Alternatives to Amazon Data Firehose
Compare Amazon Data Firehose alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Amazon Data Firehose in 2026. Compare features, ratings, user reviews, pricing, and more from Amazon Data Firehose competitors and alternatives in order to make an informed decision for your business.
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Cribl Stream
Cribl
Cribl Stream allows you to implement an observability pipeline which helps you parse, restructure, and enrich data in flight - before you pay to analyze it. Get the right data, where you want, in the formats you need. Route data to the best tool for the job - or all the tools for the job - by translating and formatting data into any tooling schema you require. Let different departments choose different analytics environments without having to deploy new agents or forwarders. As much as 50% of log and metric data goes unused – null fields, duplicate data, and fields that offer zero analytical value. With Cribl Stream, you can trim wasted data streams and analyze only what you need. Cribl Stream is the best way to get multiple data formats into the tools you trust for your Security and IT efforts. Use the Cribl Stream universal receiver to collect from any machine data source - and even to schedule batch collection from REST APIs, Kinesis Firehose, Raw HTTP, and Microsoft Office 365 APIsStarting Price: Free (1TB / Day) -
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Striim
Striim
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. -
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Thousands of customers use Amazon Managed Service for Apache Flink to run stream processing applications. With Amazon Managed Service for Apache Flink, you can transform and analyze streaming data in real-time using Apache Flink and integrate applications with other AWS services. There are no servers and clusters to manage, and there is no computing and storage infrastructure to set up. You pay only for the resources you use. Build and run Apache Flink applications, without setting up infrastructure and managing resources and clusters. Process gigabytes of data per second with subsecond latencies and respond to events in real-time. Deploy highly available and durable applications with Multi-AZ deployments and APIs for application lifecycle management. Develop applications that transform and deliver data to Amazon Simple Storage Service (Amazon S3), Amazon OpenSearch Service, and more.Starting Price: $0.11 per hour
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Amazon Kinesis
Amazon
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. -
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Apache Kafka
The Apache Software Foundation
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. -
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Streamkap
Streamkap
Streamkap is a streaming data platform that makes streaming as easy as batch. Stream data from database (change data capturee) or event sources to your favorite database, data warehouse or data lake. Streamkap can be deployed as a SaaS or in a bring your own cloud (BYOC) deployment.Starting Price: $600 per month -
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SelectDB
SelectDB
SelectDB is a modern data warehouse based on Apache Doris, which supports rapid query analysis on large-scale real-time data. From Clickhouse to Apache Doris, to achieve the separation of the lake warehouse and upgrade to the lake warehouse. The fast-hand OLAP system carries nearly 1 billion query requests every day to provide data services for multiple scenes. Due to the problems of storage redundancy, resource seizure, complicated governance, and difficulty in querying and adjustment, the original lake warehouse separation architecture was decided to introduce Apache Doris lake warehouse, combined with Doris's materialized view rewriting ability and automated services, to achieve high-performance data query and flexible data governance. Write real-time data in seconds, and synchronize flow data from databases and data streams. Data storage engine for real-time update, real-time addition, and real-time pre-polymerization.Starting Price: $0.22 per hour -
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Astra Streaming
DataStax
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. -
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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.
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WarpStream
WarpStream
WarpStream is an Apache Kafka-compatible data streaming platform built directly on top of object storage, with no inter-AZ networking costs, no disks to manage, and infinitely scalable, all within your VPC. WarpStream is deployed as a stateless and auto-scaling agent binary in your VPC with no local disks to manage. Agents stream data directly to and from object storage with no buffering on local disks and no data tiering. Create new “virtual clusters” in our control plane instantly. Support different environments, teams, or projects without managing any dedicated infrastructure. WarpStream is protocol compatible with Apache Kafka, so you can keep using all your favorite tools and software. No need to rewrite your application or use a proprietary SDK. Just change the URL in your favorite Kafka client library and start streaming. Never again have to choose between reliability and your budget.Starting Price: $2,987 per month -
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Apache Flink
Apache Software Foundation
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. -
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Apache Storm
Apache Software Foundation
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. -
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Amazon MSK
Amazon
Amazon Managed Streaming for Apache Kafka (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.Starting Price: $0.0543 per hour -
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Materialize
Materialize
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.Starting Price: $0.98 per hour -
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Arroyo
Arroyo
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. -
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SQLstream
Guavus, a Thales company
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. -
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Yandex Data Streams
Yandex
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.Starting Price: $0.086400 per GB -
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DeltaStream
DeltaStream
DeltaStream is a unified serverless stream processing platform that integrates with streaming storage services. Think about it as the compute layer on top of your streaming storage. It provides functionalities of streaming analytics(Stream processing) and streaming databases along with additional features to provide a complete platform to manage, process, secure and share streaming data. DeltaStream provides a SQL based interface where you can easily create stream processing applications such as streaming pipelines, materialized views, microservices and many more. It has a pluggable processing engine and currently uses Apache Flink as its primary stream processing engine. DeltaStream is more than just a query processing layer on top of Kafka or Kinesis. It brings relational database concepts to the data streaming world, including namespacing and role based access control enabling you to securely access, process and share your streaming data regardless of where they are stored. -
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Apache Beam
Apache Software Foundation
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. -
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Spark Streaming
Apache Software Foundation
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. -
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VeloDB
VeloDB
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. -
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IBM Event Streams is a fully managed event streaming platform built on Apache Kafka, designed to help enterprises process and respond to real-time data streams. With capabilities for machine learning integration, high availability, and secure cloud deployment, it enables organizations to create intelligent applications that react to events as they happen. The platform supports multi-cloud environments, disaster recovery, and geo-replication, making it ideal for mission-critical workloads. IBM Event Streams simplifies building and scaling real-time, event-driven solutions, ensuring data is processed quickly and efficiently.
