Alternatives to CrateDB

Cloud SQL is a fully managed relational database service that supports MySQL, PostgreSQL and SQL Server. It includes rich extensions, configuration flags and developer ecosystems. Cloud SQL offers $300 in credits for new customers. You won't pay until you upgrade. Reduce maintenance costs by using fully managed MySQL PostgreSQL SQL Server databases. SRE team provides 24/7 support for reliable and secure services. Data encryption in transit and at rest. Private connectivity with Virtual Private Cloud, user-controlled network access and firewall protection. Compliant with SSAE 16 ISO 27001 PCI DSS and HIPAA. Scale your database instances with a single API request, whether you are just testing or need a highly-available database in production. Standard connection drivers and integrated migration tools let you create and connect to a database in a matter of minutes.

Raima Database Manager, an embedded time series database that can be used for Edge and IoT devices, can run in-memory. It is a lightweight, secure, and extremely powerful RDBMS. It has been field tested by more than 20 000 developers around the world and has been deployed in excess of 25 000 000 times.

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.

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.

Choose from a wide range of SQL features to realise your applications from pure analytics to hybrid transactional/analytical processing. MonetDB returns queries in seconds, if not faster, when you are curious about your data and when you need to work efficiently. You can (re)use your code when you need specialised function: Use the hooks to add your user-defined functions to SQL, Python R, C/C++, or R. Join us to expand the MonetDB community that spans 130+ countries. We have students, teachers, researchers and small businesses. Join the most important Database in Analytical Jobs to surf the innovation! MonetDB's simple setup will quickly get your DBMS up to speed.

Apache Druid, an open-source distributed data store, is Apache Druid. Druid's core design blends ideas from data warehouses and timeseries databases to create a high-performance real-time analytics database that can be used for a wide range of purposes. Druid combines key characteristics from each of these systems into its ingestion, storage format, querying, and core architecture. Druid compresses and stores each column separately, so it only needs to read the ones that are needed for a specific query. This allows for fast scans, ranking, groupBys, and groupBys. Druid creates indexes that are inverted for string values to allow for fast search and filter. Connectors out-of-the box for Apache Kafka and HDFS, AWS S3, stream processors, and many more. Druid intelligently divides data based upon time. Time-based queries are much faster than traditional databases. Druid automatically balances servers as you add or remove servers. Fault-tolerant architecture allows for server failures to be avoided.

Volt Active Data is a data platform that makes your tech stack more efficient, faster, and cheaper. It allows your applications and your company to scale seamlessly to meet the ultra low latency SLAs of 5G and IoT. Volt Active Data is designed to complement your existing big data investments such as NoSQL and Hadoop, Kubernetes and Kafka, as well traditional databases or data warehouses. It replaces the many layers that are required to make contextual decisions about streaming data with one unified layer that can handle everything from ingest to action in less then 10 milliseconds. There is a lot of data in the world. It's all stored, forgotten, then deleted. "Active Data" refers to data that must be acted upon immediately in order to gain business value. You have many options for data storage, both traditional and non-SQL. If you are able to act quickly enough to 'influence' the moment, there is data that you can also make money with.

IBM Informix®, a fast and flexible database that can seamlessly integrate SQL, NoSQL/JSON and time series data, is available. Informix's versatility and ease-of-use make it a popular choice for a wide variety of environments, including enterprise data warehouses and individual application development. Informix is also well-suited for embedded data management solutions due to its small footprint and self-managing capabilities.

