Alternatives to Redpanda

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.

Unified stream and batch data processing that is serverless, fast, cost-effective, and low-cost. Fully managed data processing service. Automated provisioning of and management of processing resource. Horizontal autoscaling worker resources to maximize resource use Apache Beam SDK is an open-source platform for community-driven innovation. Reliable, consistent processing that works exactly once. Streaming data analytics at lightning speed Dataflow allows for faster, simpler streaming data pipeline development and lower data latency. Dataflow's serverless approach eliminates the operational overhead associated with data engineering workloads. Dataflow allows teams to concentrate on programming and not managing server clusters. Dataflow's serverless approach eliminates operational overhead from data engineering workloads, allowing teams to concentrate on programming and not managing server clusters. Dataflow automates provisioning, management, and utilization of processing resources to minimize latency.

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.

Streaming service is a streaming service that allows developers and data scientists to stream real-time events. It is serverless and Apache Kafka compatible. Streaming can be integrated with Oracle Cloud Infrastructure, Database, GoldenGate, Integration Cloud, and Oracle Cloud Infrastructure (OCI). The service provides integrations for hundreds third-party products, including databases, big data, DevOps, and SaaS applications. Data engineers can easily create and manage big data pipelines. Oracle manages all infrastructure and platform management, including provisioning, scaling and security patching. Streaming can provide state management to thousands of consumers with the help of consumer groups. This allows developers to easily create applications on a large scale.

WarpStream, an Apache Kafka compatible data streaming platform, is built directly on object storage. It has no inter-AZ network costs, no disks that need to be managed, and it's infinitely scalable within your VPC. WarpStream is deployed in your VPC as a stateless, auto-scaling binary agent. No local disks are required to be managed. Agents stream data directly into and out of object storage without buffering on local drives and no data tiering. Instantly create new "virtual" clusters in our control plan. Support multiple environments, teams or projects without having to manage any dedicated infrastructure. WarpStream is Apache Kafka protocol compatible, so you can continue to use your favorite tools and applications. No need to rewrite or use a proprietary SDK. Simply change the URL of your favorite Kafka library in order to start streaming. Never again will you have to choose between budget and reliability.

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.

Materialize is a reactive database that provides incremental view updates. Our standard SQL allows developers to easily work with streaming data. Materialize connects to many external data sources without any pre-processing. Connect directly to streaming sources such as Kafka, Postgres databases and CDC or historical data sources such as 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 kept current and updated as new data streams are added. With incrementally-updated views, developers can easily build data visualizations or real-time applications. It is as easy as writing a few lines SQL to build with streaming data.

DeltaStream is an integrated serverless streaming processing platform that integrates seamlessly with streaming storage services. Imagine it as a compute layer on top your streaming storage. It offers streaming databases and streaming analytics along with other features to provide an integrated platform for managing, processing, securing and sharing streaming data. DeltaStream has a SQL-based interface that allows you to easily create stream processing apps such as streaming pipelines. It uses Apache Flink, a pluggable stream processing engine. DeltaStream is much more than a query-processing layer on top Kafka or Kinesis. It brings relational databases concepts to the world of data streaming, including namespacing, role-based access control, and enables you to securely access and process your streaming data, regardless of where it is stored.

Apache Flink is a distributed processing engine and framework for stateful computations using unbounded and bounded data streams. Flink can be used in all cluster environments and perform computations at any scale and in-memory speed. A stream of events can be used to produce any type of data. All data, including credit card transactions, machine logs, sensor measurements, and user interactions on a website, mobile app, are generated as streams. Apache Flink excels in processing both unbounded and bound data sets. Flink's runtime can run any type of application on unbounded stream streams thanks to its precise control of state and time. Bounded streams are internal processed by algorithms and data structure that are specifically designed to process fixed-sized data sets. This results in excellent performance. Flink can be used with all of the resource managers previously mentioned.

