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Emisar.dev
Emisar is the last MCP server youโll need to install: a Zero-Trust gateway connecting Claude, Cursor, ChatGPT, and any AI agent to your infrastructure. One server handles production access, debugging, alerts, and internal operations, with new capabilities added as packs. Agents can inspect real production state, debug what they shipped, and help resolve incidents. Safe reads run automatically; policy allows, blocks, or routes risky actions for approval. No SSH keys, VPNs, remote shells, or standing shell access โ and every call is recorded.
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Emisar.dev's answer:
emisar is for SRE, DevOps, platform engineering, infrastructure, and security teams that want AI agents to inspect and operate production systems. It is especially relevant to teams managing multiple Linux hosts, clusters, databases, cloud services, or regulated environments where unrestricted shell access and incomplete audit records are unacceptable.
Emisar.dev's answer:
The hosted control plane and operator interface use Elixir, Phoenix, LiveView, PostgreSQL, and Tailwind CSS. The host runner and MCP bridge are written in Go. Action packs use YAML and JSON Schema, while production infrastructure is managed with Terraform on Google Cloud. The system communicates through MCP, OAuth 2.1, TLS, and WebSockets.
Emisar.dev's answer:
Emisar.dev's answer:
Founder Andrii Dryga spent a decade working as a CTO, full-stack engineer, SRE, and DevOps engineer. He experienced the cost of running the wrong command on the wrong cluster, while also seeing AI solve operational problems in seconds. emisar grew from the need to preserve both truths: AI agents are useful, and production access must remain bounded. Its answer is to give agents a reviewed catalog of operations instead of a blank terminal.
Emisar.dev's answer:
emisar lets AI agents work on real infrastructure without giving them a shell. Agents choose from a finite catalog of typed, versioned actions. Policy decides what runs, what requires approval, and what is denied, while an outbound-only runner verifies the action again on the host. New capabilities arrive as packs behind the same MCP integration, and every request is recorded in both a searchable audit trail and a tamper-evident host journal. [
Emisar.dev's answer:
Choose emisar when you want an agent to keep investigating and handling routine operations without handing it SSH credentials or supervising every call. Compared with raw shell access, copy-paste workflows, or one-off MCP servers, emisar provides reviewed action contracts, host-level enforcement, risk-based policy, scoped access, approvals, pack integrity checks, and a durable audit trail. It is built specifically for governed infrastructure access rather than generic automation.
Based on our record, Apache Flink seems to be more popular. It has been mentiond 46 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
When IoTDB was initiated in 2011, almost all influential distributed systems and databases were built in Java or on the JVMโsuch as Hadoop, HBase, Spark (Scala on JVM), Cassandra, Kafka, and Flink. To integrate deeply with the big data ecosystem, choosing Java was a natural decision. - Source: dev.to / 4 months ago
In the meantime, other query engine support is on the roadmap, including Apache Spark, Apache Flink, and others. - Source: dev.to / 12 months ago
Many stream processing systems today still rely on local disks and RocksDB to manage state. This model has been around for a while and works fine in simple, single-tenant setups. Apache Flink, for example, uses RocksDB as its default state backend - state is kept on local disks, and periodic checkpoints are written to external storage for recovery. - Source: dev.to / about 1 year ago
Because the hosted catalog is a standard JDBC catalog, tools like Spark, Trino, and Flink can still access your tables. For example:. - Source: dev.to / about 1 year ago
I wrote a python based aircraft monitor which polls the adsb.fi feed for aircraft transponder messages, and publishes each location update as a new event into an Apache Kafka topic. I used Apache Flink โ and more specially Flink SQL, to transform and analyse my flight data. The TL;DR summary is I can write SQL for my real-time data processing queries โ and get the scalability, fault tolerance, and low latency... - Source: dev.to / about 1 year ago
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.
Spark Mail - Spark helps you take your inbox under control. Instantly see whatโs important and quickly clean up the rest. Spark for Teams allows you to create, discuss, and share email with your colleagues
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.
Grails - An Open Source, full stack, web application framework for the JVM
