Alternatives to pgvector

ScaleGrid is a fully managed Database-as-a-Service (DBaaS) platform that helps you automate your time-consuming database administration tasks both in the cloud and on-premises. ScaleGrid makes it easy to provision, monitor, backup, and scale open-source databases. It offers advanced security, high availability, query analysis, and troubleshooting support to improve your deployments' performance. The following databases are supported: - MySQL - PostgreSQL - Redis™. - MongoDB®, database - Greenplum™ (coming soon) ScaleGrid supports both public and privately-owned clouds such as AWS, Azure and Google Cloud Platform (GCP), DigitalOcean and Linode, Oracle Cloud Infrastructure, (OCI), VMware, and OpenStack. ScaleGrid is used by thousands of developers, startups, as well as enterprise customers such as Accenture, Meteor and Atlassian. It handles all your database operations at any scale, so you can concentrate on your application performance.

It shouldn't take long to analyze data from your business solutions. Zuar Runner allows you to automate your ELT/ETL processes, and have data flow from hundreds of sources into one destination. Zuar Runner can manage everything: transport, warehouse, transformation, model, reporting, and monitoring. Our experts will make sure your deployment goes smoothly and quickly.

Artificial intelligence long-term memory The Pinecone vector database makes building high-performance vector search apps easy. Fully managed and developer-friendly, the database is easily scalable without any infrastructure problems. Once you have vector embeddings created, you can search and manage them in Pinecone to power semantic searches, recommenders, or other applications that rely upon relevant information retrieval. Even with billions of items, ultra-low query latency Provide a great user experience. You can add, edit, and delete data via live index updates. Your data is available immediately. For more relevant and quicker results, combine vector search with metadata filters. Our API makes it easy to launch, use, scale, and scale your vector searching service without worrying about infrastructure. It will run smoothly and securely.

You can easily assess the effort required to scale your database infrastructure. SolarWinds®, Database Performance Monitor (DPM), is a SaaS-based platform which helps improve system performance, team efficiency and infrastructure cost savings. It provides full visibility into all major open-source databases such as MySQL, PostgreSQL and MongoDB, Amazon Aurora and Redis. You can quickly search for new queries by comparing the performance of your code with system performance using before and after charts. Visualize query behavior and system metrics to identify performance issues and improve impact.

Searching and analyzing structured data is easy; however, over 80% of generated data is unstructured, requiring a different approach. Machine learning converts unstructured data into high-dimensional vectors of numerical values, which makes it possible to find patterns or relationships within that data type. Unfortunately, traditional databases were never meant to store vectors or embeddings and can not meet unstructured data's scalability and performance requirements. Zilliz Cloud is a cloud-native vector database that stores, indexes, and searches for billions of embedding vectors to power enterprise-grade similarity search, recommender systems, anomaly detection, and more. Zilliz Cloud, built on the popular open-source vector database Milvus, allows for easy integration with vectorizers from OpenAI, Cohere, HuggingFace, and other popular models. Purpose-built to solve the challenge of managing billions of embeddings, Zilliz Cloud makes it easy to build applications for scale.

Qdrant is a vector database and similarity engine. It is an API service that allows you to search for the closest high-dimensional vectors. Qdrant allows embeddings and neural network encoders to be transformed into full-fledged apps for matching, searching, recommending, etc. This specification provides the OpenAPI version 3 specification to create a client library for almost any programming language. You can also use a ready-made client for Python, or other programming languages that has additional functionality. For Approximate Nearest Neighbor Search, you can make a custom modification to the HNSW algorithm. Search at a State of the Art speed and use search filters to maximize results. Additional payload can be associated with vectors. Allows you to store payload and filter results based upon payload values.

Chroma is an AI-native, open-source embedding system. Chroma provides all the tools needed to embeddings. Chroma is creating the database that learns. You can pick up an issue, create PRs, or join our Discord to let the community know your ideas.

