DeltaStream Alternatives

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, 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. • Gain critical real-time insights to run your business • Seamlessly integrate data streaming and batch data • High performance in throughput and low-latency at petabyte scale • Fully-managed cloud service • Tiered storage to optimize cloud performance & spend • Fully-secure & enterprise-ready

Data integration for your hybrid cloud. Modern, reliable data integration across your private and public cloud. All in real-time with change data capture and data streams. Built by the executive & technical team from GoldenGate Software, Striim brings decades of experience in mission-critical enterprise workloads. Striim scales out as a distributed platform in your environment or in the cloud. Scalability is fully configurable by your team. Striim is fully secure with HIPAA and GDPR compliance. Built ground up for modern enterprise workloads in the cloud or on-premise. Drag and drop to create data flows between your sources and targets. Process, enrich, and analyze your streaming data with real-time SQL queries.

Streaming service is a real-time, serverless, Apache Kafka-compatible event streaming platform for developers and data scientists. Streaming is tightly integrated with Oracle Cloud Infrastructure (OCI), Database, GoldenGate, and Integration Cloud. The service also provides out-of-the-box integrations for hundreds of third-party products across categories such as DevOps, databases, big data, and SaaS applications. Data engineers can easily set up and operate big data pipelines. Oracle handles all infrastructure and platform management for event streaming, including provisioning, scaling, and security patching. With the help of consumer groups, Streaming can provide state management for thousands of consumers. This helps developers easily build applications at scale.

AI-powered Informatica Data Engineering Streaming enables data engineers to ingest, process, and analyze real-time streaming data for actionable insights. Advanced serverless deployment option​ with integrated metering dashboard cuts admin overhead. Rapidly build intelligent data pipelines with CLAIRE®-powered automation, including automatic change data capture (CDC). Ingest thousands of databases and millions of files, and streaming events. Efficiently ingest databases, files, and streaming data for real-time data replication and streaming analytics. Find and inventory all data assets throughout your organization. Intelligently discover and prepare trusted data for advanced analytics and AI/ML projects.

Unified stream and batch data processing that's serverless, fast, and cost-effective. Fully managed data processing service. Automated provisioning and management of processing resources. Horizontal autoscaling of worker resources to maximize resource utilization. OSS community-driven innovation with Apache Beam SDK. Reliable and consistent exactly-once processing. Streaming data analytics with speed. Dataflow enables fast, simplified streaming data pipeline development with lower data latency. Allow teams to focus on programming instead of managing server clusters as Dataflow’s serverless approach removes operational overhead from data engineering workloads. Allow teams to focus on programming instead of managing server clusters as Dataflow’s serverless approach removes operational overhead from data engineering workloads. Dataflow automates provisioning and management of processing resources to minimize latency and maximize utilization.

Infinite retention for Apache Kafka® with Confluent. Be infrastructure-enabled, not infrastructure-restricted Legacy technologies require you to choose between being real-time or highly-scalable. Event streaming enables you to innovate and win - by being both real-time and highly-scalable. Ever wonder how your rideshare app analyzes massive amounts of data from multiple sources to calculate real-time ETA? Ever wonder how your credit card company analyzes millions of credit card transactions across the globe and sends fraud notifications in real-time? The answer is event streaming. Move to microservices. Enable your hybrid strategy through a persistent bridge to cloud. Break down silos to demonstrate compliance. Gain real-time, persistent event transport. The list is endless.

Cloudera DataFlow for the Public Cloud (CDF-PC) is a cloud-native universal data distribution service powered by Apache NiFi ​​that lets developers connect to any data source anywhere with any structure, process it, and deliver to any destination. CDF-PC offers a flow-based low-code development paradigm that aligns best with how developers design, develop, and test data distribution pipelines. With over 400+ connectors and processors across the ecosystem of hybrid cloud services—including data lakes, lakehouses, cloud warehouses, and on-premises sources—CDF-PC provides indiscriminate data distribution. These data distribution flows can then be version-controlled into a catalog where operators can self-serve deployments to different runtimes.

Easily collect, process, and analyze video and data streams in real time. Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin. Amazon Kinesis enables you to ingest, buffer, and process streaming data in real-time, so you can derive insights in seconds or minutes instead of hours or days.

