Alternatives to Leo

Cloud-based solution for observability that helps businesses manage and track workload and performance through a single dashboard. Monitor all the services you run on your cloud without compromising cost, granularity or scale. Groundcover is a cloud-native APM solution that makes observability easy so you can focus on creating world-class products. Groundcover's proprietary sensor unlocks unprecedented granularity for all your applications. This eliminates the need for costly changes in code and development cycles, ensuring monitoring continuity.

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

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.

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.

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

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

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

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

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

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

Aiven manages your open-source data infrastructure in the cloud so that you don't have. Developers can do what is best for them: create applications. We do what we love: manage cloud data infrastructure. All solutions are open-source. You can also freely transfer data between clouds and create multi-cloud environments. You will know exactly what you will be paying and why. We combine storage, networking, and basic support costs. We will keep your Aiven software up and running. We will be there to help you if there is ever an issue. In 10 minutes, you can deploy a service on Aiven. 1. Register now - No credit card information required 2. Select your open-source service and choose the region and cloud to deploy to it 3. Select your plan and get $300 in credit 4. Click "Create service" to configure your data sources

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.

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

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

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

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

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

The Open, Multi-Cloud Stack to Modern Data Apps. Built on Apache Cassandra™, an open-source Apache Cassandra™. Global scale and 100% uptime without vendor lock in You can deploy on multi-clouds, open-source, on-prem and Kubernetes. For a lower TCO, use elastic and pay-as you-go. Stargate APIs allow you to build faster with NoSQL, reactive, JSON and REST. Avoid the complexity of multiple OSS projects or APIs that don’t scale. It is ideal for commerce, mobile and AI/ML. Get building modern data applications with Astra, a database-as-a-service powered by Apache Cassandra™. Richly interactive apps that are viral-ready and elastic using REST, GraphQL and JSON. Pay-as you-go Apache Cassandra DBaaS which scales easily and affordably

Real-time analytics on raw data. Live ingest from S3, DynamoDB, DynamoDB and more. Raw data can be accessed as SQL tables. In minutes, you can create amazing data-driven apps and live dashboards. Rockset is a serverless analytics and search engine that powers real-time applications and live dashboards. You can directly work with raw data such as JSON, XML and CSV. Rockset can import data from real-time streams and data lakes, data warehouses, and databases. You can import real-time data without the need to build pipelines. Rockset syncs all new data as it arrives in your data sources, without the need to create a fixed schema. You can use familiar SQL, including filters, joins, and aggregations. Rockset automatically indexes every field in your data, making it lightning fast. Fast queries are used to power your apps, microservices and live dashboards. Scale without worrying too much about servers, shards or pagers.

The Edge allows you to analyze and take action on your data. Big Data can be made small and relevant. All your assets can be used to collect sensor data. Connect any sensor, PLC or DCS and Historian. Condition monitoring of remote assets. Industry 4.0 data collection and integration Data flows can combine streaming and enterprise data. You can use your favorite Cloud Provider, or your own data centre for data storage. Crosser Edge MLOps functionality allows you to create, manage, and deploy your own ML models. Crosser Edge Node can run any ML framework. Crosser cloud central resource library for your trained model. Drag-and-drop is used for all other steps of the data pipeline. One operation is all it takes to deploy ML models on any number of Edge Nodes. Crosser Flow Studio enables self-service innovation. A rich library of pre-built modules is available. Facilitates collaboration between teams and sites. No more dependence on a single member of a team.

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

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

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

Pipes is a new way of creating queries and shaping data. It's inspired by Python Notebooks. This is a simplified way to increase performance without sacrificing complexity. Splitting your query into multiple nodes makes it easier to develop and maintain. You can activate your production-ready API endpoints in one click. Transforms happen on-the-fly, so you always have the most current data. You can share secure access to your data with one click, and get consistent results. Tinybird scales linearly, so don't worry if you have high traffic. Imagine if you could transform any Data Stream or CSV file into a secure real-time analytics API endpoint in a matter minutes. We believe in high-frequency decision making for all industries, including retail, manufacturing and telecommunications.

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.

Cloud Foundry and Kubernetes support microservice-based streaming and batch processing. Spring Cloud Data Flow allows you to create complex topologies that can be used for streaming and batch data pipelines. The data pipelines are made up of Spring Boot apps that were built using the Spring Cloud Stream and Spring Cloud Task microservice frameworks. Spring Cloud Data Flow supports a variety of data processing use cases including ETL, import/export, event streaming and predictive analytics. Spring Cloud Data Flow server uses Spring Cloud Deployer to deploy data pipelines made from Spring Cloud Stream and Spring Cloud Task applications onto modern platforms like Cloud Foundry or Kubernetes. Pre-built stream and task/batch starter applications for different data integration and processing scenarios allow for experimentation and learning. You can create custom stream and task apps that target different middleware or services using the Spring Boot programming model.