One tool allows you to operationalize your embeddings. A comprehensive database that provides embedding functionality previously unavailable on multiple platforms is now available to you. Embeddinghub makes it easy to accelerate your machine learning. Embeddings are dense numerical representations of real world objects and relationships. They can be expressed as vectors. They are often created by first defining an unsupervised machine learning problem, also known as a "surrogate issue". Embeddings are intended to capture the semantics from the inputs they were derived. They can then be shared and reused for better learning across machine learning models. This is possible with Embeddinghub in an intuitive and streamlined way.

A vector database designed for scalable similarity searches. Open-source, highly scalable and lightning fast. Massive embedding vectors created by deep neural networks or other machine learning (ML), can be stored, indexed, and managed. Milvus vector database makes it easy to create large-scale similarity search services in under a minute. For a variety languages, there are simple and intuitive SDKs. Milvus is highly efficient on hardware and offers advanced indexing algorithms that provide a 10x speed boost in retrieval speed. Milvus vector database is used in a variety a use cases by more than a thousand enterprises. Milvus is extremely resilient and reliable due to its isolation of individual components. Milvus' distributed and high-throughput nature makes it an ideal choice for large-scale vector data. Milvus vector database uses a systemic approach for cloud-nativity that separates compute and storage.

Vald is a distributed, fast, dense and highly scalable vector search engine that approximates nearest neighbors. Vald was designed and implemented using the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT for searching neighbors. Vald supports automatic vector indexing, index backup, horizontal scaling, which allows you to search from billions upon billions of feature vector data. Vald is simple to use, rich in features, and highly customizable. Usually, the graph must be locked during indexing. This can cause stop-the world. Vald uses distributed index graphs so that it continues to work while indexing. Vald has its own highly customizable Ingress/Egress filter. This can be configured to work with the gRPC interface. Horizontal scaling is available on memory and cpu according to your needs. Vald supports disaster recovery by enabling auto backup using Persistent Volume or Object Storage.

Astra DB from DataStax is a real-time vector database as a service for developers that need to get accurate Generative AI applications into production, fast. Astra DB gives you a set of elegant APIs supporting multiple languages and standards, powerful data pipelines and complete ecosystem integrations. Astra DB enables you to quickly build Gen AI applications on your real-time data for more accurate AI that you can deploy in production. Built on Apache Cassandra, Astra DB is the only vector database that can make vector updates immediately available to applications and scale to the largest real-time data and streaming workloads, securely on any cloud. Astra DB offers unprecedented serverless, pay as you go pricing and the flexibility of multi-cloud and open-source. You can store up to 80GB and/or perform 20 million operations per month. Securely connect to VPC peering and private links. Manage your encryption keys with your own key management. SAML SSO secure account accessibility. You can deploy on Amazon, Google Cloud, or Microsoft Azure while still compatible with open-source Apache Cassandra.

Weaviate is an open source vector database. It allows you to store vector embeddings and data objects from your favorite ML models, and scale seamlessly into billions upon billions of data objects. You can index billions upon billions of data objects, whether you use the vectorization module or your own vectors. Combining multiple search methods, such as vector search and keyword-based search, can create state-of-the art search experiences. To improve your search results, pipe them through LLM models such as GPT-3 to create next generation search experiences. Weaviate's next generation vector database can be used to power many innovative apps. You can perform a lightning-fast, pure vector similarity search on raw vectors and data objects. Combining keyword-based and vector search techniques will yield state-of the-art results. You can combine any generative model with your data to do Q&A, for example, over your dataset.

Vespa is forBig Data + AI, online. At any scale, with unbeatable performance. Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real-time. Users build recommendation applications on Vespa, typically combining fast vector search and filtering with evaluation of machine-learned models over the items. To build production-worthy online applications that combine data and AI, you need more than point solutions: You need a platform that integrates data and compute to achieve true scalability and availability - and which does this without limiting your freedom to innovate. Only Vespa does this. Together with Vespa's proven scaling and high availability, this empowers you to create production-ready search applications at any scale and with any combination of features.