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Apache Doris
The Apache Software Foundation
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.Starting Price: Free -
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Google Cloud Dataflow
Google
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. -
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Timeplus
Timeplus
Timeplus is a simple, powerful, and cost-efficient stream processing platform. All in a single binary, easily deployed anywhere. We help data teams process streaming and historical data quickly and intuitively, in organizations of all sizes and industries. Lightweight, single binary, without dependencies. End-to-end analytic streaming and historical functionalities. 1/10 the cost of similar open source frameworks. Turn real-time market and transaction data into real-time insights. Leverage append-only streams and key-value streams to monitor financial data. Implement real-time feature pipelines using Timeplus. One platform for all infrastructure logs, metrics, and traces, the three pillars supporting observability. In Timeplus, we support a wide range of data sources in our web console UI. You can also push data via REST API, or create external streams without copying data into Timeplus.Starting Price: $199 per month -
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Informatica Data Engineering Streaming
Informatica
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. -
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ksqlDB
Confluent
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. -
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IBM Streams
IBM
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. -
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Apache Flume
Apache Software Foundation
Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault-tolerant with tunable reliability mechanisms and many failovers and recovery mechanisms. It uses a simple extensible data model that allows for online analytic applications. The Apache Flume team is pleased to announce the release of Flume 1.8.0. Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming event data. -
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Upsolver
Upsolver
Upsolver makes it incredibly simple to build a governed data lake and to manage, integrate and prepare streaming data for analysis. Define pipelines using only SQL on auto-generated schema-on-read. Easy visual IDE to accelerate building pipelines. Add Upserts and Deletes to data lake tables. Blend streaming and large-scale batch data. Automated schema evolution and reprocessing from previous state. Automatic orchestration of pipelines (no DAGs). Fully-managed execution at scale. Strong consistency guarantee over object storage. Near-zero maintenance overhead for analytics-ready data. Built-in hygiene for data lake tables including columnar formats, partitioning, compaction and vacuuming. 100,000 events per second (billions daily) at low cost. Continuous lock-free compaction to avoid “small files” problem. Parquet-based tables for fast queries. -
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Decodable
Decodable
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.Starting Price: $0.20 per task per hour -
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Baidu AI Cloud Stream Computing
Baidu AI Cloud
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. -
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Confluent
Confluent
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. -
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Samza
Apache Software Foundation
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. -
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Apache DataFusion
Apache Software Foundation
Apache DataFusion is an extensible, high-performance query engine written in Rust that utilizes Apache Arrow as its in-memory format. Designed for developers building data-centric systems such as databases, data frames, machine learning, and streaming applications, DataFusion offers SQL and DataFrame APIs, a vectorized, multi-threaded, streaming execution engine, and support for partitioned data sources. It natively supports formats like CSV, Parquet, JSON, and Avro, and allows for seamless integration with object stores including AWS S3, Azure Blob Storage, and Google Cloud Storage. The engine features a comprehensive query planner, a state-of-the-art optimizer with capabilities like expression coercion and simplification, projection and filter pushdown, sort and distribution-aware optimizations, and automatic join reordering. DataFusion is highly customizable, enabling the addition of user-defined scalar, aggregate, and window functions, custom data sources, query languages, etc.Starting Price: Free -
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Amazon AppStream
Amazon
Amazon AppStream 2.0 is a fully managed non-persistent desktop and application virtualization service that allows your users to securely access the data, applications, and resources they need, anywhere, anytime, from any supported device. With AppStream 2.0, you can easily scale your applications and desktops to any number of users across the globe without acquiring, provisioning, and operating hardware or infrastructure. AppStream 2.0 is built on AWS, so you benefit from a data center and network architecture designed for the most security-sensitive organizations. Each end user has a fluid and responsive experience because your applications run on virtual machines optimized for specific use cases and each streaming sessions automatically adjusts to network conditions. Reducing the risk of company data leaking is critical in a more mobile and remote environment. With AppStream 2.0, applications and data are not stored on users' computers. -
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IBM StreamSets
IBM
