Dragonfly is a drop-in Redis replacement that cuts costs and boosts performance. Designed to fully utilize the power of modern cloud hardware and deliver on the data demands of modern applications, Dragonfly frees developers from the limits of traditional in-memory data stores. The power of modern cloud hardware can never be realized with legacy software. Dragonfly is optimized for modern cloud computing, delivering 25x more throughput and 12x lower snapshotting latency when compared to legacy in-memory data stores like Redis, making it easy to deliver the real-time experience your customers expect. Scaling Redis workloads is expensive due to their inefficient, single-threaded model. Dragonfly is far more compute and memory efficient, resulting in up to 80% lower infrastructure costs. Dragonfly scales vertically first, only requiring clustering at an extremely high scale. This results in a far simpler operational model and a more reliable system.
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Overmonitor is a cloud-based infrastructure, website, and endpoint monitoring platform built for fast setup without enterprise complexity. Monitor websites, APIs, servers, processes, Windows services, event logs, performance counters, SNMP data, uptime, response time, DNS, SSL certificates, internal endpoints, and network health from one dashboard.
The lightweight 2.1 MB agent runs as an idle process, requires no inbound firewall exceptions or open ports, and sends a secure heartbeat every minute. Configure monitoring quickly through the web interface and target checks by city-level geography. Maintenance windows suppress expected noise, while configurable alerts, push notifications, and audible dashboards keep the right people informed.
Multi-location endpoint checks validate incidents, reduce false alarms, expose regional problems, and reveal how availability and responsiveness differ across target markets.
Distributed Monitors expand Overmonitor’s network by allowing eligible servers to opt in and check up to 10 external endpoints. Checks add negligible data to the existing heartbeat, require no extra configuration or firewall changes, execute no remote web scripts, and store no monitored data on the server. If a participating server or network becomes unavailable, its checks are automatically reassigned. Participation also earns prorated invoice credits.
Trend Analysis turns historical infrastructure metrics into proactive capacity planning. Choose from thousands of performance counters, including CPU utilization, available memory, and disk space, then assign minimum and maximum thresholds. Once enough data has been collected, Overmonitor applies linear regression to estimate when a metric may cross its limits. A configurable planning horizon controls when responsible team members are alerted, helping you rebalance workloads, tune resources, or scale capacity before performance becomes an outage or unbudgeted emergency. Trend Analysis is included with every server metric at no additional charge.
Overmonitor also includes process rollups, embeddable performance graphs, endpoint health visibility, flexible retention, multiple users with roles, and à la carte pricing so you pay only for the monitoring you need. Detect outages, correlate website symptoms with server health, analyze performance, track availability, and improve end-user experience before small issues become downtime.
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Materialize
Materialize is a reactive database that delivers incremental view updates. We help developers easily build with streaming data using standard SQL. Materialize can connect to many different external sources of data without pre-processing. Connect directly to streaming sources like Kafka, Postgres databases, CDC, or historical sources of data like files or S3. Materialize allows you to query, join, and transform data sources in standard SQL - and presents the results as incrementally-updated Materialized views. Queries are maintained and continually updated as new data streams in. With incrementally-updated views, developers can easily build data visualizations or real-time applications. Building with streaming data can be as simple as writing a few lines of SQL.
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