Faiss is a library that allows for efficient similarity searches and clustering dense vectors. It has algorithms that can search for vectors of any size. It also includes supporting code for parameter tuning and evaluation. Faiss is written entirely in C++ and includes wrappers for Python. The GPU is home to some of the most powerful algorithms. It was developed by Facebook AI Research.

Metal is a fully-managed, production-ready ML retrieval platform. Metal embeddings can help you find meaning in unstructured data. Metal is a managed services that allows you build AI products without having to worry about managing infrastructure. Integrations with OpenAI and CLIP. Easy processing & chunking of your documents. Profit from our system in production. MetalRetriever is easily pluggable. Simple /search endpoint to run ANN queries. Get started for free. Metal API Keys are required to use our API and SDKs. Authenticate by populating headers with your API Key. Learn how to integrate Metal into your application using our Typescript SDK. You can use this library in JavaScript as well, even though we love TypeScript. Fine-tune spp programmatically. Indexed vector data of your embeddings. Resources that are specific to your ML use case.

Marqo is a complete vector search engine. It's more than just a database. A single API handles vector generation, storage and retrieval. No need to embed your own embeddings. Marqo can accelerate your development cycle. In just a few lines, you can index documents and start searching. Create multimodal indexes, and search images and text combinations with ease. You can choose from a variety of open-source models or create your own. Create complex and interesting queries with ease. Marqo allows you to compose queries that include multiple weighted components. Marqo includes input pre-processing and machine learning inference as well as storage. Marqo can be run as a Docker on your laptop, or scaled up to dozens GPU inference nodes. Marqo is scalable to provide low latency searches on multi-terabyte indices. Marqo allows you to configure deep-learning models such as CLIP for semantic meaning extraction from images.

The enterprise database for time series, documents, and vectors. Store any type data and combine the simplicity and scalability NoSQL with SQL. CrateDB is a distributed database that runs queries in milliseconds regardless of the complexity, volume, and velocity.

Create and manage AI applications without the need to move data to complex vector databases and pipelines. Integrate AI, vector search and real-time inference directly with your database. Python is all you need. All your AI models can be deployed in a single, scalable deployment. The AI models and APIs are automatically updated as new data is processed. You don't need to duplicate your data or create an additional database to use vector searching and build on it. SuperDuperDB allows vector search within your existing database. Integrate and combine models such as those from Sklearn PyTorch HuggingFace, with AI APIs like OpenAI, to build even the most complicated AI applications and workflows. With simple Python commands, deploy all your AI models in one environment to automatically compute outputs in your datastore (inference).

MyScale is a cutting-edge AI database that combines vector search with SQL analytics, offering a seamless, fully managed, and high-performance solution. Key features of MyScale include: - Enhanced data capacity and performance: Each standard MyScale pod supports 5 million 768-dimensional data points with exceptional accuracy, delivering over 150 QPS. - Swift data ingestion: Ingest up to 5 million data points in under 30 minutes, minimizing wait times and enabling faster serving of your vector data. - Flexible index support: MyScale allows you to create multiple tables, each with its own unique vector indexes, empowering you to efficiently manage heterogeneous vector data within a single MyScale cluster. - Seamless data import and backup: Effortlessly import and export data from and to S3 or other compatible storage systems, ensuring smooth data management and backup processes. With MyScale, you can harness the power of advanced AI database capabilities for efficient and effective data analysis.

Use user feedback and semantic relevance to reliably retrieve optimal document chunks for your retrieval-augmented generation system. In your search system, combine semantic relevance with document freshness because recent results are more accurate. Create a personalized ecommerce feed in real-time using user vectors based on the SKU embeddings that were viewed by the user. A vector index in your warehouse can be used to discover behavioral clusters among your customers. Use spaces to build your indices, and run queries all within a Python Notebook.

KDB.AI, a powerful knowledge based vector database, is a powerful search engine and knowledge-based vector data base that allows developers to create scalable, reliable, and real-time AI applications. It provides advanced search, recommendation, and personalization. Vector databases are the next generation of data management, designed for applications such as generative AI, IoT or time series. Here's what makes them unique, how they work and the new applications they're designed to serve.