Apache Kafka®, with Confluent, has an infinite retention. Be infrastructure-enabled, not infrastructure-restricted Legacy technologies require you to choose between being real-time or highly-scalable. Event streaming allows you to innovate and win by being both highly-scalable and real-time. Ever wonder how your rideshare app analyses massive amounts of data from multiple sources in order to calculate real-time ETA. Wondering how your credit card company analyzes credit card transactions from all over the world and sends fraud notifications in real time? Event streaming is the answer. Microservices are the future. A persistent bridge to the cloud can enable your hybrid strategy. Break down silos to demonstrate compliance. Gain real-time, persistent event transport. There are many other options.

AI-powered Informatica Data Engineering streaming allows data engineers to ingest and process real-time streaming data in order to gain actionable insights.

Amazon MSK is a fully managed service that makes coding and running applications that use Apache Kafka for streaming data processing easy. Apache Kafka is an open source platform that allows you to build real-time streaming data applications and pipelines. Amazon MSK allows you to use native Apache Kafka APIs for populating data lakes, stream changes between databases, and to power machine learning or analytics applications. It is difficult to set up, scale, and manage Apache Kafka clusters in production. Apache Kafka clusters can be difficult to set up and scale on your own.

Azure Stream Analytics is an easy-to-use, real time analytics service that's designed for mission-critical workloads. In just a few steps, you can create an end-to-end streaming pipeline that is serverless in just a few clicks. SQL--easily extensible and customizable with custom code, built-in machine learning capabilities and more advanced scenarios. You can run the most complex workloads with confidence knowing that your SLA is financially backed.

IBM Streams analyzes a wide range of streaming data, including unstructured text, video and audio, and geospatial and sensor data. This helps organizations to spot opportunities and risks, and make decisions in real-time.

You can manage your data from the edge to the cloud with a simple, no-code approach to creating sophisticated streaming applications.

You can quickly collect, process, analyze, and analyze video and data streams. Amazon Kinesis makes it easy for you to quickly and easily collect, process, analyze, and interpret streaming data. Amazon Kinesis provides key capabilities to process streaming data at any scale cost-effectively, as well as the flexibility to select the tools that best fit your application's requirements. Amazon Kinesis allows you to ingest real-time data, including video, audio, website clickstreams, application logs, and IoT data for machine learning, analytics, or other purposes. Amazon Kinesis allows you to instantly process and analyze data, rather than waiting for all the data to be collected before processing can begin. Amazon Kinesis allows you to ingest buffer and process streaming data instantly, so you can get insights in seconds or minutes, instead of waiting for hours or days.

Xeotek is a powerful desktop and web application that helps companies explore and develop data streams and applications faster. Xeotek KaDeck was created for developers, business users, and operations personnel. KaDeck gives business users, developers, operations, and business users insight into data and processes. This benefits the entire team: less misunderstandings, less work, more transparency. Xeotek KaDeck gives you control over your data streams. You can save hours by getting insights at the application and data level in projects or your day-to-day operations. KaDeck makes it easy to export, filter, transform, and manage data streams. You can run JavaScript (NodeV4) code and transform & create test data. You can also view & modify consumer offsets. Manage your streams or topics, Kafka Connect instance, schema registry, ACLs, and Kafka Connect topics from one user interface.

Samza lets you build stateful applications that can process data in real time from multiple sources, including Apache Kafka. It has been battle-tested at scale and supports flexible deployment options, including running on YARN or as a standalone program. Samza offers high throughput and low latency to instantly analyze your data. With features like host-affinity and incremental checkpoints, Samza can scale to many terabytes in state. Samza is easy-to-use with flexible deployment options YARN, Kubernetes, or standalone. The ability to run the same code to process streaming and batch data. Integrates with multiple sources, including Kafka and HDFS, AWS Kinesis Azure Eventhubs, Azure Eventhubs K-V stores, ElasticSearch, AWS Kinesis, Kafka and ElasticSearch.

Apache Kafka®, is an open-source distributed streaming platform.

Axual provides Kafka-as-a-Service to DevOps teams. Our intuitive Kafka platform will empower your team to unlock insights, drive decisions and improve productivity. Axual is the ideal solution for enterprises that want to seamlessly integrate data streaming with their core IT infrastructure. Our all-in one Kafka platform was designed to eliminate the requirement for extensive technical skills or knowledge, and provide a ready-made product that delivers all of the benefits of event-streaming without the hassle. The Axual Platform, an all-in-one platform, is designed to simplify and enhance the deployment and management of Apache Kafka real-time streaming data. The Axual Platform offers a wide range of features to meet the needs of modern enterprises. This allows organizations to maximize the potential of data streaming, while minimizing complexity.

