Alternatives to Apache Kafka

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.

EMQX is the world's most scalable and reliable MQTT messaging platform designed by EMQ. It supports 100M concurrent IoT device connections per cluster while maintaining extremely high throughput and sub-millisecond latency. EMQX boasts more than 20,000 global users from over 50 countries, connecting more than 100M IoT devices worldwide, and is trusted by over 300 customers in mission-critical IoT scenarios, including well-known brands like HPE, VMware, Verifone, SAIC Volkswagen, and Ericsson. Our edge-to-cloud IoT data solutions are flexible to meet the demands of various industries towards digital transformation, including connected vehicles, Industrial IoT, oil & gas, carrier, finance, smart energy, and smart cities. EMQX Enterprise: The World’s # 1 Scalable MQTT Messaging Platform -100M concurrent MQTT connections -1M/s messages throughput under 1ms latency -Business-critical reliability, Up to 99.99% SLA -Integrate IoT data seamlessly with over 40 cloud services and enterprise systems EMQX Cloud: Fully Managed MQTT Service for IoT - Scale as you need, pay as you go - Flexible and rich IoT data integration up to 40+ choices - Run in 19 regions across AWS, GCP, and Microsoft Azure - 100% MQTT

A subscription includes everything needed for high-volume data replication or integration. Log-Based Change Data Capture and a unique compression algorithm ensure low-impact data movement, even at high volumes. RESTful APIs allow workflow automation, streamlining and time savings. HVR offers a variety security features. It also allows data routing through a firewall proxy for hybrid environments. Multi- and bidirectional data movement is supported, giving you the freedom and flexibility to optimize your data flows. All you need to complete your data replication project are included in one license. To ensure customer success, we provide in-depth training, support and documentation. With our Data Validation feature and Live Compare, you can be sure that your data is accurate. All you need to complete your data replication project are included in one license.

Open Automation Software IIoT platform Windows and Linux allows you to liberate your Industry4.0 data. OAS is an unlimited IoT Gateway that works with Windows, Linux, Raspberry Pi 4 and Windows IoT Core. It can also be used to deploy Docker containers. HMI visualizations for web, WPF, WinForm C#, and VB.NET applications. Log data and alarms to SQL Server and MS Access, SQL Server, Oracle and MS Access, MySQL and Azure SQL, PostgreSQL and Cassandra. MQTT Broker and Client interface, as well as cloud connectivity to Azure IoT Gateway and AWS IoT Gateway. Remote Excel Workbooks can be used to read and write data. Notifications of alarm sent to voice, SMS text and email. Access to programmatic information via REST API and.NET Allen Bradley ControlLogix and CompactLogix, GuardLogix. Micro800, MicroLogix. MicroLogix. SLC 500. PLC-5. Siemens S7-220, S7-3300, S7-405, S7-490, S7-1200, S7-1500, and S7-1500 Modbus TCP and Modbus RTU are Modbus ASCII and Modbus TCP for Master and Slave communication. OPTO-22, MTConnect and OPC UA, OPC DA.

In-Memory Computing Platform. Digital world is different. Microseconds are important. The world's most important organizations rely on us for powering their most sensitive applications at scale. If they meet the current requirement for immediate access, new data-enabled apps can transform your business. Hazelcast solutions can be used to complement any database and deliver results that are much faster than traditional systems of record. Hazelcast's distributed architecture ensures redundancy and continuous cluster up-time, as well as always available data to support the most demanding applications. The capacity grows with demand without compromising performance and availability. The cloud delivers the fastest in-memory data grid and third-generation high speed event processing.

Apache Hadoop is a software library that allows distributed processing of large data sets across multiple computers. It uses simple programming models. It can scale from one server to thousands of machines and offer local computations and storage. Instead of relying on hardware to provide high-availability, it is designed to detect and manage failures at the application layer. This allows for highly-available services on top of a cluster computers that may be susceptible to failures.

To make log data easily accessible and usable, it is important to have a single, unified layer of logging. However, existing tools fall short: legacy tools are not built for new cloud APIs and microservice-oriented architecture in mind and are not innovating quickly enough. Treasure Data created Fluentd to solve the problems of creating a unified log layer with a modular architecture and extensible plugin model. It also has a performance optimized engine. Fluentd Enterprise also addresses Enterprise requirements like Trusted Packaging. Security. Security.

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.