Amazon MSK is a fully managed service that makes it easy for you to build and run applications that use Apache Kafka to process streaming data. Apache Kafka is an open-source platform for building real-time streaming data pipelines and applications. With Amazon MSK, you can use native Apache Kafka APIs to populate data lakes, stream changes to and from databases, and power machine learning and analytics applications. Apache Kafka clusters are challenging to setup, scale, and manage in production. When you run Apache Kafka on your own, you need to provision servers, configure Apache Kafka manually, replace servers when they fail, orchestrate server patches and upgrades, architect the cluster for high availability, ensure data is durably stored and secured, setup monitoring and alarms, and carefully plan scaling events to support load changes.

Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale. Any kind of data is produced as a stream of events. Credit card transactions, sensor measurements, machine logs, or user interactions on a website or mobile application, all of these data are generated as a stream. Apache Flink excels at processing unbounded and bounded data sets. Precise control of time and state enable Flink’s runtime to run any kind of application on unbounded streams. Bounded streams are internally processed by algorithms and data structures that are specifically designed for fixed sized data sets, yielding excellent performance. Flink is designed to work well each of the previously listed resource managers.

Real-Time Analytics on Raw Data. Live ingest from S3, Kafka, DynamoDB & more. Explore raw data as SQL tables. Build amazing data-driven applications & live dashboards in minutes. Rockset is a serverless search and analytics engine that powers real-time apps and live dashboards. Operate directly on raw data, including JSON, XML, CSV, Parquet, XLSX or PDF. Plug data from real-time streams, data lakes, databases, and data warehouses into Rockset. Ingest real-time data without building pipelines. Rockset continuously syncs new data as it lands in your data sources without the need for a fixed schema. Use familiar SQL, including joins, filters, and aggregations. It’s blazing fast, as Rockset automatically indexes all fields in your data. Serve fast queries that power the apps, microservices, live dashboards, and data science notebooks you build. Scale without worrying about servers, shards, or pagers.

Enable everyone to discover and observe streaming data. Sharing, documenting and cataloging your data can increase productivity by up to 95%. Then from data, build apps for production use cases. Apply a data-centric security model to cover all the gaps of open source technology, and address data privacy. Provide secure and low-code data pipeline capabilities. Eliminate all darkness and offer unparalleled observability in data and apps. Unify your data mesh and data technologies and be confident with open source in production. Lenses is the highest rated product for real-time stream analytics according to independent third party reviews. With feedback from our community and thousands of engineering hours invested, we've built features that ensure you can focus on what drives value from your real time data. Deploy and run SQL-based real time applications over any Kafka Connect or Kubernetes infrastructure including AWS EKS.

Apache Kafka® is an open-source, distributed streaming platform. Scale production clusters up to a thousand brokers, trillions of messages per day, petabytes of data, hundreds of thousands of partitions. Elastically expand and contract storage and processing. Stretch clusters efficiently over availability zones or connect separate clusters across geographic regions. Process streams of events with joins, aggregations, filters, transformations, and more, using event-time and exactly-once processing. Kafka’s out-of-the-box Connect interface integrates with hundreds of event sources and event sinks including Postgres, JMS, Elasticsearch, AWS S3, and more. Read, write, and process streams of events in a vast array of programming languages.

Nussknacker is a low-code visual tool for domain experts to define and run real-time decisioning algorithms instead of implementing them in the code. It serves where real-time actions on data have to be made: real-time marketing, fraud detection, Internet of Things, Customer 360, and Machine Learning inferring. An essential part of Nussknacker is a visual design tool for decision algorithms. It allows not-so-technical users – analysts or business people – to define decision logic in an imperative, easy-to-follow, and understandable way. Once authored, with a click of a button, scenarios are deployed for execution. And can be changed and redeployed anytime there’s a need. Nussknacker supports two processing modes: streaming and request-response. In streaming mode, it uses Kafka as its primary interface. It supports both stateful and stateless processing.

Responsive applications keep users engaged and developers inspired. Rise to meet these ever-increasing expectations with the DataStax Astra Streaming service platform. DataStax Astra Streaming is a cloud-native messaging and event streaming platform powered by Apache Pulsar. Astra Streaming allows you to build streaming applications on top of an elastically scalable, multi-cloud messaging and event streaming platform. Astra Streaming is powered by Apache Pulsar, the next-generation event streaming platform which provides a unified solution for streaming, queuing, pub/sub, and stream processing. Astra Streaming is a natural complement to Astra DB. Using Astra Streaming, existing Astra DB users can easily build real-time data pipelines into and out of their Astra DB instances. With Astra Streaming, avoid vendor lock-in and deploy on any of the major public clouds (AWS, GCP, Azure) compatible with open-source Apache Pulsar.

