When dashboards must answer in under a second over billions of streaming events, Apache Druid is the real-time analytics database built for the job. Point a supervisor at an Apache Kafka topic or Amazon Kinesis stream and events are queryable on arrival, late data included, with exactly-once guarantees. Batch loads run as SQL: INSERT and REPLACE statements ingest files, and EXTERN reads external sources. Schema auto-discovery adds new columns and types as data lands. Joins run at ingestion or at query time. A web console gathers the daily work in one place: a tabbed Query view with EXPLAIN PLAN and query history, a Connect external data wizard, and pages for datasources, segments, retention rules, supervisors, tasks, services, and lookups. Configurable tiers place hot and cold data on different hardware. Quality-of-service settings shield priority workloads from heavy ones. Clickstream analytics, ad campaign reporting, network telemetry, application performance tracking, and IoT metrics all fit. Applications connect over the HTTP SQL API or the Avatica JDBC driver, and native JSON queries remain available for advanced needs. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Some questions are really about how things connect, and Neo4j is the graph database that stores your data as nodes and relationships so those connections become the thing you query. Model people, products, accounts, devices, or transactions as labeled nodes with properties, then draw typed, directed relationships between them and traverse multi-hop paths in a single Cypher query instead of stacking SQL joins. The built-in Neo4j Browser renders query results as interactive node diagrams, so you can expand a fraud ring, a dependency chain, or a recommendation neighborhood visually and share the picture with your team. Cypher reads like the picture it queries: MATCH patterns describe shapes in the graph, filters narrow them, and aggregations roll up counts, sums, and shortest paths. Store embeddings as node properties and build vector indexes over them, then combine similarity search with graph traversal so matches can be filtered by owner, connections, or freshness. Full-text indexes search names, descriptions, and documents, while uniqueness constraints keep identifiers honest at write time. Bulk-load millions of nodes from CSV files with the offline import tool, then keep data flowing in through official drivers for Python, JavaScript, Java, Go, and .NET applications. Fraud teams trace shared phones and cards between accounts, platform teams map microservice dependencies before a migration, and security analysts hunt lateral movement through identity graphs. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. GPL-3.0 licensed.

On the factory floor, the data already exists; it is just locked inside machines, PLCs, and protocols nobody on the analytics team speaks. StreamPipes unlocks it with guided adapters for OPC UA (including a web-based node browser), MQTT, Kafka, Pulsar, REST services, and industrial controllers, turning each source into a live stream in minutes. Domain experts then drag those streams into the pipeline editor, chain processors and sinks on a canvas, and watch connection validation check schema compatibility as they build, with live preview showing real values before anything is saved. Pipelines filter, enrich, aggregate, and route machine data, raise alerts, and write results to the built-in time-series data lake, where the data explorer charts history and spots trends. Finished pipelines run under an overview that starts, stops, and bulk-manages them, while detail views expose logs, metrics, status, and the pipeline as code. Live dashboards carry the same data to shop-floor screens and operations centers, organized around assets so a line, a cell, or a single machine becomes the navigation unit. Developers extend everything with custom adapters, processors, and sinks built on the Java SDK and deployed as microservices, or drive the platform from Python, Go, and Java clients. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

No cookies, no fingerprinting, no cross-site tracking, no personal data collection - Umami's privacy contract is the foundation of the open-source web analytics platform. IP addresses are hashed rather than stored, which makes it GDPR, CCPA, and PECR compliant by default - the consent banner can come off the site entirely. The tracking script is under 2 KB, roughly 20x smaller than Google Analytics, so measurement stops being a page-weight tax. The dashboard covers the core metrics - pageviews, visitors, bounce rate, visit duration, referrers, browsers, devices, and countries - with any date range and filtering by country or device. Beyond pageviews, custom events track clicks, form submissions, and signups via a data attribute or one JavaScript call, and advanced reports add funnels, user journeys, retention and cohort analysis, goals, and automatic UTM campaign tracking. Anonymous session views show individual visitor activity without identifying anyone. Teams share websites with role-based access, one instance manages unlimited sites, and a full REST API exposes every metric programmatically. MIT-licensed and self-hosted on PostgreSQL or MySQL via Docker, your analytics data never leaves your infrastructure.

