Alternatives to KDB.AI

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.

The AI Knowledge Platform. The Pinecone Database, Inference, and Assistant make 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.

Vectorize is an open-source platform that transforms unstructured data to optimized vector search indices. This allows for retrieval-augmented generation pipelines. It allows users to import documents, or connect to external systems of knowledge management to extract natural languages suitable for LLMs. The platform evaluates chunking and embedding methods in parallel. It provides recommendations or allows users to choose the method they prefer. Vectorize automatically updates a real-time pipeline vector with any changes to data once a vector configuration has been selected. This ensures accurate search results. The platform provides connectors for various knowledge repositories and collaboration platforms as well as CRMs. This allows seamless integration of data in generative AI applications. Vectorize also supports the creation and update of vector indexes within preferred vector databases.

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.

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.

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.

Vector search can give you a competitive edge. Streamline your AI/ML workflows, reduce costs and stay ahead with up to a 10x faster time-to market. ApertureDB’s unified multimodal management of data will free your AI teams from data silos and allow them to innovate. Setup and scale complex multimodal infrastructure for billions objects across your enterprise in days instead of months. Unifying multimodal data with advanced vector search and innovative knowledge graph, combined with a powerful querying engine, allows you to build AI applications at enterprise scale faster. ApertureDB will increase the productivity of your AI/ML team and accelerate returns on AI investment by using all your data. You can try it for free, or schedule a demonstration to see it in action. Find relevant images using labels, geolocation and regions of interest. Prepare large-scale, multi-modal medical scanning for ML and Clinical studies.

Azure Managed Redis offers the latest Redis innovations and industry-leading availability. It also has a cost-effective Total Cost Of Ownership (TCO) that is designed for hyperscale clouds. Azure Managed Redis provides these capabilities on a trusted platform, empowering businesses with the ability to scale and optimize generative AI applications in a seamless manner. Azure Managed Redis uses the latest Redis innovations for high-performance and scalable AI applications. Its features, such as in-memory storage, vector similarity searches, and real-time computing, allow developers to handle large datasets, accelerate machine-learning, and build faster AI applications. Its interoperability to Azure OpenAI Service allows AI workloads that are ready for mission-critical applications to be faster, more scalable and more reliable.

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).

Start building in just minutes. Vectorize provides fast and cost-effective vector storage for your AI Retrieval augmented generation (RAG) & search applications. Vectorize integrates seamlessly with Cloudflare’s AI developer platform & AI gateway to centralize development, monitoring, and control of AI applications at a global level. Vectorize is a globally-distributed vector database that allows you to build AI-powered full-stack applications using Cloudflare Workers AI. Vectorize makes it easier and cheaper to query embeddings - representations of objects or values such as text, images, audio, etc. - that are intended to be consumed by machine intelligence models and semantic search algorithms. Search, similarity and recommendation, classification, anomaly detection, and classification based on your data. Search results are improved and faster. Support for string, number and boolean type.

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.

VectorDB is a lightweight Python program for storing and retrieving texts using chunking techniques, embedding techniques, and vector search. It offers an easy-to use interface for searching, managing, and saving textual data, along with metadata, and is designed to be used in situations where low latency and speed are essential. When working with large language model datasets, vector search and embeddings become essential. They allow for efficient and accurate retrieval relevant information. These techniques enable quick comparisons and search, even with millions of documents. This allows you to find the most relevant search results in a fraction the time of traditional text-based methods. The embeddings also capture the semantic meaning in the text. This helps improve the search results, and allows for more advanced natural-language processing tasks.

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.

Deliver high-quality answers with a database that is built for advanced retrieval, augmented generation (RAG), and modern search. Focus on exponential growth using a vector database built for enterprise that includes security, compliance and responsible AI practices. With sophisticated retrieval strategies that are backed by decades worth of research and validation from customers, you can build better applications. Rapidly deploy your generative AI application with seamless platform and integrations of data sources, AI models and frameworks. Upload data automatically from a variety of supported Azure and 3rd-party sources. Streamline vector data with integrated extraction, chunking and enrichment. Support for multivectors, hybrids, multilinguals, and metadata filters. You can go beyond vector-only searching with keyword match scoring and reranking. Also, you can use geospatial searches, autocomplete, and geospatial search.