AppScope, a new approach in black-box instrumentation, delivers ubiquitous, unified Telemetry from any Linux executable. It does this by simply prepending scope on the command. Any customer who uses Application Performance Management will tell you how much they love the solution but wish they could expand it to more applications. Most customers have only 10% of their apps set up for APM and supplement what they can with basic metrics. What about the remaining 80%? Enter AppScope. No language-specific instrumentation. No application developers required. AppScope works with all applications, from the CLI to production. It is language-independent and completely usable. AppScope data can be sent to any existing monitoring tool or time series database. AppScope allows Ops teams and SREs to query running applications in order to determine how they behave in any deployment context. This includes on-prem, cloud, containers and cloud.

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.

Centralize, transform & stash your data. Logstash is an open, free server-side data processing platform that ingests data and transforms it before sending it to your favorite "stash". Logstash dynamically ingests and transforms your data, regardless of its format or complexity. With grok, you can create structure from unstructured data, decipher geo coordinates using IP addresses, anonymize sensitive fields or exclude them, and simplify overall processing. Data can be scattered or siloed across many different systems in many formats. Logstash can handle a variety inputs that pull events from many common sources at once. You can stream continuously and easily ingest logs, metrics web applications, data stores, as well as other AWS services in a continuous stream. Download: https://sourceforge.net/projects/logstash.mirror/

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.

Innovative, modern message queue and broker in a lightweight container designed to run in Kubernetes. Certified in the CNCF landscape. Connect natively to the cloud's native ecosystem.

Lightbend technology allows developers to quickly build data-centric applications that can handle the most complex, distributed applications and streaming data streams. Lightbend is used by companies around the world to address the problems of distributed, real-time data to support their most important business initiatives. Akka Platform is a platform that makes it easy for businesses build, deploy, manage, and maintain large-scale applications that support digitally transformational initiatives. Reactive microservices are a way to accelerate time-to-value, reduce infrastructure costs, and lower cloud costs. They take full advantage the distributed nature cloud and are highly efficient, resilient to failure, and able to operate at any scale. Native support for encryption, data destruction, TLS enforcement and compliance with GDPR. Framework to quickly build, deploy and manage streaming data pipelines.

One Platform for Realtime Communication: A platform to build and operate real-time interactivity for web, mobile, AI/ML, IoT, and Edge computing applications Faster & Easier Deployments: SDK support for 50+ mobile, web, server, and IoT environments (PubNub & community supported) and more than 65 pre-built integrations with external and third-party APIs to give you the features you need regardless of programming language or tech stack. Scalability: The industry’s most scalable platform capable of supporting millions of concurrent users for rapid growth with low latency, high uptime, and without financial penalties.

Oracle Coherence, the industry's leading in-memory grid solution, enables organizations to scale mission-critical applications quickly by providing quick access to frequently used data. Data volumes and customer expectations are increasing due to the "internet-of-things", mobile, cloud, social, and cloud. This means that organizations need to be able handle more data in real time, offload redundant shared data services, and ensure availability. The 14.1.1 release of Oracle Coherence adds a patented scalable message implementation, support for polyglot grid side programming on GraalVM and distributed tracing in grid. Coherence stores each piece in multiple members (one primary, one or more backup copies), so that any mutating operation is not considered complete until the backup(s). This ensures that your data grid is resilient to failure at all levels, from a single JVM to a whole data center.

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

NServiceBus is the most user-friendly service bus for.NET. NServiceBus is used by more than 50,000 developers every day. NServiceBus is backed by a solid distributed development methodology and a global community of experts, consultants, and contributors. It offers enterprise-grade scalability for your workflows. NServiceBus is used extensively in mission-critical systems, business domains, and usage scenarios. NServiceBus' performance and scalability are proven and ready to take on the most difficult assignments. Reliable integration with automatic restarts. Reliable by default. With configurable mechanisms to retry failures using best practices in messaging and lessons learned from thousands production usage scenarios. Workflow and background task scheduling.

You need a way of integrating applications and data across your enterprise to respond quickly to business needs. Red Hat®, a flexible messaging platform based on open-source communities like Apache ActiveMQ or Apache Kafka, is able to deliver reliable information and enable real-time integration and connection to the Internet of Things.

RabbitMQ is lightweight, portable, and easy to use on the premises or in the cloud. It supports multiple messaging protocols. RabbitMQ can both be deployed in distributed or federated configurations to meet high availability and scale requirements.