Pageviews tell you someone arrived; Pug is an open-source product analytics platform that shows what people actually did next, across events, funnels, retention, and user flows. Capture events from browser, Android, Flutter, Node, Go, or Ruby SDKs into an ingestion pipeline that enriches each hit with geography, user agent, bot detection, and web attribution automatically. Identify and alias the users behind events, then open a profile to see traits, sessions, and the full activity history of a person. Build insights as trends, funnels, retention cohorts, segmentation, Sankey user flows, and top-K breakdowns, with filters and period-over-period comparison on every chart. A web analytics overview is generated from your events with visitors, sessions, pageviews, bounce rate, and traffic sources, requiring no tile wiring. The live view plots every current visitor on a real-time world map, down to the person and the event they just fired. Compose dashboards from insight and markdown tiles on a time-windowed grid that analysts rearrange without touching ingestion. Erase a person's events and profile on request to satisfy GDPR and DPDP data-subject obligations. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

Your AI application is burning through API tokens faster than you can refresh the billing page, and you have no idea which prompt template is responsible. OpenLIT plugs that visibility gap with a self-hosted observability platform built specifically for LLM workloads. Add one line of code to instrument 90+ LLM providers, agent frameworks, and vector databases, then watch every request flow through a tracing dashboard that shows tokens consumed, latency measured, and dollars spent per call, per model, per environment. The requests view lists every LLM interaction with provider, model, cost, and token breakdown in a filterable table, while the trace detail panel lets you drill into individual spans to read the exact prompt sent and response received. Prompt Hub turns prompts into versioned artifacts you deploy, rollback, and A/B test without touching application code. OpenGround compares models side by side on the same input, so you can evaluate cost-versus-quality tradeoffs before committing to a provider. Automated evaluations run LLM-as-a-judge scoring on live production traces, flagging hallucinations, bias, and toxicity in real time. The Vault stores and rotates API keys centrally so secrets stay out of your codebase. Custom dashboards let you build drag-and-drop monitoring views with charts, stat cards, and tables backed by SQL queries against ClickHouse. GPU utilization, memory, temperature, and power metrics feed into the same platform for end-to-end infrastructure visibility. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

Screen equities against dozens of technical indicators, backtest algorithmic trading strategies, and spot intraday market anomalies using Tick Stock Panel, a self-hosted quantitative analysis workbench. Traders can filter entire exchanges across technical indicators including moving averages, relative strength index, and Bollinger bands with near-instantaneous execution. The platform provides a full backtesting environment where users test customized entry and exit rules, account for simulated slippage and commission fees, and review streamed performance metrics. Dedicated monitoring modules track intraday volume spikes, price breakouts, and multi-day limit streaks with automated alert dispatching to messaging channels and voice announcements. Users can explore individual stock profiles featuring multi-dimensional technical breakdowns, financial balance sheets, and automated event post-mortems generated via integrated language models. Visual factor mining tools allow analysts to discover uncorrelated signals and assemble composite portfolio screens without external database overhead. An interactive dashboard aggregates sector rotations, hot concept heatmaps, and customizable watchlists into modular operational views. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

When a ClickHouse cluster slows down, the answers are buried in system tables nobody wants to query by hand; CHMonitor turns those tables into an operations dashboard that explains what the cluster is doing and what to fix next. Live views cover running queries with elapsed time, memory, and a kill action, while history, failed, slowest, and most-expensive pages rank finished work by CPU, duration, and rows read, each with the full statement and one-click EXPLAIN. Thread-level analysis shows how a single query parallelized, and per-user summaries reveal which analyst or service is eating the cluster. Beyond queries, you get a merge and part-lifecycle timeline, replication queue and Keeper health, a live topology map of shards and replicas, storage breakdowns by database, table, and part, and a query activity heatmap spanning a year. An AI advisor reviews the workload and recommends skip indexes, projections, PREWHERE clauses, and TTL changes ranked by impact, with an MCP interface and built-in agent for asking questions in plain English; it recommends only and never applies DDL. Threshold-based health checks alert to Slack, Discord, PagerDuty, or Opsgenie, multiple ClickHouse hosts are watched from one dashboard, and a companion terminal UI covers quick checks from the shell. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. GPL-3.0 licensed.