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.

LanceDB is an open-source database for AI that is developer-friendly. LanceDB provides the best foundation for AI applications. From hyperscalable vector searches and advanced retrieval of RAG data to streaming training datasets and interactive explorations of large AI datasets. Installs in seconds, and integrates seamlessly with your existing data and AI tools. LanceDB is an embedded database with native object storage integration (think SQLite, DuckDB), which can be deployed anywhere. It scales down to zero when it's not being used. LanceDB is a powerful tool for rapid prototyping and hyper-scale production. It delivers lightning-fast performance in search, analytics, training, and multimodal AI data. Leading AI companies have indexed petabytes and billions of vectors, as well as text, images, videos, and other data, at a fraction the cost of traditional vector databases. More than just embedding. Filter, select and stream training data straight from object storage in order to keep GPU utilization at a high level.

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.

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.

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.

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.

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.

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.

We've already done the hard work of bundling, pre-configuring and integrating all the components that you need to build solutions and integrate LLMs into your business processes. ConfidentialMind allows you to jump into action. Deploy an endpoint for powerful open-source LLMs such as Llama-2 and turn it into an LLM API. Imagine ChatGPT on your own cloud. This is the most secure option available. Connects the rest with the APIs from the largest hosted LLM provider like Azure OpenAI or AWS Bedrock. ConfidentialMind deploys a Streamlit-based playground UI with a selection LLM-powered productivity tool for your company, such as writing assistants or document analysts. Includes a vector data base, which is critical for most LLM applications to efficiently navigate through large knowledge bases with thousands documents. You can control who has access to your team's solutions and what data they have.

Substrate is a platform for agentic AI. Elegant abstractions, high-performance components such as optimized models, vector databases, code interpreter and model router, as well as vector databases, code interpreter and model router. Substrate was designed to run multistep AI workloads. Substrate will run your task as fast as it can by connecting components. We analyze your workload in the form of a directed acyclic network and optimize it, for example merging nodes which can be run as a batch. Substrate's inference engine schedules your workflow graph automatically with optimized parallelism. This reduces the complexity of chaining several inference APIs. Substrate will parallelize your workload without any async programming. Just connect nodes to let Substrate do the work. Our infrastructure ensures that your entire workload runs on the same cluster and often on the same computer. You won't waste fractions of a sec per task on unnecessary data transport and cross-regional HTTP transport.

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.

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.

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.

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.

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.

Postgres: Open-source vector similarity search Supports exact and approximate closest neighbor search for L2 distances, inner product and cosine distances.

A modern Postgres dataplatform for operators, developers and data engineers. AI builders can also use it to power mission-critical workloads. Flexible deployment across hybrid cloud and multi-cloud. EDB Postgres is the first intelligent data-platform for transactional, analytic, and new AI workloads, powered by a Postgres engine enhanced. It can be deployed either as a cloud managed service, as self-managed software or as a physical device. It provides built-in observability and AI-driven assistance. It also includes migration tooling and a single pane-of-glass for managing hybrid data estates. EDB Postgres AI elevates data infrastructure into a strategic technology asset, bringing analytical and AI systems close to customers' core transactional and operational data. All managed through Postgres, the world's most popular database. Modernize legacy systems with the most comprehensive Oracle compatibility and a suite migration tools to get customers onboard.

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

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.

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.

Simplified IT/OT Integration, Data Engineering & Real-Time Edge Intelligence. Easy to use, cross platform, open architecture edge computing for industrial time series data. Foghub provides the Critical-Path for IT/OT convergence. It connects Operations (Sensors and Devices, and Systems) and Business (People, Processes and Applications). This allows automated data acquisition, transformations, advanced analytics, and ML. You can manage large volumes, velocity, and variety of industrial data with the out-of-the box support for all data types, most industrial network protocols, OT/lab system, and databases. Automate data collection about your production runs, batches and parts, as well as process parameters, asset condition, performance, utility costs, consumables, operators and their performance. Foghub is designed for scale and offers a wide range of capabilities to handle large volumes of data at high velocity.