The Open, Multi-Cloud Stack to Modern Data Apps. Built on Apache Cassandra™, an open-source Apache Cassandra™. Global scale and 100% uptime without vendor lock in You can deploy on multi-clouds, open-source, on-prem and Kubernetes. For a lower TCO, use elastic and pay-as you-go. Stargate APIs allow you to build faster with NoSQL, reactive, JSON and REST. Avoid the complexity of multiple OSS projects or APIs that don’t scale. It is ideal for commerce, mobile and AI/ML. Get building modern data applications with Astra, a database-as-a-service powered by Apache Cassandra™. Richly interactive apps that are viral-ready and elastic using REST, GraphQL and JSON. Pay-as you-go Apache Cassandra DBaaS which scales easily and affordably

Google Cloud Bigtable provides a fully managed, scalable NoSQL data service that can handle large operational and analytical workloads. Cloud Bigtable is fast and performant. It's the storage engine that grows with your data, from your first gigabyte up to a petabyte-scale for low latency applications and high-throughput data analysis. Seamless scaling and replicating: You can start with one cluster node and scale up to hundreds of nodes to support peak demand. Replication adds high availability and workload isolation to live-serving apps. Integrated and simple: Fully managed service that easily integrates with big data tools such as Dataflow, Hadoop, and Dataproc. Development teams will find it easy to get started with the support for the open-source HBase API standard.

Amazon Aurora is a MySQL and PostgreSQL-compatible relational database built for the cloud, that combines the performance and availability of traditional enterprise databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora is five times faster that standard MySQL databases and three time faster than standard PostgreSQL database. It offers the same security, availability, reliability, and cost-effectiveness as commercial databases, but at a fraction of the cost. Amazon Aurora is fully managed and maintained by Amazon Relational Database Service, (RDS). This automates tedious administration tasks such as hardware provisioning, database setup, patching and backups. Amazon Aurora is a distributed, fault-tolerant and self-healing storage that auto-scales up 64TB per database instance. It offers high availability and performance with up to 15 low latency read replicas, point in time recovery, continuous backup to Amazon S3, replication across threeAvailability Zones, and continuous backup to Amazon S3.

MariaDB Platform is an enterprise-level open-source database solution. It supports transactional, analytical, and hybrid workloads, as well as relational and JSON data models. It can scale from standalone databases to data warehouses to fully distributed SQL, which can execute millions of transactions per second and perform interactive, ad-hoc analytics on billions upon billions of rows. MariaDB can be deployed on prem-on commodity hardware. It is also available on all major public cloud providers and MariaDB SkySQL, a fully managed cloud database. MariaDB.com provides more information.

IBM Db2®, a family of hybrid data management tools, offers a complete suite AI-empowered capabilities to help you manage structured and unstructured data both on premises and in private and public clouds. Db2 is built upon an intelligent common SQL engine that allows for flexibility and scalability.

Proficy Historian, a top-of-the-line historian software solution, collects industrial time-series data and A&E data at a high speed, stores it securely and efficiently, distributes it, and allows fast retrieval and analysis. This results in greater business value. Proficy Historian has thousands of satisfied customers around the globe and decades of experience. It makes data available for process and asset performance analysis. Proficy Historian's latest version improves usability, configuration and maintainability by making significant architectural improvements. The solution's powerful yet simple features will allow you to unlock new value for your equipment, process data and business models. Remote collector management with UX. Horizontal scaling that allows enterprise-wide data visibility

Real-time analytics on raw data. Live ingest from S3, DynamoDB, DynamoDB and more. Raw data can be accessed as SQL tables. In minutes, you can create amazing data-driven apps and live dashboards. Rockset is a serverless analytics and search engine that powers real-time applications and live dashboards. You can directly work with raw data such as JSON, XML and CSV. Rockset can import data from real-time streams and data lakes, data warehouses, and databases. You can import real-time data without the need to build pipelines. Rockset syncs all new data as it arrives in your data sources, without the need to create a fixed schema. You can use familiar SQL, including filters, joins, and aggregations. Rockset automatically indexes every field in your data, making it lightning fast. Fast queries are used to power your apps, microservices and live dashboards. Scale without worrying too much about servers, shards or pagers.