You can deliver customer experiences like never before with breakthrough data streaming capabilities Both the ecosystem and Kafka API are compatible. Redpanda BulletPredictable low latency with zero data loss. Redpanda BulletUp to 10x faster than Kafka Redpanda BulletEnterprise-grade support and hotfixes. Redpanda BulletAutomated backups for S3/GCS. Redpanda Bullet100% freedom of routine Kafka operations. Redpanda BulletSupports for AWS/GCP. Redpanda was built from the ground up to be easy to install and get running quickly. Redpanda's power will be evident once you have tried it in production. You can use the more advanced Redpanda functions. We manage all aspects of provisioning, monitoring, as well as upgrades. We do not have access to your cloud credentials. Sensitive data never leaves your environment. You can have it provisioned, operated, maintained, and updated for you. Configurable instance types. As your needs change, you can expand the cluster.

Redis Labs is the home of Redis. Redis Enterprise is the best Redis version. Redis Enterprise is more than a cache. Redis Enterprise can be free in the cloud with NoSQL and data caching using the fastest in-memory database. Redis can be scaled, enterprise-grade resilience, massive scaling, ease of administration, and operational simplicity. Redis in the Cloud is a favorite of DevOps. Developers have access to enhanced data structures and a variety modules. This allows them to innovate faster and has a faster time-to-market. CIOs love the security and expert support of Redis, which provides 99.999% uptime. Use relational databases for active-active, geodistribution, conflict distribution, reads/writes in multiple regions to the same data set. Redis Enterprise offers flexible deployment options. Redis Labs is the home of Redis. Redis JSON, Redis Java, Python Redis, Redis on Kubernetes & Redis gui best practices.

Amazon EventBridge is a serverless event bus that makes it easy to connect applications together using data from your own applications, integrated Software-as-a-Service (SaaS) applications, and AWS services. EventBridge provides a stream of real time data from event sources like Pagerduty, Datadog, and Zendesk. It routes that data to AWS Lambda. To build applications that respond in real-time to all your data sources, you can set up routing rules. EventBridge makes it easy for you to create event-driven apps. It handles event ingestion, delivery, authorization, security, and error handling. Your applications will become more interconnected by events. You need to spend more time finding and understanding the structure of events in order to write code that reacts to them.

Alooma allows data teams visibility and control. It connects data from all your data silos into BigQuery in real-time. You can set up and flow data in minutes. Or, you can customize, enrich, or transform data before it hits the data warehouse. Never lose an event. Alooma's safety nets make it easy to handle errors without affecting your pipeline. Alooma infrastructure can handle any number of data sources, low or high volume.

Amazon MQ is a managed messaging broker service for Apache ActiveMQ. It makes it easy to create and manage message brokers in the Cloud. Message brokers allow multiple software systems to communicate and exchange information, even though they may use different programming languages and platforms. Amazon MQ helps reduce your operational burden by managing ActiveMQ, an open-source message broker, provisioning, setup and maintenance. It uses industry-standard protocols and APIs for messaging to connect your existing applications to Amazon MQ. These include JMS, NMS and AMQP. Standardization means that you don't have to rewrite any message code in most cases when migrating to AWS.

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.

Amazon Simple Notification Service is a fully managed messaging platform that allows for system-to–system and app-to–person (A2P), communication. It allows you to communicate between systems using publish/subscribe patterns (pub/sub), which allow messaging between decoupled microservice apps. You can also communicate directly with users via SMS, mobile push, and email. The system-to–system pub/sub functionality allows for topic-based, high-throughput push-based, many–to-many messaging. Your publisher systems can use Amazon SNS topics to fanout messages to large numbers of customer endpoints or subscriber systems, including Amazon SQS queues and AWS Lambda functions. You can send messages to large numbers of users using the A2P messaging functionality. This allows you to use either a pub/sub or direct-publish message using one API.

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.

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.

All your ELT data pipelines, including custom ones, will be up and running in minutes. Your team can focus on innovation and insights. Unify all your data integration pipelines with one open-source ELT platform. Airbyte can meet all the connector needs of your data team, no matter how complex or large they may be. Airbyte is a data integration platform that scales to meet your high-volume or custom needs. From large databases to the long tail API sources. Airbyte offers a long list of connectors with high quality that can adapt to API and schema changes. It is possible to unify all native and custom ELT. Our connector development kit allows you to quickly edit and create new connectors from pre-built open-source ones. Transparent and scalable pricing. Finally, transparent and predictable pricing that scales with data needs. No need to worry about volume. No need to create custom systems for your internal scripts or database replication.

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.