With over 11,000 GitHub stars and now backed by Datadog while remaining fully Apache 2.0 licensed, Quickwit delivers the search performance Elasticsearch users expect at a fraction of the infrastructure cost by moving the index to object storage instead of expensive local SSDs. The Rust-based engine, built on the Tantivy search library with SIMD-accelerated vectorized processing and zero garbage collection overhead, achieves sub-second search latency directly against Amazon S3, Azure Blob Storage, Google Cloud Storage, or any S3-compatible backend like MinIO and Ceph. The Elasticsearch-compatible REST API covers ingest, search, query DSL, and aggregations, enabling existing log shippers including Vector, Fluent Bit, and Syslog to migrate without rewriting configurations. Native OpenTelemetry Protocol endpoints accept logs and traces via gRPC, while Jaeger integration provides a drop-in distributed tracing backend. Ingestion from Apache Kafka, Amazon Kinesis, and Apache Pulsar supports streaming pipelines with multi-index partitioning, and the schemaless JSON indexing mode eliminates the need for upfront schema definitions. Stateless searchers and indexers scale horizontally on Kubernetes or bare metal, with a control plane that distributes indexing tasks and a janitor that manages retention policies and GDPR-compliant deletions. The built-in web UI displays search results and cluster state, while the official Grafana data source enables log exploration dashboards. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

Sensors on factory floors, wind turbines, and connected vehicles produce readings every second, and Apache IoTDB is the time-series database built to store those readings and answer questions over them. Devices write through client sessions that tolerate out-of-order, delayed, and multi-frequency arrivals, so a gateway that reconnects after an outage can backfill history without corrupting the timeline. Series live in a device hierarchy you can browse like a directory tree, or in tables queried with standard SQL, common table expressions, set operations, and more than one hundred built-in aggregation and time-series functions. The TsFile columnar format shares one time column across aligned series and applies encodings designed for sensor data, which keeps high-frequency archives compact while Spark and Hadoop jobs read the same files directly for larger analysis. Pipe synchronization streams records or transfers whole TsFiles between edge sites and central clusters with path and device filtering, so plants keep collecting locally while headquarters queries a continuous copy. AINode brings forecasting, anomaly detection, and missing-value imputation into the database itself: register a model with SQL, then ask for predictions in the same query language instead of shipping measurements to a separate machine learning service. A low-memory Edge edition runs collection on constrained gateways, and clusters scale by adding DataNodes under ConfigNode coordination with automatic load balancing. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Transactions, analytics, full-text search, and vector retrieval stop needing four separate systems: MatrixOne folds them into one hyper-converged database engine that speaks the MySQL 8.0 wire protocol, so existing drivers, JDBC applications, Navicat, and BI tools connect unchanged. A single TAE storage engine serves OLTP and OLAP together, with compute and storage separated so query nodes scale independently of the data they scan. Vector columns carry IVF and HNSW indexes with L2, cosine, and inner-product distance, powering RAG and semantic search at billions scale beside your relational tables instead of in a sidecar database. Full-text indexing joins the same query, so hybrid searches combine keyword relevance and embedding similarity in one statement. Git-style version control for data lets teams clone, branch, and roll back datasets the way they already treat code, which makes experiments and audits reproducible. Streaming ingestion through Kafka connectors keeps analytical tables current, and the 4.2 line sharpens window-query performance, composite-index planning, and physical backup and restore that preserves table history. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

