doesn't have to be complicated. 3forge offers a highly adaptable, low-code platform designed to accelerate enterprise application development like never before. Need reliability? Absolutely. Looking for scalability? You got it. What about rapid deliverability? That's also guaranteed, even with intricate workflows and extensive data sets. With 3forge, you can seamlessly unify data integration, virtualization, processing, visualization, and workflows in a single solution, effectively tackling some of the most intricate real-time streaming data challenges faced today. This award-winning technology from 3forge empowers developers to launch mission-critical applications swiftly and efficiently. Discover the transformative power of real-time data coupled with zero latency, as 3forge prioritizes seamless data integration, virtualization, processing, and visualization for optimal performance. As a result, you can focus on innovation rather than getting bogged down in technical difficulties.

A live data platform based on Change Data Capture (CDC) facilitates the replication of diverse databases, supports real-time data integration, and assists in constructing a real-time data warehouse. By implementing CDC to synchronize production line information stored in DB2 and Oracle with a modern database, TapData has empowered an AI-enhanced real-time dispatch application that streamlines the semiconductor manufacturing process. The availability of real-time data has enabled prompt decision-making within the RTD application, resulting in quicker turnaround times and increased production yield. As a major telecommunications provider, the client operates numerous regional systems designed to serve local users effectively. By consolidating and integrating data from multiple sources and locations into a single centralized data repository, the client successfully established an order center that aggregates orders from various applications. Additionally, TapData integrates inventory data from over 500 stores, delivering real-time insights into stock availability and customer preferences, thereby significantly boosting supply chain efficiency. This innovative approach not only enhances operational performance but also positions the client to respond swiftly to market demands.

VeloDB, which utilizes Apache Doris, represents a cutting-edge data warehouse designed for rapid analytics on large-scale real-time data. It features both push-based micro-batch and pull-based streaming data ingestion that occurs in mere seconds, alongside a storage engine capable of real-time upserts, appends, and pre-aggregations. The platform delivers exceptional performance for real-time data serving and allows for dynamic interactive ad-hoc queries. VeloDB accommodates not only structured data but also semi-structured formats, supporting both real-time analytics and batch processing capabilities. Moreover, it functions as a federated query engine, enabling seamless access to external data lakes and databases in addition to internal data. The system is designed for distribution, ensuring linear scalability. Users can deploy it on-premises or as a cloud service, allowing for adaptable resource allocation based on workload demands, whether through separation or integration of storage and compute resources. Leveraging the strengths of open-source Apache Doris, VeloDB supports the MySQL protocol and various functions, allowing for straightforward integration with a wide range of data tools, ensuring flexibility and compatibility across different environments.

Aerospike is the global leader for next-generation, real time NoSQL data solutions at any scale. Aerospike helps enterprises overcome seemingly impossible data bottlenecks and compete with other companies at a fraction of the cost and complexity of legacy NoSQL databases. Aerospike's Hybrid Memory Architecture™ is a patented technology that unlocks the full potential of modern hardware and delivers previously unimaginable value. It does this by delivering unimaginable value from huge amounts of data at both the edge, core, and in the cloud. Aerospike empowers customers with the ability to instantly combat fraud, dramatically increase shopping cart sizes, deploy global digital payment networks, and provide instant, one-to-1 personalization for millions. Aerospike customers include Airtel and Banca d'Italia as well as Snap, Verizon Media, Wayfair, PayPal, Snap, Verizon Media, and Nielsen. The company's headquarters is in Mountain View, California. Additional locations are in London, Bengaluru, India, and Tel Aviv in Israel.