Arroyo 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, 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. • 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.

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.

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.

The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker.

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.

Whether you're working with a single table or multiple, you'll experience at least 300% better performance on StarRocks compared to other popular solutions. From streaming data to data capture, with a rich set of connectors, you can ingest data into StarRocks in real time for the freshest insights. A query engine that adapts to your use cases. Without moving your data or rewriting SQL, StarRocks provides the flexibility to scale your analytics on demand with ease. StarRocks enables a rapid journey from data to insight. StarRocks' performance is unmatched and provides a unified OLAP solution covering the most popular data analytics scenarios. Whether you're working with a single table or multiple, you'll experience at least 300% better performance on StarRocks compared to other popular solutions. StarRocks' built-in memory-and-disk-based caching framework is specifically designed to minimize the I/O overhead of fetching data from external storage to accelerate query performance.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Aerospike is the global leader in next-generation, real-time NoSQL data solutions for any scale. Aerospike enterprises overcome seemingly impossible data bottlenecks to compete and win with a fraction of the infrastructure complexity and cost of legacy NoSQL databases. Aerospike’s patented Hybrid Memory Architecture™ delivers an unbreakable competitive advantage by unlocking the full potential of modern hardware, delivering previously unimaginable value from vast amounts of data at the edge, to the core and in the cloud. Aerospike empowers customers to instantly fight fraud; dramatically increase shopping cart size; deploy global digital payment networks; and deliver instant, one-to-one personalization for millions of customers. Aerospike customers include Airtel, Banca d’Italia, Nielsen, PayPal, Snap, Verizon Media and Wayfair. The company is headquartered in Mountain View, Calif., with additional locations in London; Bengaluru, India; and Tel Aviv, Israel.

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.

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.

Pathway is a Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG. Pathway comes with an easy-to-use Python API, allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: you can use it in both development and production environments, handling both batch and streaming data effectively. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a scalable Rust engine based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with Docker and Kubernetes.

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.

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.

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.

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.

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.

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.

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.

SingleStore (formerly MemSQL) is a distributed, highly-scalable SQL database that can run anywhere. We deliver maximum performance for transactional and analytical workloads with familiar relational models. SingleStore is a scalable SQL database that ingests data continuously to perform operational analytics for the front lines of your business. Ingest millions of events per second with ACID transactions while simultaneously analyzing billions of rows of data in relational SQL, JSON, geospatial, and full-text search formats. SingleStore delivers ultimate data ingestion performance at scale and supports built in batch loading and real time data pipelines. SingleStore lets you achieve ultra fast query response across both live and historical data using familiar ANSI SQL. Perform ad hoc analysis with business intelligence tools, run machine learning algorithms for real-time scoring, perform geoanalytic queries in real time.

Save time & costs by streamlining data pipelines with zero-maintenance open source solutions. From ingestion to visualization, all are integrated, fully managed, and highly reliable, so your engineers will love working with data. You choose whether to use any of DoubleCloud’s managed open source services or leverage the full power of the platform, including data storage, orchestration, ELT, and real-time visualization. We provide leading open source services like ClickHouse, Kafka, and Airflow, with deployment on Amazon Web Services or Google Cloud. Our no-code ELT tool allows real-time data syncing between systems, fast, serverless, and seamlessly integrated with your existing infrastructure. With our managed open-source data visualization you can simply visualize your data in real time by building charts and dashboards. We’ve designed our platform to make the day-to-day life of engineers more convenient.

Trino is a query engine that runs at ludicrous speed. Fast-distributed SQL query engine for big data analytics that helps you explore your data universe. Trino is a highly parallel and distributed query engine, that is built from the ground up for efficient, low-latency analytics. The largest organizations in the world use Trino to query exabyte-scale data lakes and massive data warehouses alike. Supports diverse use cases, ad-hoc analytics at interactive speeds, massive multi-hour batch queries, and high-volume apps that perform sub-second queries. Trino is an ANSI SQL-compliant query engine, that works with BI tools such as R, Tableau, Power BI, Superset, and many others. You can natively query data in Hadoop, S3, Cassandra, MySQL, and many others, without the need for complex, slow, and error-prone processes for copying the data. Access data from multiple systems within a single query.