We know how simple Apache Kafka®, can be when you have the right tools. kPow was created to simplify the Kafka development experience and save businesses time and money. kPow makes it easy to find the root cause of production problems in a matter of clicks and not hours. With kPow's Data Inspect and kREPL functions, you can search tens of thousands messages per second. Are you new to Kafka kPow's Kafka UI is unique and allows developers to quickly understand the core Kafka concepts. You can upskill new members of your team and increase your Kafka knowledge. kPow offers a range of Kafka management features and monitoring capabilities in a single Docker Container. You can manage multiple clusters and schema registries. Connect installs with one instance.

Red Hat®, OpenShift®, Streams for Apache Kafka provides a simplified developer experience for building, scaling, and modernizing cloud-native apps or upgrading existing systems. Red Hat OpenShift streams for Apache Kafka make it easy to create, discover and connect to real-time data stream no matter where they're deployed. Streams are essential for delivering event-driven or data analytics applications. The seamless operation of distributed microservices, large data transfers volumes, and managed operations allow teams to focus on their strengths, speed up value, and lower operating costs. OpenShift streams for Apache Kafka is part of the Red Hat OpenShift product range, which allows you to build a wide variety of data-driven solutions.

Simplifies data transfer between components in microservices architectures. When used as a microservice transport, it simplifies integration and increases reliability. It also improves scaling. Read and write data near real-time. Set the data throughput to your needs. You can configure the resources to process data streams in granular detail, from 100 KB/s up to 100 MB/s. Yandex Data Transfer allows you to send a single data stream to multiple destinations with different retention policies. Data is automatically replicated over multiple geographically dispersed availability zones. Once created, data streams can be managed centrally via the management console or API. Yandex Data Streams is able to collect data continuously from sources such as website browsing histories, system and application logs, or social media feeds. Yandex Data Streams can continuously collect data from sources like website browsing histories, logs of application, etc.

Integrate legacy systems seamlessly into the next-gen cloud or data platforms with one solution. Connect allows you to take control of your data, from mainframe to cloud. Integrate data via batch and real-time input for advanced analytics, comprehensive machinelearning and seamless data migration. Connect draws on the decades of experience Precisely has gained as a leader in mainframe sorting and IBM i data availability security. This allows the company to be a leader in the field of complex data access and integration. Access to all enterprise data is possible for critical business projects. Connect supports a wide range targets and sources for all your ELT/CDC needs.

Aerospike is the global leader for next-generation, real time NoSQL data solutions at any scale. Aerospike helps enterprises overcome seemingly impossible data bottlenecks and compete with other companies at a fraction of the cost and complexity of legacy NoSQL databases. Aerospike's Hybrid Memory Architecture™ is a patented technology that unlocks the full potential of modern hardware and delivers previously unimaginable value. It does this by delivering unimaginable value from huge amounts of data at both the edge, core, and in the cloud. Aerospike empowers customers with the ability to instantly combat fraud, dramatically increase shopping cart sizes, deploy global digital payment networks, and provide instant, one-to-1 personalization for millions. Aerospike customers include Airtel and Banca d'Italia as well as Snap, Verizon Media, Wayfair, PayPal, Snap, Verizon Media, and Nielsen. The company's headquarters is in Mountain View, California. Additional locations are in London, Bengaluru, India, and Tel Aviv in Israel.

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.

The Flowcore platform combines event streaming and event sourcing into a single service that is easy to use. Data flow and replayable data storage designed for developers in data-driven startups or enterprises that want to remain at the forefront of growth and innovation. All data operations are efficiently preserved, ensuring that no valuable data will ever be lost. Immediate transformations, reclassifications and loading of your data to any destination. Break free from rigid data structure. Flowcore's scalable architectural design adapts to your business growth and handles increasing volumes of data without difficulty. By streamlining and simplifying backend data processes, you can allow your engineering teams to focus on what they are best at, creating innovative products. Integrate AI technologies better, enhancing your products with smart data-driven solutions. Flowcore was designed with developers in mind but its benefits go beyond the dev team.

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

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

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.

Streamkap is a modern streaming ETL platform built on top of Apache Kafka and Flink, designed to replace batch ETL with streaming in minutes. It enables data movement with sub-second latency using change data capture for minimal impact on source databases and real-time updates. The platform offers dozens of pre-built, no-code source connectors, automated schema drift handling, updates, data normalization, and high-performance CDC for efficient and low-impact data movement. Streaming transformations power faster, cheaper, and richer data pipelines, supporting Python and SQL transformations for common use cases like hashing, masking, aggregations, joins, and unnesting JSON. Streamkap allows users to connect data sources and move data to target destinations with an automated, reliable, and scalable data movement platform. It supports a broad range of event and database sources.

