Alternatives to Esper Enterprise Edition

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.

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.

Instant™, Enterprise Data and Intelligence. All transaction data is visible, as it happens, from anywhere in the world. Organizations can access worldwide accounting, liquidity management, marketing, and supply chain information with just one network connection. This allows them to be empowered with enterprise intelligence. The TreasuryPay product set streams global receivables information and provides instant accountancy as well as cognitive services. It is simply the most advanced intelligence platform and insights platform available to global organizations. You can instantly provide enriched information to your entire global organization. It's easy to make the change. The Return on Investment is remarkable. With TreasuryPay Instant™, you can now access actionable intelligence and global accountancy in real-time.

KX Insights serves as a cloud-native platform that provides essential real-time performance analytics and actionable intelligence continuously. By utilizing advanced techniques such as complex event processing, rapid analytics, and machine learning interfaces, it facilitates swift decision-making and automates responses to events in mere fractions of a second. The migration to the cloud encompasses not only storage and computational flexibility but also includes a comprehensive array of elements: data, tools, development, security, connectivity, operations, and maintenance. KX empowers organizations to harness this cloud capability, enabling them to make more informed and insightful decisions by seamlessly integrating real-time analytics into their operational frameworks. Additionally, KX Insights adheres to industry standards, promoting openness and interoperability with diverse technologies, which accelerates the delivery of insights in a cost-effective manner. Its architecture is based on microservices, designed for efficiently capturing, storing, and processing high-volume and high-velocity data utilizing established cloud standards, services, and protocols, ensuring optimal performance and scalability. This innovative approach not only enhances operational efficiency but also positions businesses to adapt swiftly to changing market dynamics.

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 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.

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

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

KX Streaming Analytics offers a comprehensive solution for ingesting, storing, processing, and analyzing both historical and time series data, ensuring that analytics, insights, and visualizations are readily accessible. To facilitate rapid productivity for your applications and users, the platform encompasses the complete range of data services, which includes query processing, tiering, migration, archiving, data protection, and scalability. Our sophisticated analytics and visualization tools, which are extensively utilized in sectors such as finance and industry, empower you to define and execute queries, calculations, aggregations, as well as machine learning and artificial intelligence on any type of streaming and historical data. This platform can be deployed across various hardware environments, with the capability to source data from real-time business events and high-volume inputs such as sensors, clickstreams, radio-frequency identification, GPS systems, social media platforms, and mobile devices. Moreover, the versatility of KX Streaming Analytics ensures that organizations can adapt to evolving data needs and leverage real-time insights for informed decision-making.

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.

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.

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.

Establishing a cohesive logging framework is essential for ensuring that log data is both accessible and functional. Unfortunately, many current solutions are inadequate; traditional tools do not cater to the demands of modern cloud APIs and microservices, and they are not evolving at a sufficient pace. Fluentd, developed by Treasure Data, effectively tackles the issues associated with creating a unified logging framework through its modular design, extensible plugin system, and performance-enhanced engine. Beyond these capabilities, Fluentd Enterprise also fulfills the needs of large organizations by providing features such as Trusted Packaging, robust security measures, Certified Enterprise Connectors, comprehensive management and monitoring tools, as well as SLA-based support and consulting services tailored for enterprise clients. This combination of features makes Fluentd a compelling choice for businesses looking to enhance their logging infrastructure.

Gathr is a Data+AI fabric, helping enterprises rapidly deliver production-ready data and AI products. Data+AI fabric enables teams to effortlessly acquire, process, and harness data, leverage AI services to generate intelligence, and build consumer applications— all with unparalleled speed, scale, and confidence. Gathr’s self-service, AI-assisted, and collaborative approach enables data and AI leaders to achieve massive productivity gains by empowering their existing teams to deliver more valuable work in less time. With complete ownership and control over data and AI, flexibility and agility to experiment and innovate on an ongoing basis, and proven reliable performance at real-world scale, Gathr allows them to confidently accelerate POVs to production. Additionally, Gathr supports both cloud and air-gapped deployments, making it the ideal choice for diverse enterprise needs. Gathr, recognized by leading analysts like Gartner and Forrester, is a go-to-partner for Fortune 500 companies, such as United, Kroger, Philips, Truist, and many others.

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.

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.

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.