A cloud database that can scale to handle large streaming data sets. Kinetica harnesses modern vectorized processors to perform orders of magnitude faster for real-time spatial or temporal workloads. In real-time, track and gain intelligence from billions upon billions of moving objects. Vectorization unlocks new levels in performance for analytics on spatial or time series data at large scale. You can query and ingest simultaneously to take action on real-time events. Kinetica's lockless architecture allows for distributed ingestion, which means data is always available to be accessed as soon as it arrives. Vectorized processing allows you to do more with fewer resources. More power means simpler data structures which can be stored more efficiently, which in turn allows you to spend less time engineering your data. Vectorized processing allows for incredibly fast analytics and detailed visualizations of moving objects at large scale.

Kudu clusters store tables that look exactly like the tables in relational (SQL), databases. A table can have a single binary key and value or a multitude of strongly-typed attributes. Every table has a primary key that is made up of one or more columns, just like SQL. This could be a single column, such as a unique user ID, or a compound key, such as a (host.metric.timestamp) tuple to a machine-time-series database. Rows can be easily read, updated, and deleted by their primary keys. Kudu's data model is simple and easy to use. It makes it easy to port legacy applications and build new ones. You can use standard tools such as Spark or SQL engines to analyze your tables. Tables are self-describing. Kudu's APIs were designed to be simple to use.

Altair Panopticon Streaming Analytics allows engineers and business users to create, modify, and deploy advanced event processing and data visualization apps with a drag and drop interface. They can connect to any data source, including streaming feeds and time-series database, and develop stream processing programs. They can also design visual user interfaces to give them the perspective they need to make informed decisions based upon large amounts of rapidly changing data.

Couchbase Capella, a cloud-based database platform, optimizes storage and processing of JSON-native operational data and analytical data. A faster and better DBaaS with real-time analytics for edge, transaction, search, AI and edge applications. Capella iQ provides natural language coding help. Capella is a multipurpose data base that supports operational and analytical workloads and use-cases, including JSON analysis, vector search, mobile synchronization, and more. It delivers millisecond response at scale, with the best price-performance of any document Database-as-a-Service. Try it for free today, and you'll be up and running within minutes. Capella is the best way to future-proof your business, whether you're facing challenges in development, architecture, or deployment.

The Unified Analytics Warehouse. The Unified Analytics Warehouse is the best place to find high-performing analytics and machine learning at large scale. Tech research analysts are seeing new leaders as they strive to deliver game-changing big data analytics. Vertica empowers data-driven companies so they can make the most of their analytics initiatives. It offers advanced time-series, geospatial, and machine learning capabilities, as well as data lake integration, user-definable extensions, cloud-optimized architecture and more. Vertica's Under the Hood webcast series allows you to dive into the features of Vertica - delivered by Vertica engineers, technical experts, and others - and discover what makes it the most scalable and scalable advanced analytical data database on the market. Vertica supports the most data-driven disruptors around the globe in their pursuit for industry and business transformation.

Dgraph is an open-source, low-latency, high throughput native and distributed graph database. DGraph is designed to scale easily to meet the needs for small startups and large companies with huge amounts of data. It can handle terabytes structured data on commodity hardware with low latency to respond to user queries. It addresses business needs and can be used in cases that involve diverse social and knowledge networks, real-time recommendation engines and semantic search, pattern matching, fraud detection, serving relationship information, and serving web applications.

Unlock the full potential of your enterprise's time-series data with the dataPARC Historian. This solution elevates data management, facilitating smooth and secure data flow across your organization. Its design ensures easy integration with AI, ML, and cloud technologies, paving the way for innovative adaptability and deeper insights. Rapid access to data, advanced manufacturing intelligence, and scalability make dataPARC Historian the optimal choice for businesses striving for excellence in their operations. It's not just about storing data; it's about transforming data into actionable insights with speed and precision. The dataPARC Historian stands out as more than just a repository for data. It empowers enterprises with the agility to use time-series data more effectively, ensuring decisions are informed and impactful, backed by a platform known for its reliability and ease of use.

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.