We've been working on Generative AI for 5 years. Deep Lake combines the power and flexibility of vector databases and data lakes to create enterprise-grade LLM-based solutions and refine them over time. Vector search does NOT resolve retrieval. You need a serverless search for multi-modal data including embeddings and metadata to solve this problem. You can filter, search, and more using the cloud, or your laptop. Visualize your data and embeddings to better understand them. Track and compare versions to improve your data and your model. OpenAI APIs are not the foundation of competitive businesses. Your data can be used to fine-tune LLMs. As models are being trained, data can be efficiently streamed from remote storage to GPUs. Deep Lake datasets can be visualized in your browser or Jupyter Notebook. Instantly retrieve different versions and materialize new datasets on the fly via queries. Stream them to PyTorch, TensorFlow, or Jupyter Notebook.

In less than 2 minutes, you can create a backend. Get a Postgres database, authentication and instant APIs to start your project. Real-time subscriptions are also available. You can build faster and concentrate on your products. Every project is a Postgres database, the most trusted relational database in the world. You can add user sign-ups or logins to secure your data with Row Level Security. Large files can be stored, organized and served. Any media, including images and videos. Without the need to deploy or scale servers, you can write custom code and cron jobs. There are many starter projects and example apps to help you get started. We will instantly inspect your database and provide APIs. Stop creating repetitive CRUD endpoints. Instead, focus on your product. Type definitions directly from your database schema. Supabase can be used in the browser without a build. You can develop locally and push to production as soon as you are ready. You can manage Supabase projects on your local machine.

Atlas integrates with your workflow by organizing text, embedding datasets and creating interactive maps that can be explored in a web browser. To understand your data, you don't need to scroll through Excel files or log Dataframes. Atlas automatically analyzes, organizes, and summarizes your documents, surfacing patterns and trends. Atlas' pre-organized data interface makes it easy to quickly identify and remove any data that could be harmful to your AI projects. You can label and tag your data, while cleaning it up with instant sync to your Jupyter notebook. Although vector databases are powerful, they can be difficult to interpret. Atlas stores, visualizes, and allows you to search through all your vectors within the same API.

PostgreSQL, a powerful open-source object-relational database system, has over 30 years of experience in active development. It has earned a strong reputation for reliability and feature robustness.

Semantee, a managed database that is easy to configure and optimized for semantic searches, is hassle-free. It is available as a set REST APIs that can be easily integrated into any application in minutes. It offers multilingual semantic searching for applications of any size, both on-premise and in the cloud. The product is significantly cheaper and more transparent than most providers, and is optimized for large-scale applications. Semantee also offers an abstraction layer over an e-shop's product catalog, enabling the store to utilize semantic search instantly without having to re-configure its database.

Percona Distribution for PostgreSQL provides an enterprise-grade, open-source installation of PostgreSQL Core Distribution and critical additional enterprise components. PostgreSQL Core Distribution supports a wide variety of data types and user-defined object. It is ACID-compliant. PostgreSQL Core distribution is a stable, secure, open-source product that many organizations rely on. PostgreSQL Core Distribution is a reliable open-source product that can be relied upon by multiple organizations. Percona Distribution is installed for PostgreSQL. This means you have everything you need to get PostgreSQL up and running, including pg_repack and pgaudit and pgBackRest. Percona Distribution for PostgreSQL provides all of this in one installation. All components have been tested together and are updated as needed.

ParadeDB adds column-oriented storage to Postgres tables and vectorized query processing. Users can choose between column- and row-oriented storage when creating tables. Column-oriented tables can be stored as Parquet and managed by Delta Lake. Search by keyword, with BM25 scoring and configurable tokenizers. Multi-language support. Search by semantic meaning, with support for dense and sparse vectors. Combining the strengths of both full text and similarity searches, you can get better results. ParadeDB is ACID compliant and has concurrency control across all transactions. ParadeDB integrates seamlessly with the Postgres ecosystem including clients, extensions and libraries.