High-performance, cross-platform columnar historical time-series columnar data featuring: - An in-memory computation engine - A streaming processor that streams real-time - A combination of a programming language and expressive query, q

RDS for MySQL provides a reliable and scalable cloud-based database service. Database deployment is quick and easy. This allows you to focus on application development. You can say goodbye to tedious database O&M once again. With just a few clicks, you can easily create a MySQL database. RDS for MySQL allows you to deploy software and hardware and manage your databases. This includes monitoring metrics, installing patches, managing metrics, dynamically scaling up and down, failing over, and installing patches. RDS for MySQL excels in handling large numbers of concurrent requests thanks to its optimized kernel thresholds. It is superior to the MySQL community edition. RDS for MySQL uses implicit key primary keys to ensure that there is almost no delay in primary/standby replication. No data is lost, even when your database is loaded heavily. You can easily add 1 TB storage to your database as your storage requirements increase. This allows you to create read replicas quickly and with zero downtime.

RDS for MySQL is a relational cloud database with high performance and reliability. It offers a simple and convenient web-based interface, reliable data backups and recovery features, as well as a complete security management service. RDS for MySQL is a relational database service that provides high-performance, high-reliability. It supports three series: stand-alone basic, dual high availability, and Raft Financial. It implements monitoring, automatic failureover, data backup, recovery, instance management and other features. It also meets the higher demands of customers for data management and storage, through slow SQL statistics, diagnosis, and reliable services such as cross domain hot instance groups. It supports disaster recovery for a single availability area and disaster recovery across multiple availability zones.

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).

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.

Redis Labs is the home of Redis. Redis Enterprise is the best Redis version. Redis Enterprise is more than a cache. Redis Enterprise can be free in the cloud with NoSQL and data caching using the fastest in-memory database. Redis can be scaled, enterprise-grade resilience, massive scaling, ease of administration, and operational simplicity. Redis in the Cloud is a favorite of DevOps. Developers have access to enhanced data structures and a variety modules. This allows them to innovate faster and has a faster time-to-market. CIOs love the security and expert support of Redis, which provides 99.999% uptime. Use relational databases for active-active, geodistribution, conflict distribution, reads/writes in multiple regions to the same data set. Redis Enterprise offers flexible deployment options. Redis Labs is the home of Redis. Redis JSON, Redis Java, Python Redis, Redis on Kubernetes & Redis gui best practices.

Greenplum Database®, an open-source data warehouse, is a fully featured, advanced, and fully functional data warehouse. It offers powerful and fast analytics on petabyte-scale data volumes. Greenplum Database is uniquely designed for big data analytics. It is powered by the most advanced cost-based query optimizer in the world, delivering high analytical query performance with large data volumes. The Apache 2 license is used to release Greenplum Database®. We would like to thank all of our community contributors. We are also open to new contributions. We encourage all contributions to the Greenplum Database community, no matter how small. Open-source, massively parallel data platform for machine learning, analytics, and AI. Rapidly create and deploy models to support complex applications in cybersecurity, predictive management, risk management, fraud detection, among other areas. The fully integrated, open-source analytics platform is now available.

Warp 10 is a modular open source platform that collects, stores, and allows you to analyze time series and sensor data. Shaped for the IoT with a flexible data model, Warp 10 provides a unique and powerful framework to simplify your processes from data collection to analysis and visualization, with the support of geolocated data in its core model (called Geo Time Series). Warp 10 offers both a time series database and a powerful analysis environment, which can be used together or independently. It will allow you to make: statistics, extraction of characteristics for training models, filtering and cleaning of data, detection of patterns and anomalies, synchronization or even forecasts. The Platform is GDPR compliant and secure by design using cryptographic tokens to manage authentication and authorization. The Analytics Engine can be implemented within a large number of existing tools and ecosystems such as Spark, Kafka Streams, Hadoop, Jupyter, Zeppelin and many more. From small devices to distributed clusters, Warp 10 fits your needs at any scale, and can be used in many verticals: industry, transportation, health, monitoring, finance, energy, etc.

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.

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.

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.

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.

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.

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.