Built on open-source Apache Kafka, IBM® Event Streams is an event-streaming platform that helps you build smart apps that can react to events as they happen. Event Streams is based on years of IBM operational expertise gained from running Apache Kafka event streams for enterprises. This makes Event Streams ideal for mission-critical workloads. With connectors to a wide range of core systems and a scalable REST API, you can extend the reach of your existing enterprise assets. Rich security and geo-replication aids disaster recovery. Take advantage of IBM productivity tools and use the CLI to ensure best practices. Replicate data between Event Streams deployments in a disaster-recovery situation.

Materialize is a reactive database that delivers incremental view updates. We help developers easily build with streaming data using standard SQL. Materialize can connect to many different external sources of data without pre-processing. Connect directly to streaming sources like Kafka, Postgres databases, CDC, or historical sources of data like files or S3. Materialize allows you to query, join, and transform data sources in standard SQL - and presents the results as incrementally-updated Materialized views. Queries are maintained and continually updated as new data streams in. With incrementally-updated views, developers can easily build data visualizations or real-time applications. Building with streaming data can be as simple as writing a few lines of SQL.

Event Hubs is a fully managed, real-time data ingestion service that’s simple, trusted, and scalable. Stream millions of events per second from any source to build dynamic data pipelines and immediately respond to business challenges. Keep processing data during emergencies using the geo-disaster recovery and geo-replication features. Integrate seamlessly with other Azure services to unlock valuable insights. Allow existing Apache Kafka clients and applications to talk to Event Hubs without any code changes—you get a managed Kafka experience without having to manage your own clusters. Experience real-time data ingestion and microbatching on the same stream. Focus on drawing insights from your data instead of managing infrastructure. Build real-time big data pipelines and respond to business challenges right away.

Spark Streaming brings Apache Spark's language-integrated API to stream processing, letting you write streaming jobs the same way you write batch jobs. It supports Java, Scala and Python. Spark Streaming recovers both lost work and operator state (e.g. sliding windows) out of the box, without any extra code on your part. By running on Spark, Spark Streaming lets you reuse the same code for batch processing, join streams against historical data, or run ad-hoc queries on stream state. Build powerful interactive applications, not just analytics. Spark Streaming is developed as part of Apache Spark. It thus gets tested and updated with each Spark release. You can run Spark Streaming on Spark's standalone cluster mode or other supported cluster resource managers. It also includes a local run mode for development. In production, Spark Streaming uses ZooKeeper and HDFS for high availability.

Scale from zero to millions of events per second. Arroyo ships as a single, compact binary. Run locally on MacOS or Linux for development, and deploy to production with Docker or Kubernetes. Arroyo is a new kind of stream processing engine, built from the ground up to make real-time easier than batch. Arroyo was designed from the start so that anyone with SQL experience can build reliable, efficient, and correct streaming pipelines. Data scientists and engineers can build end-to-end real-time applications, models, and dashboards, without a separate team of streaming experts. Transform, filter, aggregate, and join data streams by writing SQL, with sub-second results. Your streaming pipelines shouldn't page someone just because Kubernetes decided to reschedule your pods. Arroyo is built to run in modern, elastic cloud environments, from simple container runtimes like Fargate to large, distributed deployments on the Kubernetes logo Kubernetes.

SQLstream ranks #1 for IoT stream processing & analytics (ABI Research). Used by Verizon, Walmart, Cisco, & Amazon, our technology powers applications across data centers, the cloud, & the edge. Thanks to sub-ms latency, SQLstream enables live dashboards, time-critical alerts, & real-time action. Smart cities can optimize traffic light timing or reroute ambulances & fire trucks. Security systems can shut down hackers & fraudsters right away. AI / ML models, trained by streaming sensor data, can predict equipment failures. With lightning performance, up to 13M rows / sec / CPU core, companies have drastically reduced their footprint & cost. Our efficient, in-memory processing permits operations at the edge that are otherwise impossible. Acquire, prepare, analyze, & act on data in any format from any source. Create pipelines in minutes not months with StreamLab, our interactive, low-code GUI dev environment. Export SQL scripts & deploy with the flexibility of Kubernetes.