With over 9,100 GitHub stars and production deployments powering real-time analytics at companies like SHOPLINE where it reduced API latency by 76.7%, RisingWave is the PostgreSQL-compatible streaming database that collapses the traditional Debezium-plus-Kafka-plus-Flink-plus-serving-database stack into a single Rust-powered system. The platform continuously ingests data from PostgreSQL and MySQL via native CDC connectors that eliminate Debezium middleware, consumes Kafka, Redpanda, Pulsar, and Kinesis topics, accepts webhook events from SaaS applications, and batch-loads historical data from S3 and data warehouses. Standard SQL defines sources, materialized views, and sinks — no new DSL, no Java, and no custom API — while the PostgreSQL wire protocol means psql, DBeaver, pgAdmin, Grafana, Metabase, Superset, Tableau, and every PostgreSQL client library works without modification. Materialized views are incrementally maintained as events arrive, delivering point lookups in single-digit milliseconds without recomputing aggregates from scratch. For long-term retention, RisingWave writes to Apache Iceberg tables with a hosted REST catalog and automated table maintenance including compaction, small-file optimization, and snapshot cleanup, with data queryable by Spark, Trino, DuckDB, and DataFusion. The disaggregated compute-storage architecture uses S3-based state management for elastic scaling, instant failure recovery measured in seconds rather than the minutes-to-hours typical of RocksDB-based systems, and cost-efficient storage tiering. An MCP server enables AI agents to query and operate RisingWave directly. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

GoAccess processes millions of web log entries in seconds and renders the results as interactive dashboards that update every 200 milliseconds in the terminal or every second via WebSocket-connected HTML reports. Written entirely in C with only ncurses as a dependency, it achieves exceptional performance even on resource-constrained servers, reducing memory usage by approximately 20% and parsing time by 35% in recent releases through optimized in-memory hash tables with on-disk persistence support. The analyzer parses virtually every web log format out of the box (Apache Combined and Common, Nginx, Amazon CloudFront, Amazon S3, AWS Elastic Load Balancing, Google Cloud Storage, Squid, W3C IIS, Caddy JSON, and Traefik) while supporting fully custom log format strings for non-standard configurations. The self-contained HTML dashboard displays 15+ interactive panels covering unique visitors, requested files, static files, 404 errors, referring sites, search keyphrases, geographic location with city-level GeoIP resolution, operating systems, browsers, HTTP status codes, time distribution, and virtual host metrics. WebSocket authentication via JWT provides secure remote access, and incremental log processing ensures data continuity across daemon restarts. Docker deployment requires a single command with volume-mapped access logs. Over 20,800 stars with active development since 2010. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

HitKeep is a sovereign web analytics platform that consolidates cookieless visitor telemetry, conversion funnels, e-commerce revenue reports, and AI engine visibility into a privacy-respecting dashboard that replaces Google Analytics and complex tracking clouds. Marketing teams can monitor inbound visitor journeys without installing intrusive consent banners or violating regional GDPR regulations. Custom conversion funnels and goal milestones track user registrations, checkout transactions, newsletter subscriptions, and software downloads with exact revenue attribution. Integrated Google Search Console synchronization brings organic search queries, keyword impression rankings, and click-through metrics directly alongside real-time pageview logs. A dedicated artificial intelligence visibility dashboard monitors crawling bots, retrieval citations, and direct referral visits originating from ChatGPT, Claude, Perplexity, and other generative search agents. Growth analysts can pose natural language questions to an embedded AI reporting assistant, generating instant cohort breakdowns and engagement trends without assembling complex manual filters. Site operators can configure custom tracking domains, whitelist reverse proxy networks, filter out referrer spam, and schedule automated database backups across persistent storage volumes. Complete data sovereignty controls allow teams to export raw event telemetry into open Parquet and CSV archives or stream metrics into external business intelligence warehouses via webhooks. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

DataHub maps your entire data ecosystem into a searchable, governed catalog where every table, pipeline, dashboard, and metric is discoverable and traceable from source to consumer. Originally built at LinkedIn to manage metadata at hyperscale and proven to handle 10 million+ assets and billions of relationships in production, the platform is now trusted by 3,000+ organizations including Netflix, Visa, Slack, and Pinterest. The Spring Java backend (GMS) exposes both GraphQL and OpenAPI REST endpoints, while the React frontend delivers an intuitive interface for searching, browsing, and governing data assets. The Python-based ingestion framework provides 80+ production-grade connectors extracting deep metadata from Snowflake, BigQuery, Redshift, Databricks, dbt, Airflow, Spark, Kafka, Looker, Tableau, Power BI, Superset, PostgreSQL, MySQL, Hive, Glue, S3, Iceberg, and Unity Catalog through pull-based scheduled crawls and push-based emission via Python and Java SDKs. Automatic table-level and column-level lineage detection uses SQL parsing with 97-99% accuracy, tracing data flows from ingestion pipelines through warehouses to BI dashboards. Real-time metadata streaming via Kafka keeps the catalog continuously synchronized as schemas evolve and pipelines execute. The governance layer provides business glossary management, tag propagation along lineage graphs, domain-based organization, and fine-grained access control policies. DataHub Actions triggers automated responses to metadata changes, enabling notifications, quality checks, and downstream workflows. Elasticsearch powers full-text search with faceted filtering across entities. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