VictoriaMetrics is a cost-effective, scalable monitoring solution that can also be used as a time series database. It can also be used to store Prometheus' long-term data. VictoriaMetrics is a single executable that does not have any external dependencies. All configuration is done using explicit command-line flags and reasonable defaults. It provides global query view. Multiple Prometheus instances, or other data sources, may insert data into VictoriaMetrics. Later this data may be queried via a single query. It can handle high cardinality and high churn rates issues by using a series limiter.

CUBRID is a relational DBMS optimized for online transaction processing (OLTP) that complies with ANSI SQL standards and provides MVCC support, High-Availability (HA) capabilities, and GUI-based tools for DB management/migration. It also provides Oracle/MySQL compatibility and supports a variety of interfaces, including JDBC. * [Major RDBMS Features]: - ANSI SQL standard and support extended SQL syntaxes - Support VIEW/TRIGGER/PRIMARY KEY/FOREIGN KEY/SERIAL - Support Stored Procedure/Function - Seamless transactions: COMMIT/ROLLBACK/SAVEPOINT - Support automatic recovery in the event of failure CUBRID consists of a 3-tier structure of applications/interfaces, brokers, and servers, and the flexibility to build systems is ideal for data-intensive online transaction processing (OLTP) services. CUBRID provides ease of installation and native GUI-based administration tools for developers' convenience. Multi-threaded, multi-server architecture, native broker middleware, cost-based optimizer, and intensive caching techniques for your OLTP services. Very accurate predictable automatic fail-over built-in technology, based on the CUBRID Heartbeat native engine core.

Scale as much as you need, with no limitations Globally distributed ACID-compliant database that automatically handles replicas and sharding. Transaction processing is also included. This allows you to quickly scale up to meet any usage pattern and ensure the success of your products. Make your database easily accessible anywhere: Cloud Spanner is built using Google's dedicated network, and battle tested by Google services that are used by billions. It provides up to 99.999% availability and zero downtime for scheduled maintenance or schema changes. You can do less tedious tasks and have a more enjoyable experience. IT administrators and DBAs are overwhelmed with managing databases. Cloud Spanner makes it easy to create or scale a globally replicated database in just a few clicks. This also reduces the cost of maintaining databases.

Postico is a database client. Postico can connect to either a local PostgreSQL Server on your Mac or remote servers on a different computer. Postgres.app is a great option if you are looking to install a local PostgreSQL Server on your Mac. Postgres.app will create a PostgreSQL Server on your Mac. It will create a new directory and an empty database the first time it starts. After a few seconds, the server will start and wait for connections. Postgres.app cannot start if another PostgreSQL Server is running on your computer. If you have problems, make sure to deactivate and uninstall any other PostgreSQL servers. Reboot your computer after uninstalling! First, create a favorite to connect to a PostgreSQL Server with Postico. A favorite contains parameters that allow you to connect to a server. Some parameters for connecting to a server are optional.

High performance, fully managed and hosted PostgreSQL. All the extensions you need right out of the box. You can set up fully managed PostgreSQL within 10 minutes using our web console, or programmatically via our API or CLI, Terraform provider, or Kubernetes operators. Stand-by nodes can be used as read replicas. You can also create additional read replicas in other public clouds or regions. Our Point-In-Time-Recovery lets you restore your data to any time within your selected retention period. Fully managed SQL relational database that can be deployed in the cloud of choice. It is known for its reliability, robust features, and exceptional performance. It can be used in almost any situation, including creating location-based services, mission-critical business applications, or as a general transactional data base.

With relational database management systems (RDBMS), solutions from SAP SQL Anywhere, you can run your business from anywhere. Secure, reliable data management for servers without a DBA. Also, synchronization for thousands of mobile devices, IoT systems, and remote environments. Remote offices and mobile devices can receive critical business data for faster, more accurate decision-making. Mobile applications can be run reliably and efficiently even without the need for technical staff. This gives remote workers and mobile workers a seamless experience, even when they're not on-site. With built-in self-managing capabilities, users can streamline data maintenance tasks and resolve issues quickly and efficiently. Strong encryption features can help protect and secure your data even during data exchange and transmission.