IBM® Event Streams, an event-streaming platform built on Apache Kafka open-source software, is a smart app that reacts to events as they occur. Event Streams is based upon years of IBM operational experience running Apache Kafka stream events for enterprises. Event Streams is ideal for mission-critical workloads. You can extend the reach and reach of your enterprise assets by connecting to a variety of core systems and using a scalable RESTAPI. Disaster recovery is made easier by geo-replication and rich security. Use the CLI to take advantage of IBM productivity tools. Replicate data between Event Streams deployments during a disaster-recovery scenario.

Event Hubs is a fully managed, real time data ingestion service that is simple, reliable, and scalable. Stream millions of events per minute from any source to create dynamic data pipelines that can be used to respond to business problems. Use the geo-disaster recovery or geo-replication features to continue processing data in emergencies. Integrate seamlessly with Azure services to unlock valuable insights. You can allow existing Apache Kafka clients to talk to Event Hubs with no code changes. This allows you to have a managed Kafka experience, without the need to manage your own clusters. You can experience real-time data input and microbatching in the same stream. Instead of worrying about infrastructure management, focus on gaining insights from your data. Real-time big data pipelines are built to address business challenges immediately.

ScaleOut Digital Twin Streaming Service™ Easily create and deploy real-time twins for streaming analytics Connect with many data sources with Azure & AWS IoT Hubs, Kafka, etc. Maximize situational awareness through live, aggregate analytics. A breakthrough cloud service that simultaneously tracks telemetry across millions of data sources, with "real-time digital twins" -- enabling deep, immediate introspection and state-tracking for thousands of devices. The powerful UI makes deployment easy and displays aggregate analytics in real-time to maximize situational awareness. Ideal for a wide variety of applications, including the Internet of Things, real-time intelligent monitoring and logistics, financial services, and financial services. Simple pricing makes it easy to get started. The ScaleOut Digital Twin Builder software and ScaleOut Digital Twin Streaming Service enable the next generation of stream processing.

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

HarperDB is an integrated distributed systems platform which combines database, caching and application functions into one technology. It allows you to deliver global back-end services at a lower cost, with higher performance and less effort. Install user-programmed apps and pre-built additions on top of data for a back end with ultra-low latencies. Distributed database with a high throughput per second, delivering orders of magnitude higher than NoSQL alternatives. Native real-time pub/sub data processing and communication via MQTT interfaces, WebSockets, and HTTP interfaces. HarperDB provides powerful data-in motion capabilities without adding additional services such as Kafka. Focus on features that will help your business grow, rather than fighting complicated infrastructure. You can't slow down the speed of light but you can reduce the amount of light between your users' data and them.

Now that your data has been in motion, it is time to make sense. Stream processing allows you to extract instant insights from your data streams but it can be difficult to set up the infrastructure. Confluent created ksqlDB to support stream processing applications. Continuously processing streams of data from your business will make your data actionable. The intuitive syntax of ksqlDB allows you to quickly access and augment Kafka data, allowing development teams to create innovative customer experiences and meet data-driven operational requirements. ksqlDB is a single solution that allows you to collect streams of data, enrich them and then serve queries on new derived streams or tables. This means that there is less infrastructure to manage, scale, secure, and deploy. You can now focus on the important things -- innovation -- with fewer moving parts in your data architecture.

Amazon Managed Service For Apache Flink is used by thousands of customers to run stream-processing applications. Amazon Managed Service Apache Flink allows you to transform and analyze streaming data using Apache Flink in real-time and integrate applications with AWS services. There are no clusters or servers to manage and no computing infrastructure to install. You only pay for the resources that you use. You can build and run Apache Flink apps without having to manage resources or clusters, or set up infrastructure. Process gigabytes per second, with latencies of subseconds and respond to events instantly. Multi-AZ deployments, APIs for lifecycle management and APIs to manage application lifecycles help you deploy highly available and durable apps. Create applications that transform data and deliver it to Amazon Simple Storage Service (Amazon S3) and Amazon OpenSearch Service.