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

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.

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

RudderStack is the smart customer information pipeline. You can easily build pipelines that connect your entire customer data stack. Then, make them smarter by pulling data from your data warehouse to trigger enrichment in customer tools for identity sewing and other advanced uses cases. Start building smarter customer data pipelines today.

Deliver Continuous Intelligence Solutions easier, faster and more cost-effectively with less engineering effort. The Cogility Cogynt Platform delivers cloud-scalable, Expert AI-based analytics-powered event stream processing software. A complete integrated toolset allows organizations to deliver continuous intelligence solutions quickly, easily and more efficiently. The end-toend platform streamlines deployment by streamlining model logic, customizing the data source intake, processing of data streams, examining and visualizing intelligence findings, sharing them, auditing, and improving results. Cogynt’s Authoring Tool is a convenient design environment that uses zero-code for creating, updating and deploying data model. Cogynt Data Management Tool allows you to quickly publish your model for immediate application to stream data processing, while abstracting Flink Job Coding.

You can start by opening a link or using our client libraries with batching support. Track signups, response time, MRR, and other metrics - and visualize your data in a beautiful dashboard. Create beautiful TV modes to organize your metrics. Send additional data to compare the differences between categories. Our powerful and simple libraries make it easy to integrate with NodeJS. All data is stored and accessible at a 1-minute resolution. Your data is kept at a 1-Minute resolution as long as your customer. Invite your team members to share your dashboards. We made sure that all data loaded as fast as possible. Data doesn't need to look boring. We made it beautiful.

Build and share real-time business dashboards without the hassle. Geckoboard integrates with over 80 tools and services to help you pull in your data and get a professional-looking dashboard in front of others in a matter of minutes. Create dashboards directly in your browser with a straightforward, drag-and-drop interface, and bring important numbers, metrics and KPIs out of lifeless reports. When ready, share your dashboard with a link, invite your teammates, schedule email and Slack updates to go out automatically. For maximum visibility, Geckoboard has ‘Send to TV’, allowing you to pair your account with a browser on a large screen or TV, and pick which dashboards you’d like displayed on there. It can even loop through several dashboard on one screen. We’ve got easy-to-follow instructions for how to achieve this in an afternoon using affordable off the shelf hardware.

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.

Akka is a toolkit that allows you to build highly concurrent, distributed and resilient message-driven Java and Scala applications. Akka Insights, an intelligent monitoring and observability tool, is specifically designed for Akka. Actors and streams allow you to build systems that scale up by using more server resources and out by using multiple servers. You can build systems that self-heal and remain responsive to failures by using the principles of The Reactive Manifesto Akka. Distributed systems that are resilient to failure. Load balancing across nodes and adaptive routing. Cluster Sharding is used for event sourcing and CQRS. Distributed Data to ensure eventual consistency with CRDTs Backpressure and asynchronous stream processing. A great platform for microservice development is the fully async streaming HTTP server and client. Alpakka streams integrations.

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

Estuary Flow, a new DataOps platform, empowers engineering teams with the ability to build data-intensive real-time applications at scale and with minimal friction. This platform allows teams to unify their databases, pub/sub and SaaS systems around their data without having to invest in new infrastructure or development.

The industry's most comprehensive, API-driven, microservices-based, AI-powered enterprise iPaaS is here to help you go beyond the table. IICS is powered by the CLAIRE engine and supports any cloud-native patterns, including data, applications, API integration, MDM, and API integration. Our multi-cloud support and global distribution covers Microsoft Azure, AWS and Google Cloud Platform. Snowflake is also included. IICS has the industry's highest trust and enterprise scale, as well as the industry's highest security certifications. Our enterprise iPaaS offers multiple cloud data management products that can be used to increase productivity, speed up scaling, and increase efficiency. Informatica is a Leader in the Gartner 2020 Magic Quadrant Enterprise iPaaS. Informatica Intelligent Cloud Services reviews and real-world insights are available. Get our cloud services for free. Customers are our number one priority, across products, services, support, and everything in between. We have been able to earn top marks in customer loyalty 12 years running.

Flume is a reliable, distributed service that efficiently collects, aggregates, and moves large amounts of log data. Flume's architecture is based on streaming data flows and is simple and flexible. It is robust and fault-tolerant, with many failovers and recovery options. It is based on a simple extensible data structure that allows for online analytical applications. Flume 1.8.0 has been released by the Apache Flume team. Flume is a distributed, reliable and available service that efficiently collects, aggregates, and moves large amounts of streaming event information.