Turn your data into a realtime stream, making it immediately available and ready to use. Leo reduces the complexity of event sourcing by making it easy to create, visualize, monitor, and maintain your data flows. Once you unlock your data, you are no longer limited by the constraints of your legacy systems. Dramatically reduced dev time keeps your developers and stakeholders happy. Adopt microservice architectures to continuously innovate and improve agility. In reality, success with microservices is all about data. An organization must invest in a reliable and repeatable data backbone to make microservices a reality. Implement full-fledged search in your custom app. With data flowing, adding and maintaining a search database will not be a burden.

Breakthrough data streaming capabilities that let you deliver customer experiences never before possible. Kafka API and ecosystem are compatible. Redpanda BulletPredictable low latencies with zero data loss. Redpanda BulletUpto 10x faster than Kafka. Redpanda BulletEnterprise-grade support and hotfixes. Redpanda BulletAutomated backups to S3/GCS. Redpanda Bullet100% freedom from routine Kafka operations. Redpanda BulletSupport for AWS and GCP. Redpanda was designed from the ground up to be easily installed to get streaming up and running quickly. After you see its power, put Redpanda to the test in production. Use the more advanced Redpanda features. We manage provisioning, monitoring, and upgrades. Without any access to your cloud credentials. Sensitive data never leaves your environment. Provisioned, operated, and maintained for you. Configurable instance types. Expand cluster as your needs grow.

Samza allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka. Battle-tested at scale, it supports flexible deployment options to run on YARN or as a standalone library. Samza provides extremely low latencies and high throughput to analyze your data instantly. Scales to several terabytes of state with features like incremental checkpoints and host-affinity. Samza is easy to operate with flexible deployment options - YARN, Kubernetes or standalone. Ability to run the same code to process both batch and streaming data. Integrates with several sources including Kafka, HDFS, AWS Kinesis, Azure Eventhubs, K-V stores and ElasticSearch.

Red Hat® OpenShift® Streams for Apache Kafka is a managed cloud service that provides a streamlined developer experience for building, deploying, and scaling new cloud-native applications or modernizing existing systems. Red Hat OpenShift Streams for Apache Kafka makes it easy to create, discover, and connect to real-time data streams no matter where they are deployed. Streams are a key component for delivering event-driven and data analytics applications. The combination of seamless operations across distributed microservices, large data transfer volumes, and managed operations allows teams to focus on team strengths, speed up time to value, and lower operational costs. OpenShift Streams for Apache Kafka includes a Kafka ecosystem and is part of a family of cloud services—and the Red Hat OpenShift product family—which helps you build a wide range of data-driven solutions.

Streaming data from operations, transactions, sensors and IoT devices is valuable – when it's well-understood. Event stream processing from SAS includes streaming data quality and analytics – and a vast array of SAS and open source machine learning and high-frequency analytics for connecting, deciphering, cleansing and understanding streaming data – in one solution. No matter how fast your data moves, how much data you have, or how many data sources you’re pulling from, it’s all under your control via a single, intuitive interface. You can define patterns and address scenarios from all aspects of your business, giving you the power to stay agile and tackle issues as they arise.

StreamSets DataOps Platform. The data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps, and power modern analytics and hybrid integration. Only StreamSets provides a single design experience for all design patterns for 10x greater developer productivity; smart data pipelines that are resilient to change for 80% less breakages; and a single pane of glass for managing and monitoring all pipelines across hybrid and cloud architectures to eliminate blind spots and control gaps. With StreamSets, you can deliver the continuous data that drives the connected enterprise.

Deliver Continuous Intelligence solutions easier, faster, and cost-effectively - with less engineering effort. The Cogility Cogynt platform delivers cloud-scalable event stream processing software powered by advanced, Expert AI-based analytics. A complete, integrated toolset enables organizations to quickly, easily, and more efficiently deliver continuous intelligence solutions. The end-to-end platform streamlines deployment, constructing model logic, customizing data source intake, processing data streams, examining, visualizing and sharing intelligence findings, auditing and improving results, and integrating with other applications. Cogynt’s Authoring Tool provides a convenient, zero-code design environment for creating, updating, and deploying data models. Cogynt’s Data Management Tool makes it easy to publish your model to immediately apply to stream data processing while abstracting Flink job coding.

