Apache Mesos Integrations

Monitor your servers, containers, and applications, in high-resolution and in real-time. Netdata collects metrics per second and presents them in beautiful low-latency dashboards. It is designed to run on all of your physical and virtual servers, cloud deployments, Kubernetes clusters, and edge/IoT devices, to monitor your systems, containers, and applications. It scales nicely from just a single server to thousands of servers, even in complex multi/mixed/hybrid cloud environments, and given enough disk space it can keep your metrics for years. KEY FEATURES: Collects metrics from 800+ integrations Real-Time, Low-Latency, High-Resolution Unsupervised Anomaly Detection Powerful Visualization Out of box Alerts systemd Journal Logs Explorer Low Maintenance Open and Extensible Troubleshoot slowdowns and anomalies in your infrastructure with thousands of per-second metrics, meaningful visualisations, and insightful health alarms with zero configuration. Netdata is different. Real-Time data collection and visualization. Infinite scalability baked into its design. Flexible and extremely modular. Immediately available for troubleshooting, requiring zero prior knowledge and preparation.

Docker streamlines tedious configuration processes and is utilized across the entire development lifecycle, facilitating swift, simple, and portable application creation on both desktop and cloud platforms. Its all-encompassing platform features user interfaces, command-line tools, application programming interfaces, and security measures designed to function cohesively throughout the application delivery process. Jumpstart your programming efforts by utilizing Docker images to craft your own distinct applications on both Windows and Mac systems. With Docker Compose, you can build multi-container applications effortlessly. Furthermore, it seamlessly integrates with tools you already use in your development workflow, such as VS Code, CircleCI, and GitHub. You can package your applications as portable container images, ensuring they operate uniformly across various environments, from on-premises Kubernetes to AWS ECS, Azure ACI, Google GKE, and beyond. Additionally, Docker provides access to trusted content, including official Docker images and those from verified publishers, ensuring quality and reliability in your application development journey. This versatility and integration make Docker an invaluable asset for developers aiming to enhance their productivity and efficiency.

Pliant offers a robust solution for IT Process Automation that simplifies, enhances, and secures the way teams create and implement automation. By minimizing human errors, ensuring compliance, and boosting overall efficiency, Pliant serves as an invaluable resource. Users can easily incorporate existing automation or develop new workflows through a unified orchestration interface. The platform provides reliable governance that maintains compliance through practical, built-in features. By abstracting thousands of vendor APIs, Pliant creates intelligent action blocks that empower users to simply drag and drop, eliminating the need for repetitive coding. Citizen developers can seamlessly construct effective and uniform automation across various platforms, services, and applications within minutes, thereby maximizing the value of their entire technology ecosystem from a single interface. Furthermore, with the capability to integrate new APIs in just 15 business days, Pliant ensures that any non-standard requirements will be addressed in a leading timeframe, keeping your automation capabilities up to date. This efficiency allows teams to remain agile and responsive in a rapidly changing technological landscape.

SolarWinds®, AppOptics™, is a SaaS-based infrastructure and application monitoring tool for custom-built on-premises, hybrid, and cloud systems. AppOptics reduces MTTR by allowing quick identification of performance issues across the stack, from the application to the underlying infrastructure down to the line code. AppOptics was designed to be easy to use and set up by IT professionals. It has powerful features that quickly and automatically identify performance issues, eliminating the guesswork and reducing the time spent on troubleshooting. AppOptics allows you to align infrastructure and performance objectives with business objectives.

Fidelis Halo, a SaaS-based cloud security platform, automates cloud computing security controls. It also provides compliance across containers, servers, and IaaS within any public, private or hybrid cloud environment. Halo's extensive automation capabilities allow for faster workflows between InfoSec (DevOps) and Halo with over 20,000 pre-configured policies and more than 150 policy templates. These templates cover standards like PCI, CIS and HIPAA. The comprehensive, bidirectional Halo API, SDK, and toolkit automate security and compliance controls in your DevOps toolchain. This allows you to identify and correct critical vulnerabilities before they go into production. Free Halo Cloud Secure edition includes full access to the Halo Cloud Secure CSPM Service for up to 10 cloud service account across any mix of AWS and Azure. Get started now to automate your cloud security journey!