In-Memory Computing Platform. The digital world is different. Microseconds matter. That's why the world's largest organizations rely on us to power their most time-sensitive applications at scale. New data-enabled applications can deliver transformative business power – if they meet today’s requirement of immediacy. Hazelcast solutions complement virtually any database to deliver results that are significantly faster than a traditional system of record. Hazelcast’s distributed architecture provides redundancy for continuous cluster up-time and always available data to serve the most demanding applications. Capacity grows elastically with demand, without compromising performance or availability. The fastest in-memory data grid, combined with third-generation high-speed event processing, delivered through the cloud.

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.

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.

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.

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.

The Open, Multi-Cloud Stack for Modern Data Apps. Built on open-source Apache Cassandra™. Global-scale and 100% uptime without vendor lock-in. Deploy on multi-cloud, on-prem, open-source, and Kubernetes. Elastic and pay-as-you-go for improved TCO. Start building faster with Stargate APIs for NoSQL, real-time, reactive, JSON, REST, and GraphQL. Skip the complexity of multiple OSS projects and APIs that don’t scale. Ideal for commerce, mobile, AI/ML, IoT, microservices, social, gaming, and richly interactive applications that must scale-up and scale-down with demand. Get building modern data applications with Astra, a database-as-a-service powered by Apache Cassandra™. Use REST, GraphQL, JSON with your favorite full-stack framework Richly interactive apps that are elastic and viral-ready from Day 1. Pay-as-you-go Apache Cassandra DBaaS that scales effortlessly and affordably.

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.

InfinyOn has architected a programmable continuous intelligence platform for data in motion. Unlike other event streaming platforms that were built on Java, Infinyon Cloud is built on Rust and delivers industry leading scale and security for real-time applications. Ready to use programmable connectors that shape data events in real-time. Provision intelligent analytics pipelines that refine, protect, and correlate events in real-time. Attach programmable connectors to dispatch events and notify stakeholders. Each connector is either a source, which imports data, or a sink, which exports data. Connectors may be deployed in one of two ways: as a Managed Connector, in which the Fluvio cluster provisions and manages the connector; or as a Local Connector, in which you manually launch the connector as a docker container where you want it. Additionally, connectors conceptually have four stages, where each stage has distinct responsibilities.

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.

Imply is a real-time analytics platform built on Apache Druid, designed to handle large-scale, high-performance OLAP (Online Analytical Processing) workloads. It offers real-time data ingestion, fast query performance, and the ability to perform complex analytical queries on massive datasets with low latency. Imply is tailored for organizations that need interactive analytics, real-time dashboards, and data-driven decision-making at scale. It provides a user-friendly interface for data exploration, along with advanced features such as multi-tenancy, fine-grained access controls, and operational insights. With its distributed architecture and scalability, Imply is well-suited for use cases in streaming data analytics, business intelligence, and real-time monitoring across industries.

Apache Druid is an open source distributed data store. Druid’s core design combines ideas from data warehouses, timeseries databases, and search systems to create a high performance real-time analytics database for a broad range of use cases. Druid merges key characteristics of each of the 3 systems into its ingestion layer, storage format, querying layer, and core architecture. Druid stores and compresses each column individually, and only needs to read the ones needed for a particular query, which supports fast scans, rankings, and groupBys. Druid creates inverted indexes for string values for fast search and filter. Out-of-the-box connectors for Apache Kafka, HDFS, AWS S3, stream processors, and more. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures.

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.

Quasar's brain is QuasarDB, a high-performance, distributed, column-oriented timeseries database management system designed from the ground up to deliver real-time on petascale use cases. Up to 20X less disk usage. Quasardb ingestion and compression capabilities are unmatched. Up to 10,000X faster feature extraction. QuasarDB can extract features in real-time from the raw data, thanks to the combination of a built-in map/reduce query engine, an aggregation engine that leverages SIMD from modern CPUs, and stochastic indexes that use virtually no disk space. The most cost-effective timeseries solution, thanks to its ultra-efficient resource usage, the capability to leverage object storage (S3), unique compression technology, and fair pricing model. Quasar runs everywhere, from 32-bit ARM devices to high-end Intel servers, from Edge Computing to the cloud or on-premises.

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.

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.

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.