Apache ActiveMQ®, a Java-based, open-source, multi-protocol message broker, is one of the most popular. It supports industry-standard protocols, so users can enjoy the benefits of client options across a wide range of languages and platforms. Connect to clients written in JavaScript and C++. Use the ubiquitous AMQP protocol to integrate multi-platform applications. Use STOMP over websockets to exchange messages between web applications. Manage your IoT devices using MQTT. Support your existing JMS infrastructure, and more. ActiveMQ has the flexibility and power to support any messaging use case. There are currently two ActiveMQ flavors available: the well-known "classic broker" and the "next-generation" broker code-named Artemis. Artemis will be the next major ActiveMQ version once it reaches sufficient feature parity with its "Classic" code base. Artemis has an initial migration document and a development roadmap.

StreamSets DataOps Platform. An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.

StreamNative redefines the streaming infrastructure by integrating Kafka MQ and other protocols into a unified platform that provides unparalleled flexibility and efficiency to modern data processing requirements. StreamNative is a unified platform that adapts to diverse streaming and messaging requirements in a microservices environment. StreamNative's comprehensive and intelligent approach to streaming and messaging empowers organizations to navigate with efficiency and agility the complexity and scalability in the modern data ecosystem. Apache Pulsar’s unique architecture decouples message storage from the message serving layer, resulting in a cloud-native data streaming platform. Scalable and elastic, allowing it to adapt to changing business needs and event traffic. Scale up to millions of topics using architecture that decouples computing from storage.

ZeroMQ (also known by OMQ or 0MQ or zmq), looks like an embedded networking library, but acts as a concurrency platform. It provides sockets that can carry atomic messages across multiple transports, including TCP, multicast, in-process, and inter-process. You can connect sockets N to N with patterns such as fan-out, pubsub, task distribution and request-reply. It is fast enough to be used as the fabric for clustered goods. Its asynchronous I/O allows you to build multicore applications that are scalable and can be used for asynchronous message processing tasks. It supports a number of language APIs and runs on all operating systems.

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.

Event Grid simplifies event-based apps. It is a single service that manages all events from any source to any destination. Event Grid is designed to be highly available, consistent in performance, and dynamically scale. This allows you to focus on your app logic, rather than infrastructure. Eliminate polling and the associated cost- and latency. Event Grid allows event publishers to be decoupled with event subscribers through a pub/sub model. This allows you to create scalable, serverless, microservices and distributed systems. You can gain massive scale dynamically while receiving near-real-time notifications about any changes that you are interested in. Reactive programming allows you to build better and more reliable applications, taking advantage of guaranteed event delivery and high availability in the cloud. Connect multiple sources and destinations of events to create richer applications.

Service-oriented architecture is now a reality. Warewolf allows developers the ability to create and manage microservices in a visual, flow-based, drag-and-drop environment. It's a revolution in the way we think about SOA and how we use it. Warewolf doesn't require that you learn anything new. Warewolf will be easy to use if you have used a drag-and-drop design tool. Complex integrations and systems can now be created as easily as a flowchart. Warewolf Studio allows you to design and layout your microservices exactly as you would normally. Next, enter the credentials and variables, and add connectors as needed. Finally, you can call Warewolf directly from your application.

Managed service for bidirectional communication between IoT devices (IoT) and Azure High-secure and reliable communication between your Internet of Things application and the devices it manages. Azure IoT Hub is a cloud-hosted back end that allows you to connect almost any device. Scaled provisioning, device management, per-device authentication and scaled provisioning allow you to extend your solution from the cloud up to the edge. You can use device-to-cloud data telemetry to determine the status of your devices and to define message routes to other Azure services. You can send commands and notifications to your connected devices reliably using cloud-to-device messaging. You can also track the delivery of messages with acknowledgement receipts. To accommodate intermittent connectivity, automatically resend messages to devices. Azure IoT Central - Proof of concept is not your goal. With a hosted IoT platform, we'll help you create industry-leading solutions.

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.

Airflow is a community-created platform that allows programmatically to schedule, author, and monitor workflows. Airflow is modular in architecture and uses a message queue for managing a large number of workers. Airflow can scale to infinity. Airflow pipelines can be defined in Python to allow for dynamic pipeline generation. This allows you to write code that dynamically creates pipelines. You can easily define your own operators, and extend libraries to suit your environment. Airflow pipelines can be both explicit and lean. The Jinja templating engine is used to create parametrization in the core of Airflow pipelines. No more XML or command-line black-magic! You can use standard Python features to create your workflows. This includes date time formats for scheduling, loops to dynamically generate task tasks, and loops for scheduling. This allows you to be flexible when creating your workflows.