IBM® StreamSets enables users to create and manage smart streaming data pipelines through an intuitive graphical interface, facilitating seamless data integration across hybrid and multicloud environments. This is why leading global companies rely on IBM StreamSets to support millions of data pipelines for modern analytics, intelligent applications and hybrid integration. Decrease data staleness and enable real-time data at scale—handling millions of records of data, across thousands of pipelines within seconds. Insulate data pipelines from change and unexpected shifts with drag-and-drop, prebuilt processors designed to automatically identify and adapt to data drift. Create streaming pipelines to ingest structured, semistructured or unstructured data and deliver it to a wide range of destinations.Starting Price: $1000 per month -
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Google Cloud Datastream
Google
Serverless and easy-to-use change data capture and replication service. Access to streaming data from MySQL, PostgreSQL, AlloyDB, SQL Server, and Oracle databases. Near real-time analytics in BigQuery. Easy-to-use setup with built-in secure connectivity for faster time-to-value. A serverless platform that automatically scales, with no resources to provision or manage. Log-based mechanism to reduce the load and potential disruption on source databases. Synchronize data across heterogeneous databases, storage systems, and applications reliably, with low latency, while minimizing impact on source performance. Get up and running fast with a serverless and easy-to-use service that seamlessly scales up or down, and has no infrastructure to manage. Connect and integrate data across your organization with the best of Google Cloud services like BigQuery, Spanner, Dataflow, and Data Fusion. -
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Onehouse
Onehouse
The only fully managed cloud data lakehouse designed to ingest from all your data sources in minutes and support all your query engines at scale, for a fraction of the cost. Ingest from databases and event streams at TB-scale in near real-time, with the simplicity of fully managed pipelines. Query your data with any engine, and support all your use cases including BI, real-time analytics, and AI/ML. Cut your costs by 50% or more compared to cloud data warehouses and ETL tools with simple usage-based pricing. Deploy in minutes without engineering overhead with a fully managed, highly optimized cloud service. Unify your data in a single source of truth and eliminate the need to copy data across data warehouses and lakes. Use the right table format for the job, with omnidirectional interoperability between Apache Hudi, Apache Iceberg, and Delta Lake. Quickly configure managed pipelines for database CDC and streaming ingestion. -
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The Tie Terminal
The Tie
The data terminal is trusted by the leading hedge funds, VC firms, OTC desks, prop firms, market makers, and institutional professionals. The fastest and most comprehensive news updates in crypto. Coverage on thousands of primary and secondary sources. Real-time spot market updates from the leading crypto exchanges. Open-interest, funding, and futures data. Four years of point-in-time sentiment data built on Twitter’s Firehose. Quantified data on TikTok and YouTube to identify retail trends before the market reacts. Cross-chain comparative TVL metrics. Data on holdings, ownership distribution, and whale movements. Real-time mint tracking to identify the newest projects. Monitoring on NFT market trends and bluechip projects. Funding, web traffic, news, and team growth data. Monitor custodians, exchanges, service providers, miners, and funds. Direct updates from over five hundred projects and exchanges. -
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Tinybird
Tinybird
Query and shape your data using Pipes, a new way to chain SQL queries inspired by Python Notebooks. Designed to reduce complexity without sacrificing performance. By splitting your query in different nodes you simplify development and maintenance. Activate your production-ready API endpoints with one click. Transformations occur on-the-fly so you will always work with the latest data. Share access securely to your data in one click and get fast and consistent results. Apart from providing monitoring tools, Tinybird scales linearly: don't worry about traffic spikes. Imagine if you could turn, in a matter of minutes, any Data Stream or CSV file into a fully secured real-time analytics API endpoint. We believe in high-frequency decision-making for all organizations in all industries including retail, manufacturing, telecommunications, government, advertising, entertainment, healthcare, and financial services.Starting Price: $0.07 per processed GB -
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HarperDB
HarperDB
HarperDB is a distributed systems platform that combines database, caching, application, and streaming functions into a single technology. With it, you can start delivering global-scale back-end services with less effort, higher performance, and lower cost than ever before. Deploy user-programmed applications and pre-built add-ons on top of the data they depend on for a high throughput, ultra-low latency back end. Lightning-fast distributed database delivers orders of magnitude more throughput per second than popular NoSQL alternatives while providing limitless horizontal scale. Native real-time pub/sub communication and data processing via MQTT, WebSocket, and HTTP interfaces. HarperDB delivers powerful data-in-motion capabilities without layering in additional services like Kafka. Focus on features that move your business forward, not fighting complex infrastructure. You can't change the speed of light, but you can put less light between your users and their data.Starting Price: Free -
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Cloudera DataFlow
Cloudera
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. -