Change data capture and replication service that is serverless and easy to use. Access to streaming data in MySQL, PostgreSQL and AlloyDB databases. BigQuery offers near-real-time analytics. Easy-to use setup with built-in security connectivity for faster time to value. A serverless platform which automatically scales without the need to provision or manage resources. Log-based mechanism reduces the load on source databases and any potential disruption. Synchronize data reliably across heterogeneous storage systems, databases, and applications with low latency while minimising impact on source performance. Easy-to-use and serverless service that scales up and down seamlessly and does not require infrastructure management will get you up and running quickly. Connect and integrate your data with the best Google Cloud services, including BigQuery, Spanner Dataflow and Data Fusion.

Google Cloud Pub/Sub: Delivery of messages in large quantities with push and pull modes. Auto-scaling, auto-provisioning, support from zero to hundreds GB/second Independent quota and billing are available for subscribers and publishers. Multi-region systems can be simplified by global message routing High availability made easy: Ensure reliable delivery at all scales with synchronous, cross-zone message replication. Auto-everything, no-planning Auto-scaling, auto-provisioning without partitions eliminates the need for planning and ensures that workloads are ready for production from day one. Advanced features built in: Filtering, dead letter delivery, and exponential backoff all help to simplify your applications

Spark Streaming uses Apache Spark's language-integrated API for stream processing. It allows you to write streaming jobs in the same way as you write batch jobs. It supports Java, Scala, and Python. Spark Streaming recovers lost work as well as operator state (e.g. Without any additional code, Spark Streaming recovers both lost work and operator state (e.g. sliding windows) right out of the box. Spark Streaming allows you to reuse the same code for batch processing and join streams against historical data. You can also run ad-hoc queries about stream state by running on Spark. Spark Streaming allows you to create interactive applications that go beyond analytics. Apache Spark includes Spark Streaming. It is updated with every Spark release. Spark Streaming can be run on Spark's standalone mode or other supported cluster resource mangers. It also has a local run mode that can be used for development. Spark Streaming uses ZooKeeper for high availability in production.

The issues facing your enterprise may be complex. The solution doesn't have to be complex. 3forge, the low-code platform with high flexibility and speed, allows enterprise application development to be done in record time. Reliability? Check. Scalability? Deliverability? Deliverability? In record time. Even for the most complex data sets and work flows. You no longer need to choose with 3forge. Data integration, virtualization and processing, visualization and workflows are all available in one place, allowing you to solve the most complex real-time data challenges. 3forge's award-winning technology allows developers to deploy mission critical applications in record time. 3forge's focus is on data integration and virtualization. It also focuses on processing and visualization.

Scale from 0 to millions of events every second. Arroyo is shipped as a single compact binary. Run locally on MacOS, Linux or Kubernetes for development and deploy to production using Docker or Kubernetes. Arroyo is an entirely new stream processing engine that was built from the ground-up to make real time easier than batch. Arroyo has been designed so that anyone with SQL knowledge can build reliable, efficient and correct streaming pipelines. Data scientists and engineers are able to build real-time dashboards, models, and applications from end-to-end without the need for a separate streaming expert team. SQL allows you to transform, filter, aggregate and join data streams with results that are sub-second. Your streaming pipelines should not page someone because Kubernetes rescheduled your pods. Arroyo can run in a modern, elastic cloud environment, from simple container runtimes such as Fargate, to large, distributed deployments using the Kubernetes logo.

Baidu Stream Computing provides real-time data processing with low delay, high throughput, and high accuracy. It is compatible with Spark SQL and can process complex business logic through SQL statements. It also provides users with a full life cycle management of streaming-oriented computing jobs. As the upstream and downstream of stream computing, integrate deeply with multiple storage solutions of Baidu AI Cloud, including Baidu Kafka and RDS. Provide a comprehensive monitoring indicator for the job. The user can view monitoring indicators and set alarm rules to protect the task.

Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.