Hydra is a column-oriented Postgres that is open source. No code changes required to query billions of rows. Hydra parallelizes, vectorizes, and aggregates (COUNTS, SUMs, AVGs) to deliver the speed that you've always desired on Postgres. Boost performance in every size! Hydra can be installed in just 5 minutes, without requiring any changes to your tools, extensions, syntax, data model or data model. Hydra Cloud allows for smooth sailing and fully managed operations. Different industries have different requirements. Take control of your analytics with powerful Postgres custom functions and extensions. Built by you for you. Hydra is the fastest Postgres on the market. Boost performance by using columnar storage, query parallelization, and vectorization.

If you are one of the many organizations migrating a commercial database to open source, such as MySQL and PostgreSQL or if your company is building new applications using open-source database management system (OSDBMS), you will know that there is no commercial tooling available for these databases. Imagine if you could save money and be able to quickly ramp up your business using a familiar tool set. Toad Edge® makes it possible. It's a lightweight and reliable desktop tool that simplifies the development and management of open-source databases. Toad for MySQL or PostgreSQL management makes learning new databases easy. Toad Edge is a great tool for MariaDB, Amazon Redshift, and EDB Postgres Advanced Server. Toad Edge supports coding and editing, schema compare, sync, and DevOps CI processes so that you can manage open-source databases quickly and confidently.

Fujitsu Enterprise Postgres, a highly reliable and robust relational database designed for organizations that need high query performance and high availability, is a great choice. It is built on the well-respected open-source PostgreSQL system, and includes enterprise-grade features that enhance security and performance. Fujitsu Enterprise Postgres is managed and installed by Fujitsu's database specialists. They can also help with data migration from your existing database. FEP, which is based on PostgreSQL has high compatibility with other systems and applications. The graphic user interface is simple and clean, which makes it easier for DBAs to execute core database functions such as scanning, queries, and back-up. This makes your data and reports more accessible.

TimescaleDB is the most popular open-source relational database that supports time-series data. Fully managed or self-hosted. You can rely on the same PostgreSQL that you love. It has full SQL, rock-solid reliability and a huge ecosystem. Write millions of data points per node. Horizontally scale up to petabytes. Don't worry too much about cardinality. Reduce complexity, ask more questions and build more powerful applications. You will save money with 94-97% compression rates using best-in-class algorithms, and other performance improvements. Modern cloud-native relational database platform that stores time-series data. It is based on PostgreSQL and TimescaleDB. This is the fastest, easiest, and most reliable way to store all of your time-series information. All observability data can be considered time-series data. Time-series problems are those that require efficient solutions to infrastructure and application problems.

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

HeidiSQL is a free program that aims to make it easy to use. "Heidi" allows you to view and edit data and structures on computers that run one of the following database systems: MariaDB, MySQL or Microsoft SQL. Ansgar invented HeidiSQL in 2002. It is one of the most popular tools for MariaDB or MySQL. OpenSource, free for everyone Multiple servers can be connected in one window. MariaDB, MySQL and MS SQL are supported. You can connect to servers via commandline. You can connect via SSH tunnel or SSL settings. Edit tables, views, stored procedures, triggers, and scheduled events. You can create beautiful SQL-exports and then compress them or copy them to the clipboard. Export directly from one server/database to another server/database. You can manage user privileges, import text-files, and export table rows in CSV, HTML HTML, XML, SQL, LaTeX Wiki Markup, and PHP Array. A grid allows you to browse and edit table-data.

AutoML, an open-source, state-of the-art AutoML technology, is used to train high-quality models quickly and easily. Deep Learning and pre-trained models are used to provide additional intelligence when applicable. Uses Causal AI and AutoML to ensure fairness, causal inference, and counterfactual predictions. All models can be instantly deployed online to be used with an API or interactively online. Complete feature descriptions and model explanations with Shapley value. Our AI engine is completely open-source. Our algorithms can be fully audited, and can be used anywhere. It groups customers and products into similar groups with a rich set features. Forecasts the future by using historical data to capture temporal patterns. Predicts unlabelled data using predictive models built with labelled data

Manticore Search, an open-source database, was created in 2017 to continue the Sphinx Search engine. We took the best of it, improved its functionality, fixed hundreds bugs, rewrote almost all of the code, and kept it open source! Manticore Search is a modern, lightweight, full-featured database that offers outstanding full-text searching capabilities. It is our belief that technology vendors should make it as simple as possible. Manticore products are not meant to require developers or DevOps to be experts in search engines and databases. We understand that you have other priorities than trying to figure out how this or that setting affects the functionality. Manticore Search should be able to work in most cases, even with default settings.