We deliver a geo-distributed real-time database, stream processing and compute runtime for event-driven applications across up to 175 worldwide edge data centers. App & API builders love our platform because we solve the hardest problems of sharing mutable state across 100s of global locations, with strong consistency & low latency. Macrometa enables you to surgically extend your existing infrastructure to bring part of or your entire application closer to your end users. This allows you to improve performance, user experience, and comply with global data governance laws. Macrometa is a serverless, streaming NoSQL database, with integrated pub/sub and stream data processing and compute engine. Create stateful data infrastructure, stateful functions & containers for long running workloads, and process data streams in real time. You do the code, we do all the ops and orchestration.

Apache Doris is a modern data warehouse for real-time analytics. It delivers lightning-fast analytics on real-time data at scale. Push-based micro-batch and pull-based streaming data ingestion within a second. Storage engine with real-time upsert, append and pre-aggregation. Optimize for high-concurrency and high-throughput queries with columnar storage engine, MPP architecture, cost based query optimizer, vectorized execution engine. Federated querying of data lakes such as Hive, Iceberg and Hudi, and databases such as MySQL and PostgreSQL. Compound data types such as Array, Map and JSON. Variant data type to support auto data type inference of JSON data. NGram bloomfilter and inverted index for text searches. Distributed design for linear scalability. Workload isolation and tiered storage for efficient resource management. Supports shared-nothing clusters as well as separation of storage and compute.

Powered by Apache Doris, VeloDB is a modern data warehouse for lightning-fast analytics on real-time data at scale. Push-based micro-batch and pull-based streaming data ingestion within seconds. Storage engine with real-time upsert、append and pre-aggregation. Unparalleled performance in both real-time data serving and interactive ad-hoc queries. Not just structured but also semi-structured data. Not just real-time analytics but also batch processing. Not just run queries against internal data but also work as a federate query engine to access external data lakes and databases. Distributed design to support linear scalability. Whether on-premise deployment or cloud service, separation or integration of storage and compute, resource usage can be flexibly and efficiently adjusted according to workload requirements. Built on and fully compatible with open source Apache Doris. Support MySQL protocol, functions, and SQL for easy integration with other data tools.

Baidu Stream Computing (BSC) provides real-time streaming data processing capacity with low delay, high throughput and high accuracy. It is fully compatible with Spark SQL; and can realize the logic data processing of complicated businesses through SQL statement, which is easy to use; provides users with full life cycle management for the streaming-oriented computing jobs. Integrate deeply with multiple storage products of Baidu AI Cloud as the upstream and downstream of stream computing, including Baidu Kafka, RDS, BOS, IOT Hub, Baidu ElasticSearch, TSDB, SCS and others. Provide a comprehensive job monitoring indicator, and the user can view the monitoring indicators of the job and set the alarm rules to protect the job.

Now that your data is in motion, it’s time to make sense of it. Stream processing enables you to derive instant insights from your data streams, but setting up the infrastructure to support it can be complex. That’s why Confluent developed ksqlDB, the database purpose-built for stream processing applications. Make your data immediately actionable by continuously processing streams of data generated throughout your business. ksqlDB’s intuitive syntax lets you quickly access and augment data in Kafka, enabling development teams to seamlessly create real-time innovative customer experiences and fulfill data-driven operational needs. ksqlDB offers a single solution for collecting streams of data, enriching them, and serving queries on new derived streams and tables. That means less infrastructure to deploy, maintain, scale, and secure. With less moving parts in your data architecture, you can focus on what really matters -- innovation.

IBM Streams evaluates a broad range of streaming data — unstructured text, video, audio, geospatial and sensor — helping organizations spot opportunities and risks and make decisions in real-time. Make sense of your data, turning fast-moving volumes and varieties into insight with IBM® Streams. Streams evaluate a broad range of streaming data — unstructured text, video, audio, geospatial and sensor — helping organizations spot opportunities and risks as they happen. Combine Streams with other IBM Cloud Pak® for Data capabilities, built on an open, extensible architecture. Help enable data scientists to collaboratively build models to apply to stream flows, plus, analyze massive amounts of data in real-time. Acting upon your data and deriving true value is easier than ever.

No more low level code and stitching together complex systems. Build and deploy pipelines in minutes with SQL. A data engineering service that makes it easy for developers and data engineers to build and deploy real-time data pipelines for data-driven applications. Pre-built connectors for messaging systems, storage systems, and database engines make it easy to connect and discover available data. For each connection you make, you get a stream to or from the system. With Decodable you can build your pipelines with SQL. Pipelines use streams to send data to, or receive data from, your connections. You can also use streams to connect pipelines together to handle the most complex processing tasks. Observe your pipelines to ensure data keeps flowing. Create curated streams for other teams. Define retention policies on streams to avoid data loss during external system failures. Real-time health and performance metrics let you know everything’s working.