The Hitachi Streaming Data Platform (SDP) is engineered for real-time processing of extensive time-series data as it is produced. Utilizing in-memory and incremental computation techniques, SDP allows for rapid analysis that circumvents the typical delays experienced with conventional stored data processing methods. Users have the capability to outline summary analysis scenarios through Continuous Query Language (CQL), which resembles SQL, thus enabling adaptable and programmable data examination without requiring bespoke applications. The platform's architecture includes various components such as development servers, data-transfer servers, data-analysis servers, and dashboard servers, which together create a scalable and efficient data processing ecosystem. Additionally, SDP’s modular framework accommodates multiple data input and output formats, including text files and HTTP packets, and seamlessly integrates with visualization tools like RTView for real-time performance monitoring. This comprehensive design ensures that users can effectively manage and analyze data streams as they occur.

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.

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.

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

Apache Spark™ serves as a comprehensive analytics platform designed for large-scale data processing. It delivers exceptional performance for both batch and streaming data by employing an advanced Directed Acyclic Graph (DAG) scheduler, a sophisticated query optimizer, and a robust execution engine. With over 80 high-level operators available, Spark simplifies the development of parallel applications. Additionally, it supports interactive use through various shells including Scala, Python, R, and SQL. Spark supports a rich ecosystem of libraries such as SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming, allowing for seamless integration within a single application. It is compatible with various environments, including Hadoop, Apache Mesos, Kubernetes, and standalone setups, as well as cloud deployments. Furthermore, Spark can connect to a multitude of data sources, enabling access to data stored in systems like HDFS, Alluxio, Apache Cassandra, Apache HBase, and Apache Hive, among many others. This versatility makes Spark an invaluable tool for organizations looking to harness the power of large-scale data analytics.

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

Tealium Customer Data hub is an advanced platform that unifies, manages, and activates customer data across multiple touchpoints and channels. It allows businesses to create a real-time, cohesive view of their customers by integrating data from mobile apps, websites, and other digital sources. This centralized data center empowers organizations to deliver customized experiences, optimize marketing strategy, and enhance customer interaction. Tealium Customer Data Hub offers robust features such as data collection, audience segmentation and real-time orchestration of data. This allows businesses to transform raw data into actionable insight, driving more effective interactions with customers and improved business outcomes.

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.

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

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.

An innovative and more secure approach to developing fault-tolerant cloud applications is offered through the groundbreaking cloud-native DBOS operating system. Drawing from three years of collaborative open-source research and development between MIT and Stanford, DBOS transforms the landscape of cloud-native architecture. This cloud-native operating system leverages a relational database to significantly streamline the intricate application stacks commonly found today. DBOS underpins DBOS Cloud, which serves as a transactional serverless platform that ensures fault tolerance, observability, cyber resilience, and straightforward deployment for stateful TypeScript applications. The services of the operating system are built upon a distributed database management system, featuring integrated transactional and fault-tolerant state management that reduces complexity by eliminating the need for containers, cluster management, or workflow orchestration. It offers seamless scalability, outstanding performance, and consistent availability, while metrics, logs, and traces are conveniently stored in SQL-accessible tables. Additionally, the architecture minimizes the cyber attack surface, incorporates self-detection mechanisms for cyber threats, and enhances overall cyber resilience, making it a robust choice for modern cloud applications. Overall, the DBOS operating system represents a significant leap forward in simplifying cloud application development while ensuring high security and reliability.

Voldemort does not function as a relational database, as it does not aim to fulfill arbitrary relations while adhering to ACID properties. It also does not operate as an object database that seeks to seamlessly map object reference structures. Additionally, it does not introduce a novel abstraction like document orientation. Essentially, it serves as a large, distributed, durable, and fault-tolerant hash table. For applications leveraging an Object-Relational (O/R) mapper such as ActiveRecord or Hibernate, this can lead to improved horizontal scalability and significantly enhanced availability, albeit with a considerable trade-off in convenience. In the context of extensive applications facing the demands of internet-level scalability, a system is often comprised of multiple functionally divided services or APIs, which may handle storage across various data centers with their own horizontally partitioned storage systems. In these scenarios, the possibility of performing arbitrary joins within the database becomes impractical, as not all data can be accessed within a single database instance, making data management even more complex. Consequently, developers must adapt their strategies to navigate these limitations effectively.

When seeking a database that ensures both scalability and high availability without sacrificing performance, Apache Cassandra stands out as an ideal option. Its linear scalability paired with proven fault tolerance on standard hardware or cloud services positions it as an excellent choice for handling mission-critical data effectively. Additionally, Cassandra's superior capability to replicate data across several datacenters not only enhances user experience by reducing latency but also offers reassurance in the event of regional failures. This combination of features makes it a robust solution for organizations that prioritize data resilience and efficiency.