Businesses struggle to find answers as data volumes increase faster than ever. Teradata Vantage™, solves this problem. Vantage uses 100 per cent of the data available to uncover real-time intelligence at scale. This is the new era in Pervasive Data Intelligence. All data across the organization is available in one place. You can access it whenever you need it using preferred languages and tools. Start small and scale up compute or storage to areas that have an impact on modern architecture. Vantage unifies analytics and data lakes in the cloud to enable business intelligence. Data is growing. Business intelligence is becoming more important. Four key issues that can lead to frustration when using existing data analysis platforms include: Lack of the right tools and supportive environment required to achieve quality results. Organizations don't allow or give proper access to the tools they need. It is difficult to prepare data.

Blueflood, a distributed metric processing system that is high throughput and low latency for multi-tenant multi-tenants, is behind Rackspace Metrics. It is currently being used in production by Rackspace Monitoring team as well as Rackspace public cloud team to store metrics created by their systems. Blueflood is also used in large-scale Rackspace deployments. Blueflood data can be used to create dashboards, reports, graphs, or any other use that involves time-series information. It is focused on near-realtime data and data that can be queryable within milliseconds of ingestion. You send metrics to ingestion service. The Query service allows you to query your metrics. Rollups are processed offline in the background so that queries with long time periods are quickly returned.

Riak®, TS is an enterprise-grade NoSQL Time Series Database that is specifically designed for IoT data and Time Series data. It can ingest, transform, store, and analyze massive amounts of time series information. Riak TS is designed to be faster than Cassandra. Riak TS masterless architecture can read and write data regardless of network partitions or hardware failures. Data is evenly distributed throughout the Riak ring. By default, there are three copies of your data. This ensures that at least one copy is available for reading operations. Riak TS is a distributed software system that does not have a central coordinator. It is simple to set up and use. It is easy to add or remove nodes from a cluster thanks to the masterless architecture. Riak TS's masterless architecture makes it easy for you to add or remove nodes from your cluster. Adding nodes made of commodity hardware to your cluster can help you achieve predictable and almost linear scale.

Hypertable provides scalable database capacity at maximum speed to speed up big data applications and reduce your hardware footprint. Hypertable offers superior performance and efficiency over other competitors, which can translate into significant cost savings. It is a proven, scalable design that powers hundreds Google services. Open source brings all the benefits of open-source with a vibrant community. C++ implementation for optimal performance. Support for your business-critical big-data application is available 24/7/365 The employer of all core Hypertable developers provides unrivalled access to the Hypertable brain power. Hypertable was created to solve the scalability issue. This problem is not well handled by traditional RDBMSs. Hypertable is a Google-developed design that meets their scalability requirements. It solves the scale problem better then any other NoSQL solutions.

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.

Amazon ElastiCache makes it easy to create, manage, and scale popular open source compatible in-memory cloud data stores. You can build data-intensive apps and improve the performance of existing databases by retrieving data in high-throughput and low latency, in-memory storages. Amazon ElastiCache is popular for real-time applications such as Caching, Session Stores and Gaming, Geospatial Service, Real-Time Analytics and Queuing. Amazon ElastiCache provides fully managed Redis, Memcached and other services for demanding applications that need sub-millisecond response time. Amazon ElastiCache is an in-memory cache and data store that can support the most demanding applications that require sub-millisecond response time. Amazon ElastiCache delivers secure, lightning fast performance by using an optimized stack that runs on customer-dedicated nodes.

SiriDB is optimized for speed. Inserts and queries are answered quickly. You can speed up your development with the custom query language. SiriDB is flexible and can be scaled on the fly. There is no downtime when you update or expand your database. You can scale your database without losing speed. As we distribute your time series data across all pools, we make full use of all resources. SiriDB was designed to deliver unmatched performance with minimal downtime. A SiriDB cluster distributes time series across multiple pools. Each pool has active replicas that can be used for load balancing or redundancy. The database can still be accessed even if one of the replicas is unavailable.