Discover Azure Stream Analytics, the easy-to-use, real-time analytics service that is designed for mission-critical workloads. Build an end-to-end serverless streaming pipeline with just a few clicks. Go from zero to production in minutes using SQL—easily extensible with custom code and built-in machine learning capabilities for more advanced scenarios. Run your most demanding workloads with the confidence of a financially backed SLA.

Analyze, continuously query, and act on IoT and other streaming data at lightning fast speeds. Take real-time operations and analytics to the next level with intelligent applications that deploy quickly for taking action based on new decisions and models, all without extra overhead. TIBCO® Streaming software is enterprise-grade, cloud-ready streaming analytics for quickly building real-time applications at a fraction of the cost and risk of alternatives.

Apache Kafka as a fully managed service, with zero vendor lock-in and a full set of capabilities to build your streaming pipeline. Set up fully managed Kafka in less than 10 minutes — directly from our web console or programmatically via our API, CLI, Terraform provider or Kubernetes operator. Easily connect it to your existing tech stack with over 30 connectors, and feel confident in your setup with logs and metrics available out of the box via the service integrations. A fully managed distributed data streaming platform, deployable in the cloud of your choice. Ideal for event-driven applications, near-real-time data transfer and pipelines, stream analytics, and any other case where you need to move a lot of data between applications — and quickly. With Aiven’s hosted and managed-for-you Apache Kafka, you can set up clusters, deploy new nodes, migrate clouds, and upgrade existing versions — in a single mouse click — and monitor them through a simple dashboard.

Apache Storm is a free and open source distributed realtime computation system. Apache Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Apache Storm is simple, can be used with any programming language, and is a lot of fun to use! Apache Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Apache Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate. Apache Storm integrates with the queueing and database technologies you already use. An Apache Storm topology consumes streams of data and processes those streams in arbitrarily complex ways, repartitioning the streams between each stage of the computation however needed. Read more in the tutorial.

Simplifies data exchange between components in microservice architectures. When used as a transport for microservices, it simplifies integration, increases reliability, and improves scaling. Read and write data in near real-time. Set data throughput and storage times to meet your needs. Enjoy granular configuration of the resources for processing data streams, from small streams of 100 KB/s to streams of 100 MB/s. Deliver a single stream to multiple targets with different retention policies using Yandex Data Transfer. Data is automatically replicated across multiple geographically distributed availability zones. Once created, you can manage data streams centrally in the management console or using the API. Yandex Data Streams can continuously collect data from sources like website browsing histories, application and system logs, and social media feeds. Yandex Data Streams is capable of continuously collecting data from sources such as website browsing histories, application logs, etc.

A scalable cloud database for real-time analysis on large and streaming datasets. Kinetica is designed to harness modern vectorized processors to be orders of magnitude faster and more efficient for real-time spatial and temporal workloads. Track and gain intelligence from billions of moving objects in real-time. Vectorization unlocks new levels of performance for analytics on spatial and time series data at scale. Ingest and query at the same time to act on real-time events. Kinetica's lockless architecture and distributed ingestion ensures data is available to query as soon as it lands. Vectorized processing enables you to do more with less. More power allows for simpler data structures, which lead to lower storage costs, more flexibility and less time engineering your data. Vectorized processing opens the door to amazingly fast analytics and detailed visualization of moving objects at scale.

Aiven manages your open source data infrastructure in the cloud - so you don't have to. Developers can do what they do best: create applications. We do what we do best: manage cloud data infrastructure. All solutions are open source. You can also freely move data between clouds or create multi-cloud environments. Know exactly how much you’ll be paying and why. We bundle networking, storage and basic support costs together. We are committed to keeping your Aiven software online. If there’s ever an issue, we’ll be there to fix it. Deploy a service on the Aiven platform in 10 minutes. Sign up - no credit card info needed. Select your open source service, and the cloud and region to deploy to. Choose your plan - you have $300 in free credits. Click "Create service" and go on to configure your data sources. Stay in control of your data using powerful open-source services.