Allow everyone to view and discover streaming data. Up to 95% of productivity can be increased by sharing, documenting, and cataloging data. Next, create apps for production use cases using the data. To address privacy concerns and cover all the gaps in open source technology, apply a data-centric security approach. Secure and low-code data pipeline capabilities. All darkness is eliminated and data and apps can be viewed with unparalleled visibility. Unify your data technologies and data meshes and feel confident using open source production. Independent third-party reviews have rated Lenses the best product for real time stream analytics. We have built features to allow you to focus on what is driving value from real-time data. This was based on feedback from our community as well as thousands of engineering hours. You can deploy and run SQL-based real-time applications over any Kafka Connect, Kubernetes or Kubernetes infrastructure, including AWS EKS.

Nussknacker allows domain experts to use a visual tool that is low-code to help them create and execute real-time decisioning algorithm instead of writing code. It is used to perform real-time actions on data: real-time marketing and fraud detection, Internet of Things customer 360, Machine Learning inferring, and Internet of Things customer 360. A visual design tool for decision algorithm is an essential part of Nussknacker. It allows non-technical users, such as analysts or business people, to define decision logic in a clear, concise, and easy-to-follow manner. With a click, scenarios can be deployed for execution once they have been created. They can be modified and redeployed whenever there is a need. Nussknacker supports streaming and request-response processing modes. It uses Kafka as its primary interface in streaming mode. It supports both stateful processing and stateless processing.

Easy to capture, transform and load streaming data. Create a stream of data, select the destination and start streaming real time data in just a few simple clicks. Automate the provisioning and scaling of compute, memory and network resources, without any ongoing administration. Transform streaming data into formats such as Apache Parquet and dynamically partition streaming without building your own pipelines. Amazon Data Firehose is the fastest way to acquire data streams, transform them, and then deliver them to data lakes, warehouses, or analytics services. Amazon Data Firehose requires you to create a stream that includes a destination, a source and the transformations required. Amazon Data Firehose continuously processes a stream, scales automatically based on data availability, and delivers the results within seconds. Select the source of your data stream, or write data with the Firehose Direct PUT (API) API.

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.

Streaming data from operations and transactions is valuable when it is well-understood. SAS Event stream processing includes streaming data quality, analytics, and a vast array SAS and open-source machine learning and high frequency analytics for connecting to, deciphering and cleansing streaming data. It doesn't matter how fast your data moves or how many sources you pull from, all of it is under your control through a single, intuitive interface. You can create patterns and address situations from any aspect of your business, giving the ability to be agile and deal with issues as they arise.

Responsive apps keep developers motivated and users engaged. With the DataStax Astra streaming service platform, you can meet these ever-increasing demands. DataStax Astra Streaming, powered by Apache Pulsar, is a cloud-native messaging platform and event streaming platform. Astra Streaming lets you build streaming applications on top a multi-cloud, elastically scalable and event streaming platform. Apache Pulsar is the next-generation event streaming platform that powers Astra Streaming. It provides a unified solution to streaming, queuing and stream processing. Astra Streaming complements Astra DB. Astra Streaming allows existing Astra DB users to easily create real-time data pipelines from and to their Astra DB instances. Astra Streaming allows you to avoid vendor lock-in by deploying on any major public cloud (AWS, GCP or Azure) compatible with open source Apache Pulsar.

Esper Enterprise Edition is a distributed platform for horizontal and linear elastic scalability, fault-tolerant event processing, and fault tolerance.

No more low-level code or gluing together complex systems. SQL makes it easy to build and deploy pipelines quickly. Data engineering service that allows developers and data engineers to quickly build and deploy data pipelines for data-driven apps. It is easy to connect to and find available data using pre-built connectors for messaging, storage, and database engines. Each connection you make will result in a stream of data to or from the system. You can create your pipelines using SQL with Decodable. Pipelines use streams to send and receive data to and from your connections. Streams can be used to connect pipelines to perform the most difficult processing tasks. To ensure data flows smoothly, monitor your pipelines. Create curated streams that can be used by other teams. To prevent data loss due to system failures, you should establish retention policies for streams. You can monitor real-time performance and health metrics to see if everything is working.