QuestDB is a relational database that uses column-oriented databases. It can be used for event and time series data. It uses SQL with extensions to time series to aid in real-time analytics. These pages provide information about core concepts of QuestDB. They include setup steps, usage guides, as well as reference documentation for syntax, APIs, and configuration. This section explains the architecture of QuestDB and how it stores and queries data. It also introduces new capabilities and features that are unique to the system. The core feature of QuestDB is the designated timestamp. It enables partitioning and time-oriented language capabilities. The symbol type makes it easy to store and retrieve repetitive strings. QuestDB's storage model describes how it stores records and partitions within tables. Indexes can be used to provide faster access to specific columns. Partitions can be used to provide significant performance improvements in calculations and queries. SQL extensions enable time series analysis that is efficient and concise with a concise syntax.

Amazon Timestream is a fast, scalable and serverless time series data service for IoT/operational applications. It makes it possible to store and analyze trillions per day up to 1000 times faster than traditional relational databases and at as low as 1/10th of the cost. Amazon Timestream helps you save time and money when managing the lifecycles of time series data. It stores recent data in memory and moves historical data to a cost-optimized storage tier according to user defined policies. Amazon Timestream's purpose-built query tool allows you to access and analyze both recent and historic data simultaneously, without having to specify in the query whether the data is in the in-memory tier or the cost-optimized. Amazon Timestream's built-in time series analytics functions allow you to identify trends and patterns within your data in real-time.

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.

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.

ClickHouse is an open-source OLAP database management software that is fast and easy to use. It is column-oriented, and can generate real-time analytical reports by using SQL queries. ClickHouse's performance is superior to comparable column-oriented database management software currently on the market. It processes hundreds of millions of rows to more than a million and tens if not thousands of gigabytes per second. ClickHouse makes use of all hardware available to process every query as quickly as possible. Peak processing speed for a single query is more than 2 Terabytes per Second (after decompression, only utilized columns). To reduce latency, reads in distributed setups are automatically balanced between healthy replicas. ClickHouse supports multimaster asynchronous replication, and can be deployed across multiple datacenters. Each node is equal, which prevents single points of failure.

Azure Time Series Intelligences Gen2 is an open-source, scalable IoT analytics service that can be integrated into existing workflows and applications. It can be used to collect, store, query, query, and visualize data at Internet of Things scale (IoT),-data that is highly contextualized and optimized in time series. Azure Time Series Insights Gen2 can be used for operational analysis and data exploration. It allows you to spot anomalies and uncover hidden trends. It is flexible and open to all industrial IoT deployments.

BASIS Database Management System (DBMS), provides developers with relational access and fast direct navigational access. BASIS DBMS offers robust and powerful functionality that allows developers to combine powerful SQL statements with relational data access methods with performance and control over navigational operations, such as direct table and table movement and index and table access. The BASIS SQL engine allows developers to execute queries as well as other SQL statements using third-party products. Combining these two powerful data access methods yields the result set from an SQL query, or from a table that was opened directly on the server using the highly optimized BASIS ISAM engine. The result is a simple-to-use interface that supports SQL commands as well as direct-result set navigation.

We make BI and Big Data analytics easier and more efficient. Our goal is to empower business users, make BI specialists and always-busy business more independent when solving data-driven business problems. Querona is a solution for those who have ever been frustrated by a lack in data, slow or tedious report generation, or a long queue to their BI specialist. Querona has a built-in Big Data engine that can handle increasing data volumes. Repeatable queries can be stored and calculated in advance. Querona automatically suggests improvements to queries, making optimization easier. Querona empowers data scientists and business analysts by giving them self-service. They can quickly create and prototype data models, add data sources, optimize queries, and dig into raw data. It is possible to use less IT. Users can now access live data regardless of where it is stored. Querona can cache data if databases are too busy to query live.