Directus is an Open Data Platform that allows you to manage any SQL database's content. It offers a powerful API layer to developers and an intuitive app for non-technical users. Directus is written entirely in JavaScript (primarily Node.js or Vue.js), and it is modular and extensible. This allows it to be tailored to your specific project needs. Our platform can be used to manage digital experiences as a headless CMS, a database client for democratizing information, or as a standalone web app for back-office CRM, inventory, business intelligence and project management.

VividCortex offers deep database performance monitoring to improve system performance, team efficiency, as well as infrastructure savings. The GDPR- and SOC 2 compliant SaaS platform provides complete visibility into all major open-source databases - MySQL and PostgreSQL. It also allows for full team collaboration without overhead. VividCortex is trusted by industry leaders like Etsy and SendGrid. It provides all-query monitoring and easy drill-down views across thousands servers simultaneously. VividCortex customers save 90% on diagnosing database problems, 75% faster queries and 10x more productivity. VividCortex offers 24/7 customer support and makes it easy for engineers to deploy code with confidence, troubleshoot, diagnose outages and monitor your database health.

A service to manage OpenSearch clusters within Yandex Cloud Infrastructure. Use this popular open-source solution to integrate fast and scalable search into your product. In just a few moments, you can deploy a cluster that is ready to use with product settings optimized for the cluster size. We will take care of all cluster maintenance, including reserves, monitoring and fault tolerance. Use our visualization tools for analytical dashboards, application monitors, and alert systems. Connect third-party authentication services (SAML). The service allows for granular configurations of data access levels. Open source code allows for us to develop with the community and be the first to release timely updates. OpenSearch is a system of open-source search and analytic tools that can be easily scaled. It provides a set technologies for fast search and analytics.

RapidLoop offers a variety of monitoring solutions. pgDash, a comprehensive monitoring tool for PostgreSQL installations, is available. Using the open-source tool, pgmetrics, pgDash displays information and metrics about all aspects of your PostgreSQL server. pgDash can be used in both SaaS or self-hosted / on premise versions. OpsDash is a complete server, service, database and monitoring solution. OpsDash is available as a SaaS or on-premise service. It provides a cost-effective, reliable and easy-to-use monitoring solution that works with a variety of servers. pgDash - pgDash provides a comprehensive solution to monitor PostgreSQL. It provides detailed PostgreSQL metrics, alerting, and baselines. Learn more at pgdash.io. OpsDash can be used to monitor servers, services, or databases.

OctoData can be deployed in Cloud hosting at a lower price and includes personalized support, from the initial definition of your needs to the actual use of the solution. OctoData is built on open-source technologies that are innovative and can adapt to new possibilities. Its Supervisor provides a management interface that allows users to quickly capture, store, and exploit increasing amounts and varieties of data. OctoData allows you to quickly prototype and industrialize massive data recovery solutions, even in real-time, in a single environment. You can get precise reports, explore new options, increase productivity, and increase profitability by leveraging your data.

RisingWave is an open-source distributed SQL streaming database released under Apache 2.0 license. RisingWave is PostgreSQL-compatible, and allows users to process streaming data using standard SQL. Written in Rust and designed with cloud-native architecture, RisingWave can achieve 10X better performance and cost efficiency compared to conventional stream processing systems. RisingWave Cloud is a fully managed cloud service. Users can leverage RisingWave Cloud to process streaming data and serve analytical queries at ease.