Apache Doris is an advanced data warehouse for real time analytics. It delivers lightning fast analytics on real-time, large-scale data. Ingestion of micro-batch data and streaming data within a second. Storage engine with upserts, appends and pre-aggregations in real-time. Optimize for high-concurrency, high-throughput queries using columnar storage engine, cost-based query optimizer, and vectorized execution engine. Federated querying for data lakes like Hive, Iceberg, and Hudi and databases like MySQL and PostgreSQL. Compound data types, such as Arrays, Maps and JSON. Variant data types to support auto datatype inference for JSON data. NGram bloomfilter for text search. Distributed design for linear scaling. Workload isolation, tiered storage and efficient resource management. Supports shared-nothing as well as the separation of storage from compute.

Apache Storm is an open-source distributed realtime computing system that is free and open-source. Apache Storm makes it simple to process unbounded streams and data reliably, much like Hadoop did for batch processing. Apache Storm is easy to use with any programming language and is a lot fun! Apache Storm can be used for many purposes: realtime analytics and online machine learning. It can also be used with any programming language. Apache Storm is fast. A benchmark measured it at more than a million tuples per second per node. It is highly scalable, fault-tolerant and guarantees that your data will be processed. It is also easy to set up. Apache Storm can be integrated with the queueing and databases technologies you already use. Apache Storm topology processes streams of data in arbitrarily complex ways. It also partitions the streams between each stage of the computation as needed. Learn more in the tutorial.

SelectDB is an advanced data warehouse built on Apache Doris. It supports rapid query analysis of large-scale, real-time data. Clickhouse to Apache Doris to separate the lake warehouse, and upgrade the lake storage. Fast-hand OLAP system carries out nearly 1 billion queries every day in order to provide data services for various scenes. The original lake warehouse separation was abandoned due to problems with storage redundancy and resource seizure. Also, it was difficult to query and adjust. It was decided to use Apache Doris lakewarehouse, along with Doris's materialized views rewriting capability and automated services to achieve high-performance query and flexible governance. Write real-time data within seconds and synchronize data from databases and streams. Data storage engine with real-time update and addition, as well as real-time polymerization.

Azure Data Explorer provides fast, fully managed data analytics services for real-time analysis of large amounts of data streaming from websites, applications, IoT devices, etc. Ask questions and iteratively analyze data on the fly to improve products and customer experiences, monitor devices, boost operations, and increase profits. Identify patterns, anomalies, or trends quickly in your data. Find answers to your questions quickly and easily by exploring new topics. The optimized cost structure allows you to run as many queries as needed. You can explore new possibilities with your data efficiently. With the fully managed, easy-to-use data analytics service, you can focus on insights and not infrastructure. Rapidly respond to rapidly changing and fast-flowing data. Azure Data Explorer simplifies analytics for all types of streaming data.

Kapacitor, a native data processing engine in InfluxDB 1.x, is an integral component of the InfluxDB 2.0 platform. Kapacitor is able to process both batch and stream data from InfluxDB. It can also act on these data in real time via its programming language TICKscript. Modern applications need more than operator alerts and dashboarding. They also require the ability to trigger actions. Kapacitor's alerting system uses a publish-subscribe design. Alerts are sent to topics, and subscribers subscribe to a topic. Kapacitor is very flexible and can be used to control your environment. It can perform tasks such as stock reordering and auto-scaling. Kapacitor has a simple plugin architecture (or interface) that allows it integrate with any anomaly detector engine.

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.

A reliable, easy-to-use, and powerful system to process and distribute data. Apache NiFi supports powerful, scalable directed graphs for data routing, transformation, system mediation logic, and is scalable. Apache NiFi's high-level capabilities and goals include a web-based user interface that provides seamless design, control, feedback and monitoring. Highly configurable, loss-tolerant, low latency and high throughput. Dynamic prioritization is also possible. Flow can be modified at runtime by back pressure, data provenance, and track dataflow from start to finish. This is a flexible system that is extensible. You can build your own processors. This allows for rapid development and efficient testing. Secure, SSL, SSH and HTTPS encryption, as well as encrypted content. Multi-tenant authorization, internal authorization/policy administration. NiFi includes a variety of web applications, including web UI, web API, documentation and custom UI's. You will need to map to the root path.