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Amazon Interactive Video Service (Amazon IVS) is a managed live streaming solution that is quick and easy to set up, and ideal for creating interactive video experiences. Send your live streams to Amazon IVS using streaming software and the service does everything you need to make low-latency live video available to any viewer around the world, letting you focus on building interactive experiences alongside the live video. You can easily customize and enhance the audience experience through the Amazon IVS player SDK and timed metadata APIs, allowing you to build a more valuable relationship with your viewers on your own websites and applications. Create and configure live streams with just a few clicks, and start streaming in seconds. Send video to Amazon IVS, and the service manages everything needed to get your live stream to your viewers. Amazon IVS streams are designed to provide low-latency live video “out of the box".Starting Price: $0.20 per hour
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45
Hitachi Streaming Data Platform
Hitachi
The Hitachi Streaming Data Platform (SDP) is a real-time data processing system designed to analyze large volumes of time-sequenced data as it is generated. By leveraging in-memory and incremental computational processing, SDP enables swift analysis without the delays associated with traditional stored data processing. Users can define summary analysis scenarios using Continuous Query Language (CQL), similar to SQL, allowing for flexible and programmable data analysis without the need for custom applications. The platform's architecture comprises components such as development servers, data-transfer servers, data-analysis servers, and dashboard servers, facilitating scalable and efficient data processing workflows. SDP's modular design supports various data input and output formats, including text files and HTTP packets, and integrates with visualization tools like RTView for real-time monitoring. -
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Talend Pipeline Designer is a web-based self-service application that takes raw data and makes it analytics-ready. Compose reusable pipelines to extract, improve, and transform data from almost any source, then pass it to your choice of data warehouse destinations, where it can serve as the basis for the dashboards that power your business insights. Build and deploy data pipelines in less time. Design and preview, in batch or streaming, directly in your web browser with an easy, visual UI. Scale with native support for the latest hybrid and multi-cloud technologies, and improve productivity with real-time development and debugging. Live preview lets you instantly and visually diagnose issues with your data. Make better decisions faster with dataset documentation, quality proofing, and promotion. Transform data and improve data quality with built-in functions applied across batch or streaming pipelines, turning data health into an effortless, automated discipline.
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47
Lightstreamer
Lightstreamer
Lightstreamer is an event broker optimized for the internet, ensuring seamless real-time data delivery across the web. Unlike traditional brokers, Lightstreamer automatically handles proxies, firewalls, disconnections, network congestion, and the general unpredictability of the internet. With its intelligent streaming feature, Lightstreamer guarantees real-time data transmission, always finding a way to deliver your data reliably and efficiently, ensuring robust last-mile messaging. Lightstreamer offers technology that is both mature and cutting-edge, continuously evolving to stay at the forefront of innovation. With a proven track record and years of field-tested performance, Lightstreamer ensures your data is delivered reliably and efficiently. Experience unparalleled reliability in any scenario with Lightstreamer.Starting Price: Free -
48
Equalum
Equalum
Equalum’s continuous data integration & streaming platform is the only solution that natively supports real-time, batch, and ETL use cases under one, unified platform with zero coding required. Make the move to real-time with a fully orchestrated, drag-and-drop, no-code UI. Experience rapid deployment, powerful transformations, and scalable streaming data pipelines in minutes. Multi-modal, robust, and scalable CDC enabling real-time streaming and data replication. Tuned for best-in-class performance no matter the source. The power of open-source big data frameworks, without the hassle. Equalum harnesses the scalability of open-source data frameworks such as Apache Spark and Kafka in the Platform engine to dramatically improve the performance of streaming and batch data processes. Organizations can increase data volumes while improving performance and minimizing system impact using this best-in-class infrastructure. -
49
GribStream
GribStream
GribStream is a fast and efficient historical weather forecast API that provides high-speed access to both historical and real-time weather data from the National Blend of Models (NBM) and the Global Forecast System (GFS). Designed for companies, meteorologists, and researchers, GribStream enables users to retrieve tens of thousands of data points at an hourly rate, spanning months, in a single HTTP request within seconds. The platform offers a simple API with open source clients and clear documentation, facilitating quick integration. It supports various result formats, including CSV, Parquet, JSON lines, and image formats like PNG, JPG, and TIFF. Users can specify locations by latitude and longitude, and define time ranges for data retrieval. GribStream is actively developing features such as additional datasets, result formats, aggregation methods, and notification systems.Starting Price: $9.90 per month -
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Azure Stream Analytics
Microsoft
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.