ClusterControl is a multi-cloud hybrid database ops orchestration platform that supports MongoDB and Elasticsearch as well as Redis, TimescaleDB and SQL Server on Linux. It also supports Galera Cluster, PostgreSQL and MySQL in cloud and on-premises environments. It can handle all aspects of the lifecycle, including deployment, failover, backup, and more. It allows organizations to implement the Sovereign DBaaS model with its full suite of databases and ops features. ClusterControl is the ideal solution for organizations who need to run large-scale, open source database operations reliably but don't want to be restricted by traditional DBaaS providers. It allows you to choose your environment, stability of licenses, and gives you DB access.

Apache Doris is an advanced data warehouse for real time analytics. It delivers lightning fast analytics on real-time, large-scale data. Ingestion of micro-batch data and streaming data within a second. Storage engine with upserts, appends and pre-aggregations in real-time. Optimize for high-concurrency, high-throughput queries using columnar storage engine, cost-based query optimizer, and vectorized execution engine. Federated querying for data lakes like Hive, Iceberg, and Hudi and databases like MySQL and PostgreSQL. Compound data types, such as Arrays, Maps and JSON. Variant data types to support auto datatype inference for JSON data. NGram bloomfilter for text search. Distributed design for linear scaling. Workload isolation, tiered storage and efficient resource management. Supports shared-nothing as well as the separation of storage from compute.

Open-source projects are inspiration for and used to build RAWGraphs. RAWGraphs welcomes contributions from the community. There are almost 30 visual models that can be used to visualize data, hierarchies, and time series. Although RAWGraphs is a web application, your web browser will process the data you input. You can save your project or export it as a vector or raster file. You can edit it in your favorite software. RAWGraphs was originally designed as a tool to aid designers and vis-geeks. RAWGraphs aims to be a bridge between spreadsheet applications (e.g. Microsoft Excel, Apple Numbers and OpenRefine) as well as vector graphics editors (e.g. Adobe Illustrator, Inkscape and Figma. RAWGraphs is an open-source data visualization framework that makes it easy to visualize complex data. DensityDesign, Calibro, and Inmagik are the members of the team that is responsible for the design, development, and maintenance of the project. They joined the team in 2019

Synth is a data-as code tool that offers a simple CLI workflow to generate consistent data in an scalable manner. Synth can be used to generate data that is correct and anonymized, but still looks and sounds like production. Create test data fixtures to support your continuous integration, testing and development. Create data that tells you the story you wish to tell. Specify constraints and relations. Seed development, environments and CI. Anonymize sensitive production data. Create realistic data according to your specifications. Synth's declarative configuration language allows you to specify the entire data model in code. Synth can import existing data and create accurate data models. Synth is database-agnostic and supports semistructured data. It works well with SQL and NoSQL. Synth can generate thousands of semantic types, such as email addresses, credit card numbers and more.

FoundationDB supports multiple models, so you can store different types of data in one database. All data can be safely stored, distributed and replicated in Key-Value Store. FoundationDB is easy-to-use, grow, and maintain. It uses a distributed architecture that scales out gracefully and handles faults, while acting as a single ACID database. FoundationDB is extremely fast on commodity hardware and can support very heavy loads at a low cost. FoundationDB has been in production for many years and has learned from its mistakes. FoundationDB is supported by an unmatched testing system that is based on a deterministic simulator engine.

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.

NocoDB is a free platform that converts any database into an intelligent spreadsheet. Create unlimited grid views, gallery views, and form views from your own data. Search, sort and filter columns and rows easily. Share views with others and password protected. Turn software users into software producers in each organization.

Open Content Platform (OCP), an open-source IT discovery platform and integration platform, is a free-of-cost IT discovery and integration platform. OCP is responsible for discovery, integration, modeling, and implementing solutions in the datacenter. The micro-service core framework allows for process-level horizontal scaling. It is fully extensible and designed for developers. It also leverages components from the open-source community to greatly reduce product complexity. The dynamic discovery is fundamentally different than other ITSM vendors. Furthermore, the data-driven modeling keeps Applications and Services current for downstream consumption (e.g. Service-aware monitoring