PipelineDB is a PostgreSQL extension that allows high-performance time series aggregation. It's designed to power real-time reporting and analytics. PipelineDB allows the creation of continuous SQL queries that continuously aggregate time-series data, and store only the aggregate output into regular, queryable tables. This concept can be described as highly-accurate, incrementally updated materialized views that do not need to be manually refreshed. PipelineDB is extremely efficient in aggregation workloads because raw time-series data are never written to disk. Continuous queries can be linked together to create arbitrary networks of continuous SQL by creating their own output streams.

QuasarDB is Quasar's brain. It is a high-performance distributed, column-oriented, timeseries database management software system that delivers real-time data for petascale use cases. You can save up to 20X on your disk usage Quasardb compression and ingestion are unmatched. Feature extraction can be performed up to 10,000 times faster. QuasarDB is able to extract features from raw data in real-time thanks to a combination of a builtin map/reduce engine, an aggregate engine that leverages SIMD from modern processors, and stochastic indices that consume virtually no disk space.

Amazon Neptune is a fully managed graph database service that allows you to quickly and reliably build applications that can work with highly connected data sets. Amazon Neptune's core is a purpose-built graph database engine that can store billions of relationships and query the graph with only milliseconds latency. Amazon Neptune supports the popular graph models Property Graph, W3C's RDF, as well as their respective query languages Apache TinkerPop Gremlin, SPARQL. This allows you to quickly build queries that efficiently navigate large datasets. Neptune supports graph use cases like recommendation engines, fraud detection and knowledge graphs. It also powers network security and drug discovery.

The KX Delta Platform, a high-performance data management system for enterprises, is designed to capture, analyze, and store real-time data and historical data. Built on kdb+ - the world's most popular time-series data base - it offers flexible configuration options to support key deployment requirements such as redundancy and load balancing. It also ensures high availability. Robust security measures, such as LDAP authorization and data encryption, ensure strict compliance to data sensitivity and security requirements. The platform allows users to visualize data using a dashboard builder and interactive data playback. It also generates reports automatically. This platform is a powerful tool for program management. It allows for the management, manipulation and exploration of large real-time and historic datasets. Processing is done at high speeds to support mission critical applications.

SparkBeyond Discovery automatically analyzes complex data and finds answers to business problems in places you might not have thought. Integrate external data seamlessly into your search space to gain a better understanding of the real factors that influence outcomes and a more holistic view of your business reality. Analytics projects can be made more experimental by interacting in natural language with data and insights. This allows for a deeper partnership between business stakeholders and analytics. Future-proof the hard-earned benefits of analytics by maintaining an evergreen cycle for inputs and outputs. Your insights should change with the world. Auto-connect all data types, from time-series to geospatial, in their raw, granular form without having to write a single line code. Integrate our curated global knowledge database (maps, Wikipedia and demographics) to see the whole picture.

Data management and analytics platform that addresses data challenges and enables real-time decision making. DataLux includes plug-and-play adaptors that allow for the aggregation and visualization of large data sets. The data lake can be used to prevent new innovations. You can store data and make it available for data modeling. Containeristion can be used to create portable applications in a public, private, or on-premise cloud. Multiple time-series and inferred data can be combined, such as stock exchange tick data and stock market policy actions. You can also combine related and cross-industry data to extract causal information about stock market, macroeconomics, and other factors. By providing insights and guiding key decisions for product improvement, business decisions can be made. You can conduct interdisciplinary A/B tests across product design, engineering, and product development from ideation to decision-making.

Kibana is a free, open-source user interface that allows you to visualize your Elasticsearch data as well as navigate the Elastic Stack. You can track query load and understand how requests flow through your apps. Kibana allows you to choose how you want to present your data. Start with one question to see what interactive visualizations lead you. Kibana core ships the classics: histograms and line graphs, pie charts and pie charts, as well as sunbursts and sunbursts. You can also search across all your documents. Use Elastic Maps to explore your location data. Or get creative and visualize custom layers or vector shapes. Our curated time series interfaces allow you to perform advanced analysis of your Elasticsearch data. Use powerful, easy to learn expressions to describe queries, transformations, or visualizations.