JanusGraph stands out as a highly scalable graph database designed for efficiently storing and querying extensive graphs that can comprise hundreds of billions of vertices and edges, all managed across a cluster of multiple machines. This project, which operates under The Linux Foundation, boasts contributions from notable organizations such as Expero, Google, GRAKN.AI, Hortonworks, IBM, and Amazon. It offers both elastic and linear scalability to accommodate an expanding data set and user community. Key features include robust data distribution and replication methods to enhance performance and ensure fault tolerance. Additionally, JanusGraph supports multi-datacenter high availability and provides hot backups for data security. All these capabilities are available without any associated costs, eliminating the necessity for purchasing commercial licenses, as it is entirely open source and governed by the Apache 2 license. Furthermore, JanusGraph functions as a transactional database capable of handling thousands of simultaneous users performing complex graph traversals in real time. It ensures support for both ACID properties and eventual consistency, catering to various operational needs. Beyond online transactional processing (OLTP), JanusGraph also facilitates global graph analytics (OLAP) through its integration with Apache Spark, making it a versatile tool for data analysis and visualization. This combination of features makes JanusGraph a powerful choice for organizations looking to leverage graph data effectively.

Flume is a dependable and distributed service designed to efficiently gather, aggregate, and transport significant volumes of log data. Its architecture is straightforward and adaptable, centered on streaming data flows, which enhances its usability. The system is built to withstand faults and includes various mechanisms for recovery and adjustable reliability features. Additionally, it employs a simple yet extensible data model that supports online analytic applications effectively. The Apache Flume team is excited to announce the launch of Flume version 1.8.0, which continues to enhance its capabilities. This version further solidifies Flume's role as a reliable tool for managing large-scale streaming event data efficiently.

Red Hat® Data Grid is a robust, in-memory distributed NoSQL database solution designed for high-performance applications. By enabling your applications to access, process, and analyze data at lightning-fast in-memory speeds, it ensures an exceptional user experience. With its elastic scalability and constant availability, users can quickly retrieve information through efficient, low-latency data processing that leverages RAM and parallel execution across distributed nodes. The system achieves linear scalability by partitioning and distributing data among cluster nodes, while also providing high availability through data replication. Fault tolerance is ensured via cross-datacenter geo-replication and clustering, making recovery from disasters seamless. Furthermore, the platform offers development flexibility and boosts productivity with its versatile and functionally rich NoSQL capabilities. Comprehensive data security features, including encryption and role-based access, are also included. Notably, the release of Data Grid 7.3.10 brings important security enhancements to address a known CVE. It is crucial for users to upgrade any existing Data Grid 7.3 installations to version 7.3.10 promptly to maintain security and performance standards. Regular updates ensure that the system remains resilient and up-to-date with the latest technological advancements.

Infinispan is an open-source, in-memory data grid that provides versatile deployment possibilities and powerful functionalities for data storage, management, and processing. This technology features a key/value data repository capable of accommodating various data types, ranging from Java objects to simple text. Infinispan ensures high availability and fault tolerance by distributing data across elastically scalable clusters, making it suitable for use as either a volatile cache or a persistent data solution. By positioning data closer to the application logic, Infinispan enhances application performance through reduced latency and improved throughput. As a Java library, integrating Infinispan into your project is straightforward; all you need to do is include it in your application's dependencies, allowing you to efficiently manage data within the same memory environment as your executing code. Furthermore, its flexibility makes it an ideal choice for developers seeking to optimize data access in high-demand scenarios.

It facilitates device connectivity through widely accepted IoT protocols such as MQTT, CoAP, and HTTP, accommodating both cloud and local installations. ThingsBoard is engineered for scalability, reliability, and high performance, ensuring that your data remains secure and intact. You can provision, monitor, and manage your IoT devices securely by utilizing comprehensive server-side APIs. Establish connections among your devices, assets, customers, or other entities with ease. Efficiently gather and archive telemetry data in a scalable and resilient manner. You can visualize your data using either built-in or personalized widgets and adaptable dashboards, which can also be shared with clients. The platform allows you to create data processing rule chains, enabling you to transform and standardize your device data. It can trigger alerts based on incoming telemetry events, updates to attributes, periods of device inactivity, and user interactions. Build a ThingsBoard cluster to achieve maximum scalability and fault tolerance through a microservices architecture. Furthermore, ThingsBoard accommodates both cloud and on-premises setups, making it a versatile choice for various deployment needs. This flexibility ensures that users can tailor their IoT solutions according to specific requirements and preferences.