Built for self-hosting from the ground up, Kutt is a modern, MIT-licensed URL shortener: zero-configuration setup, no build step, and SQLite, PostgreSQL, or MySQL with optional Redis caching. Links carry real management features - custom slugs from a configurable alphabet (confusable characters like 0/O and l/1 omitted by default), password protection, descriptions, expiration times, and the ability to edit a destination URL without changing the short link already in circulation. Custom domains make branded short links first-class: add as many as you like and serve each under your own name instead of a third-party's. Private, per-link statistics track clicks, browsers, operating systems, and countries without logging visitor IPs or sensitive data. An admin page manages users and links instance-wide, and two environment flags (DISALLOW_REGISTRATION, DISALLOW_ANONYMOUS_LINKS) lock the instance down for private use; OpenID Connect login integrates with an existing identity provider. Automation runs through a documented REST API - create, list, delete, and pull stats - plus Chrome and Firefox extensions and ShareX compatibility for shortening from anywhere. Built with Node.js and React, deployed in one Docker container, it replaces Bitly with something you own: your domain, your analytics, and links that never die with a vendor.

Before any AI model reads a single customer message, Obsei has already stripped out the names, email addresses, card numbers, and ID numbers hiding inside it. Obsei pulls in what customers are saying from App Store and Google Play reviews, Zendesk, Freshdesk, and Intercom tickets, GitHub issues, surveys, YouTube comments, Bluesky, RSS feeds, SQL databases, and even Gong call transcripts. Every record is pseudonymised on the way in and stored in an encrypted database that never phones home. Then the labelling starts: sentiment, intent, language, and your own custom questions, each answered with a confidence score by a zero-shot classifier running locally on your machine, or by an LLM you choose through your own keys. Answers the model is unsure about are escalated to a stronger model or queued for a human instead of being silently wrong. Related complaints roll up into stable themes with week-by-week trends, and anything mentioned by fewer than five people stays hidden, so individuals cannot be picked out of a chart. When a theme spikes, alerts and digests land in Slack, and individual findings can be routed straight into Jira, Linear, or GitHub issues. Your analysts browse it all in the built-in Studio, and your AI agents can ask questions over MCP and get cited, redacted evidence back, with any action they propose waiting for your approval. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Website analytics, uptime monitoring, and server status - three tools most teams run separately - combined in Tianji, an open-source observability platform. The analytics layer tracks page views, unique visitors, referrers, and UTM parameters with a lightweight cookie-less script, which keeps collection GDPR and CCPA friendly. The uptime monitor checks availability and latency on configurable intervals, accepts passively reported results, and publishes public status pages for incident communication. Server status agents report CPU, memory, disk, and network metrics with threshold-based alerts, and notifications route through webhooks, Slack, Telegram, and other channels with noise control. It also includes anonymous telemetry for tracking deployments of your own open-source projects, surveys, waitlists, team collaboration, and an OpenAPI interface for integrations and exports. The consolidation is the point: traffic analytics, uptime checks, and server metrics share one interface and one alerting layer, so diagnosing an incident does not mean hopping between Google Analytics, Uptime Kuma, and Prometheus - and the built-in public status pages replace a separate paid Statuspage-style subscription. Because collection uses no cookies with IP truncation and aggregation by default, basic traffic measurement requires no consent banner. Built in TypeScript under the Apache 2.0 license and inspired by Umami and Uptime Kuma, it is deliberately right-sized for independent developers and small SaaS teams whose monitoring needs are real but lightweight.