The easiest way to do batch and streaming data processing. Write once, run anywhere data processing for mission-critical production workloads. Beam reads your data from a diverse set of supported sources, no matter if it’s on-prem or in the cloud. Beam executes your business logic for both batch and streaming use cases. Beam writes the results of your data processing logic to the most popular data sinks in the industry. A simplified, single programming model for both batch and streaming use cases for every member of your data and application teams. Apache Beam is extensible, with projects such as TensorFlow Extended and Apache Hop built on top of Apache Beam. Execute pipelines on multiple execution environments (runners), providing flexibility and avoiding lock-in. Open, community-based development and support to help evolve your application and meet the needs of your specific use cases.

Solace PubSub+ Platform helps enterprises design, deploy and manage event-driven systems across hybrid and multi-cloud and IoT environments so they can be more event-driven and operate in real-time. The PubSub+ Platform includes the powerful PubSub+ Event Brokers, event management capabilities with PubSub+ Event Portal, as well as monitoring and integration capabilities all available via a single cloud console. PubSub+ allows easy creation of an event mesh, an interconnected network of event brokers, allowing for seamless and dynamic data movement across highly distributed network environments. PubSub+ Event Brokers can be deployed as fully managed cloud services, self-managed software in private cloud or on-premises environments, or as turnkey hardware appliances for unparalleled performance and low TCO. PubSub+ Event Portal is a complimentary toolset for design and governance of event-driven systems including both Solace and Kafka-based event broker environments.

The Flowcore platform provides you with event streaming and event sourcing in a single, easy-to-use service. Data flow and replayable storage, designed for developers at data-driven startups and enterprises that aim to stay at the forefront of innovation and growth. All your data operations are efficiently persisted, ensuring no valuable data is ever lost. Immediate transformations and reclassifications of your data, loading it seamlessly to any required destination. Break free from rigid data structures. Flowcore's scalable architecture adapts to your growth, handling increasing volumes of data with ease. By simplifying and streamlining backend data processes, your engineering teams can focus on what they do best, creating innovative products. Integrate AI technologies more effectively, enriching your products with smart, data-driven solutions. Flowcore is built with developers in mind, but its benefits extend beyond the dev team.

In the emerging digital world, billions of people, systems, and devices will interact in real-time, suggesting new and disruptive competitive advantages. How do you play there? By building distributed, stateful, rule-based event-processing systems, experimenting, learning, and quickly evolving. Data is arriving in real-time from hundreds of internal and external sources—and it has shelf-life; its value diminishes over time. With TIBCO, you can jump-start big data processing initiatives that give you the ability to sense, reason, respond, and visualize—much different and much faster and smarter than the traditional store, analyze, report, act approach. With multiple rule authoring environments, you support collaboration between IT developers and business professionals.

Digital businesses must design applications that allow the business to be proactive not reactive. This requires the ability to process huge amounts of data in real time to identify meaningful events while there is still time to influence the outcome—whether to mitigate risk or improve the customer experience or operations. TIBCO Cloud™ Events service is a cloud-native solution for improving business outcomes by detecting key events and automating the next best action. Build declarative rules without worrying about the underlying plumbing. Define and capture key business conditions, and automate actions. TIBCO Cloud Events service takes care of the execution and matching of related data.

Microservice-based streaming and batch data processing for Cloud Foundry and Kubernetes. Spring Cloud Data Flow provides tools to create complex topologies for streaming and batch data pipelines. The data pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. Spring Cloud Data Flow supports a range of data processing use cases, from ETL to import/export, event streaming, and predictive analytics. The Spring Cloud Data Flow server uses Spring Cloud Deployer, to deploy data pipelines made of Spring Cloud Stream or Spring Cloud Task applications onto modern platforms such as Cloud Foundry and Kubernetes. A selection of pre-built stream and task/batch starter apps for various data integration and processing scenarios facilitate learning and experimentation. Custom stream and task applications, targeting different middleware or data services, can be built using the familiar Spring Boot style programming model.

Akka is a toolkit for building highly concurrent, distributed, and resilient message-driven applications for Java and Scala. Akka Insights is intelligent monitoring and observability purpose-built for Akka. Actors and Streams let you build systems that scale up, using the resources of a server more efficiently, and out, using multiple servers. Building on the principles of The Reactive Manifesto Akka allows you to write systems that self-heal and stay responsive in the face of failures. Distributed systems without single points of failure. Load balancing and adaptive routing across nodes. Event Sourcing and CQRS with Cluster Sharding. Distributed Data for eventual consistency using CRDTs. Asynchronous non-blocking stream processing with backpressure. Fully async and streaming HTTP server and client provides a great platform for building microservices. Streaming integrations with Alpakka.

Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.