Anypoint MQ allows advanced asynchronous messaging, such as queueing or pub/sub, with fully hosted and managed cloud messages queues and exchanges. Anypoint MQ is a service of Anypoint Platform™ and supports environments, business groups, role-based access control, and enterprise-grade functionality.

Apache Druid, an open-source distributed data store, is Apache Druid. Druid's core design blends ideas from data warehouses and timeseries databases to create a high-performance real-time analytics database that can be used for a wide range of purposes. Druid combines key characteristics from each of these systems into its ingestion, storage format, querying, and core architecture. Druid compresses and stores each column separately, so it only needs to read the ones that are needed for a specific query. This allows for fast scans, ranking, groupBys, and groupBys. Druid creates indexes that are inverted for string values to allow for fast search and filter. Connectors out-of-the box for Apache Kafka and HDFS, AWS S3, stream processors, and many more. Druid intelligently divides data based upon time. Time-based queries are much faster than traditional databases. Druid automatically balances servers as you add or remove servers. Fault-tolerant architecture allows for server failures to be avoided.

This is the easiest way to perform batch and streaming data processing. For mission-critical production workloads, write once and run anywhere data processing. Beam can read your data from any supported source, whether it's on-prem and in the cloud. Beam executes your business logic in both batch and streaming scenarios. Beam converts the results of your data processing logic into the most popular data sinks. A single programming model that can be used for both streaming and batch use cases. This is a simplified version of the code for all members of your data and applications teams. Apache Beam is extensible. TensorFlow Extended, Apache Hop and other projects built on Apache Beam are examples of Apache Beam's extensibility. Execute pipelines in multiple execution environments (runners), allowing flexibility and avoiding lock-in. Open, community-based development and support are available to help you develop your application and meet your specific needs.

A distributed data integration framework which simplifies common Big Data integration tasks such as data ingestion and replication, organization, and lifecycle management. It can be used for both streaming and batch data ecosystems. It can be run as a standalone program on a single computer. Also supports embedded mode. It can be used as a mapreduce application on multiple Hadoop versions. Azkaban is also available for the launch of mapreduce jobs. It can run as a standalone cluster, with primary and worker nodes. This mode supports high availability, and can also run on bare metals. This mode can be used as an elastic cluster in the public cloud. This mode supports high availability. Gobblin, as it exists today, is a framework that can build various data integration applications such as replication, ingest, and so on. Each of these applications are typically set up as a job and executed by Azkaban, a scheduler.

High-speed, data-intensive apps that meet all performance requirements can be built. Apache Geode's unique technology combines advanced techniques for data replication and partitioning with distributed processing. Apache Geode offers a database-like consistency model, reliable transactions processing, and a shared nothing architecture to maintain very low latency with high concurrency. Data can be easily partitioned (sharded), or replicated among nodes, allowing for performance scaling as required. Reliable in-memory copies are used to ensure durability. Disk-based persistence is also used to ensure longevity. Super fast write-ahead log (WAL) persistence that uses a shared-nothing architecture optimized for parallel recovery of nodes and entire clusters.

You can use Ignite as a traditional SQL Database by leveraging JDBC drivers or ODBC drivers. Or, you can use the native SQL APIs for Java, C# and C++, Python, or other programming languages. You can easily join, group, aggregate, or order your distributed on-disk and in-memory data. You can accelerate your existing applications up to 100x by using Ignite as an in memory cache or in-memory grid that is deployed over one of several external databases. You can query, transact, and calculate on this cache. Ignite is a database that scales beyond your memory capacity to support modern transactional and analytical workloads. Ignite allocates memory to your hot data and writes to disk when applications query cold records. Execute custom code up to kilobytes in size over petabytes. Your Ignite database can be transformed into a distributed supercomputer that can perform low-latency calculations, complex analysis, and machine learning.

Heron's architecture is rich in architectural improvements that help to increase efficiency. Heron is compatible with Apache Storm API, so no code changes are required to migrate. You can quickly identify and debug topologies issues, which allows for faster development. Heron UI provides a visual overview of each topology, allowing you to see hot spots and detailed counters to track progress and troubleshooting. Heron is highly adaptable in that it can execute large numbers of components for each topology as well as the ability launch and track large amounts of topologies.

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.

Apache RocketMQ™, a lightweight messaging engine and data processing platform, is an example of a unified message engine.