Kapacitor serves as a dedicated data processing engine for InfluxDB 1.x and is also a core component of the InfluxDB 2.0 ecosystem. This powerful tool is capable of handling both stream and batch data, enabling real-time responses through its unique programming language, TICKscript. In the context of contemporary applications, merely having dashboards and operator alerts is insufficient; there is a growing need for automation and action-triggering capabilities. Kapacitor employs a publish-subscribe architecture for its alerting system, where alerts are published to specific topics and handlers subscribe to these topics for updates. This flexible pub/sub framework, combined with the ability to execute User Defined Functions, empowers Kapacitor to function as a pivotal control plane within various environments, executing tasks such as auto-scaling, stock replenishment, and managing IoT devices. Additionally, Kapacitor's straightforward plugin architecture allows for seamless integration with various anomaly detection engines, further enhancing its versatility and effectiveness in data processing.

Riak®, TS is an enterprise-grade NoSQL Time Series Database that is specifically designed for IoT data and Time Series data. It can ingest, transform, store, and analyze massive amounts of time series information. Riak TS is designed to be faster than Cassandra. Riak TS masterless architecture can read and write data regardless of network partitions or hardware failures. Data is evenly distributed throughout the Riak ring. By default, there are three copies of your data. This ensures that at least one copy is available for reading operations. Riak TS is a distributed software system that does not have a central coordinator. It is simple to set up and use. It is easy to add or remove nodes from a cluster thanks to the masterless architecture. Riak TS's masterless architecture makes it easy for you to add or remove nodes from your cluster. Adding nodes made of commodity hardware to your cluster can help you achieve predictable and almost linear scale.

Marathon serves as a robust container orchestration platform that integrates seamlessly with Mesosphere’s Datacenter Operating System (DC/OS) and Apache Mesos, ensuring high availability through its active/passive clustering and leader election mechanism, which guarantees continuous uptime. It supports multiple container runtimes, offering first-class integration for Mesos containers utilizing cgroups as well as Docker, making it adaptable to various development environments. Additionally, Marathon facilitates the deployment of stateful applications by allowing persistent storage volumes to be linked to your apps, which is particularly beneficial for running databases such as MySQL and Postgres with storage managed by Mesos. The platform boasts an intuitive and powerful user interface, along with a range of service discovery and load balancing options to suit diverse needs. Health checks are implemented to monitor application performance via HTTP or TCP checks, ensuring reliability. Users can also set up event subscriptions by providing an HTTP endpoint to receive notifications, which can aid in integrating with external load balancers. Lastly, metrics can be queried in JSON format at the /metrics endpoint, while also being capable of integration with popular systems like Graphite, StatsD, DataDog, or scraped using Prometheus, allowing for comprehensive monitoring and analysis of application performance. This combination of features positions Marathon as a versatile tool for managing containerized applications effectively.

Apache Spark™ serves as a comprehensive analytics platform designed for large-scale data processing. It delivers exceptional performance for both batch and streaming data by employing an advanced Directed Acyclic Graph (DAG) scheduler, a sophisticated query optimizer, and a robust execution engine. With over 80 high-level operators available, Spark simplifies the development of parallel applications. Additionally, it supports interactive use through various shells including Scala, Python, R, and SQL. Spark supports a rich ecosystem of libraries such as SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming, allowing for seamless integration within a single application. It is compatible with various environments, including Hadoop, Apache Mesos, Kubernetes, and standalone setups, as well as cloud deployments. Furthermore, Spark can connect to a multitude of data sources, enabling access to data stored in systems like HDFS, Alluxio, Apache Cassandra, Apache HBase, and Apache Hive, among many others. This versatility makes Spark an invaluable tool for organizations looking to harness the power of large-scale data analytics.

Witboost is an adaptable, high-speed, and effective data management solution designed to help businesses fully embrace a data-driven approach while cutting down on time-to-market, IT spending, and operational costs. The system consists of various modules, each serving as a functional building block that can operate independently to tackle specific challenges or be integrated to form a comprehensive data management framework tailored to your organization’s requirements. These individual modules enhance particular data engineering processes, allowing for a seamless combination that ensures swift implementation and significantly minimizes time-to-market and time-to-value, thereby lowering the overall cost of ownership of your data infrastructure. As urban environments evolve, smart cities increasingly rely on digital twins to forecast needs and mitigate potential issues, leveraging data from countless sources and managing increasingly intricate telematics systems. This approach not only facilitates better decision-making but also ensures that cities can adapt efficiently to ever-changing demands.