Amazon Aurora is a cloud-based relational database that is compatible with both MySQL and PostgreSQL, merging the high performance and reliability of traditional enterprise databases with the ease and affordability of open-source solutions. Its performance surpasses that of standard MySQL databases by as much as five times and outpaces standard PostgreSQL databases by three times. Additionally, it offers the security, availability, and dependability synonymous with commercial databases, all at a fraction of the cost—specifically, one-tenth. Fully managed by the Amazon Relational Database Service (RDS), Aurora simplifies operations by automating essential tasks such as hardware provisioning, database configuration, applying patches, and conducting backups. The database boasts a self-healing, fault-tolerant storage system that automatically scales to accommodate up to 64TB for each database instance. Furthermore, Amazon Aurora ensures high performance and availability through features like the provision of up to 15 low-latency read replicas, point-in-time recovery options, continuous backups to Amazon S3, and data replication across three distinct Availability Zones, which enhances data resilience and accessibility. This combination of features makes Amazon Aurora an appealing choice for businesses looking to leverage the cloud for their database needs while maintaining robust performance and security.

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.

Hyperledger Iroha is crafted to be straightforward and seamlessly integrated into infrastructure or IoT initiatives that demand distributed ledger technology. It showcases an uncomplicated framework, a modular design driven by domain specificity in C++, a focus on developing client applications, and introduces a novel consensus algorithm known as YAC, which is fault-tolerant. As a user-friendly blockchain platform, Hyperledger Iroha enables the creation of reliable, secure, and efficient applications, leveraging the advantages of a permissioned blockchain coupled with its crash fault-tolerant consensus mechanism. The platform is open-source and free to use, compatible with both Linux and Mac OS, and supports a variety of libraries for mobile and desktop environments. Hyperledger Iroha serves as a versatile permissioned blockchain solution, capable of managing digital assets, identities, and serialized data efficiently. Its potential applications span across multiple sectors, including interbank settlements, central bank digital currencies, payment solutions, national identification systems, and logistics management, making it a valuable asset in the evolving tech landscape. Notably, its design allows for scalability and adaptability, ensuring that it can meet the diverse needs of modern applications.

Regardless of whether your project requires SIL 1-3, fault-tolerant systems, or triple modular redundancy (TMR) architectures, we are equipped to meet your safety instrumented system (SIS) needs. Our AADvance® fault-tolerant control system stands out as a fully distributed and scalable safety instrumented solution. This innovative AADvance system permits you to implement varying levels of module redundancy tailored to the specific demands of different segments of your application. Consequently, you can define the necessary levels of safety integrity and availability across your entire facility. Additionally, our AADvance training courses offer an in-depth look at the hardware, software, and troubleshooting aspects of AADvance. We also provide supplementary resources to ensure that your AADvance implementation and training requirements are met in a timely and effective manner. Gain insights into how AADvance functions as a fail-safe controller, grasp the configuration limits of the system, and learn how to design and assemble a complete system while selecting the most suitable hardware based on I/O specifications. Furthermore, our commitment to customer support ensures that you can navigate any challenges that arise during implementation with confidence.

Ensure your business applications are continuously accessible by leveraging a dependable database system designed for enterprise-level, high-volume workloads with exceptional fault tolerance. The CA Datacom® suite, along with its various rDBMS offerings, serves as the robust repository you need. This resilient database system takes advantage of zIIP specialty processor technology, resulting in enhanced and economically efficient database management. It facilitates seamless integration with mobile-to-mainframe initiatives, cloud services, web applications, and big data analytics through JDBC and ODBC interfaces. Effectively handle high-volume workloads, while each new version incorporates advanced hardware technologies and refined memory optimization strategies. Database Administrators and Systems Programmers can easily monitor and manage their environment by querying the Dynamic System Tables within a Multi-User Facility region on a specific LPAR, utilizing contemporary tools. Furthermore, modern developers who may be new to the mainframe environment can efficiently manage their source code using popular modern IDEs like Visual Studio Code or Eclipse CHE, bridging the gap between traditional and contemporary development practices. This capability not only enhances productivity but also fosters innovation within the organization.