Address the challenges of complex tooling and excessive workload by utilizing a single, streamlined networking and security solution. By consolidating your tools, you can reduce the time spent on tedious context switches, ultimately minimizing swivel-chair fatigue. TF excels in plugin integration, consistently going beyond the bare essentials to offer advanced capabilities that many other SDN plugins simply lack. It facilitates seamless network interactions, ensuring that your infrastructure is interconnected rather than isolated by embracing widely accepted open protocol standards in both the control and data planes. The open-source nature of TF fosters continuous innovation from various contributors, granting you the flexibility to tailor results to meet your specific needs or collaborate with trusted vendors. Moreover, it provides options for namespace isolation and micro-segmentation on a per-microservice basis, allowing for customizable security rules and tenant configurations. This adaptability positions TF as a vital tool for organizations looking to enhance their network security and operational efficiency.

OES boasts high availability and scalability, making it suitable for managing growing deployment workloads, while also being adaptable enough to work seamlessly with various SDLC tool chains. It provides a user-friendly interface for defining custom stages that allow for simultaneous deployments across multiple targets, significantly reducing time spent on deployment. Actions such as rolling back, moving forward, or halting all parallel deployments can be performed effortlessly with just a click. Additionally, the platform enables automation of repetitive tasks within the SDLC process by allowing the creation of numerous child pipelines that can be triggered from a parent pipeline. With its modular design and API-based architecture, OES functions effectively as a central Continuous Delivery (CD) tool for numerous enterprises. This flexibility allows developers on different teams to easily connect external services with Spinnaker for streamlined deployment orchestration, enhancing overall productivity and collaboration. As a result, OES stands out as a powerful solution for optimizing deployment processes across various environments.

MLlib, the machine learning library of Apache Spark, is designed to be highly scalable and integrates effortlessly with Spark's various APIs, accommodating programming languages such as Java, Scala, Python, and R. It provides an extensive range of algorithms and utilities, which encompass classification, regression, clustering, collaborative filtering, and the capabilities to build machine learning pipelines. By harnessing Spark's iterative computation features, MLlib achieves performance improvements that can be as much as 100 times faster than conventional MapReduce methods. Furthermore, it is built to function in a variety of environments, whether on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or within cloud infrastructures, while also being able to access multiple data sources, including HDFS, HBase, and local files. This versatility not only enhances its usability but also establishes MLlib as a powerful tool for executing scalable and efficient machine learning operations in the Apache Spark framework. The combination of speed, flexibility, and a rich set of features renders MLlib an essential resource for data scientists and engineers alike.

Apache OpenWhisk is a distributed, open-source Serverless platform designed to execute functions (fx) in response to various events, scaling seamlessly according to demand. By utilizing Docker containers, OpenWhisk takes care of the underlying infrastructure and server management, allowing developers to concentrate on creating innovative and efficient applications. The platform features a programming model where developers can implement functional logic (termed Actions) in any of the supported programming languages, which can be scheduled dynamically and executed in reaction to relevant events triggered by external sources (Feeds) or through HTTP requests. Additionally, OpenWhisk comes with a REST API-based Command Line Interface (CLI) and various tools to facilitate service packaging, cataloging, and deployment options for popular container frameworks. As a result of its container-based architecture, Apache OpenWhisk is compatible with numerous deployment strategies, whether locally or in cloud environments, giving developers the flexibility they need. The versatility of OpenWhisk also enables it to integrate with a wide range of services, enhancing its utility in modern application development.

Aurora manages applications and services across a communal array of machines, ensuring their continuous operation. In the event of machine failures, Aurora adeptly reallocates those jobs to functioning machines. During job updates, it assesses the health and status of the deployment, automatically reverting changes if required. To ensure that certain applications receive guaranteed resources, Aurora employs a quota system and accommodates multiple users for service deployment. The services are highly customizable through a Domain-Specific Language (DSL) that facilitates templating, which helps in creating standard patterns and reducing repetitive configurations. Additionally, Aurora communicates the services to Apache ZooKeeper, enabling client discovery through tools like Finagle. This comprehensive approach allows for efficient management and deployment of services in a dynamic environment.

Apache Flink serves as a powerful framework and distributed processing engine tailored for executing stateful computations on both unbounded and bounded data streams. It has been engineered to operate seamlessly across various cluster environments, delivering computations with impressive in-memory speed and scalability. Data of all types is generated as a continuous stream of events, encompassing credit card transactions, sensor data, machine logs, and user actions on websites or mobile apps. The capabilities of Apache Flink shine particularly when handling both unbounded and bounded data sets. Its precise management of time and state allows Flink’s runtime to support a wide range of applications operating on unbounded streams. For bounded streams, Flink employs specialized algorithms and data structures optimized for fixed-size data sets, ensuring remarkable performance. Furthermore, Flink is adept at integrating with all previously mentioned resource managers, enhancing its versatility in various computing environments. This makes Flink a valuable tool for developers seeking efficient and reliable stream processing solutions.