Alternatives to VictoriaMetrics

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.

Data integration for hybrid clouds Modern, reliable data integration across both your private cloud and public cloud. All this in real-time, with change data capture and streams. Striim was developed by the executive and technical team at GoldenGate Software. They have decades of experience in mission critical enterprise workloads. Striim can be deployed in your environment as a distributed platform or in the cloud. Your team can easily adjust the scaleability of Striim. Striim is fully secured with HIPAA compliance and GDPR compliance. Built from the ground up to support modern enterprise workloads, whether they are hosted in the cloud or on-premise. Drag and drop to create data flows among your sources and targets. Real-time SQL queries allow you to process, enrich, and analyze streaming data.

Open-source monitoring solutions are able to power your alerting and metrics. Prometheus stores all data in time series. These are streams of timestamped value belonging to the same metric with the same labeled dimensions. Prometheus can also generate temporary derived times series as a result of queries. Prometheus offers a functional query language called PromQL, which allows the user to select and aggregate time series data real-time. The expression result can be displayed as a graph or tabular data in Prometheus’s expression browser. External systems can also consume the HTTP API. Prometheus can be configured using command-line flags or a configuration file. The command-line flags can be used to configure immutable system parameters such as storage locations and the amount of data to be kept on disk and in memory. . Download: https://sourceforge.net/projects/prometheus.mirror/

VictoriaMetrics Cloud allows you to run VictoriaMetrics Enterprise on AWS without having to perform typical DevOps activities such as proper configuration and monitoring, log collection, security, software updates, software protection, or backups. We run VictoriaMetrics Cloud in our environment using AWS, and provide easy to use endpoints for data ingestion. VictoriaMetrics takes care of software maintenance and optimal configuration. It has the following features: It can be used to manage Prometheus. Configure Prometheus, Vmagent or VictoriaMetrics to write data into Managed VictoriaMetrics. Then use the endpoint provided as a Prometheus source in Grafana. Each VictoriaMetrics Cloud instance runs in a separate environment so that instances cannot interfere with one another; VictoriaMetrics Cloud can be scaled-up or scaled-down in just a few clicks. Automated backups.

VictoriaMetrics Anomaly Detection, a service which continuously scans data stored in VictoriaMetrics to detect unexpected changes in real-time, is a service for detecting anomalies in data patterns. It does this by using user-configurable models of machine learning. VictoriaMetrics Anomaly Detection is a key tool in the dynamic and complex world system monitoring. It is part of our Enterprise offering. It empowers SREs, DevOps and other teams by automating the complex task of identifying anomalous behavior in time series data. It goes beyond threshold-based alerting by utilizing machine learning to detect anomalies, minimize false positives and reduce alert fatigue. The use of unified anomaly scores and simplified alerting mechanisms allows teams to identify and address potential issues quicker, ensuring system reliability.

KairosDB can push data via multiple protocols: Telnet Rest, Graphite, and Rest. You can also use plugins and other mechanisms. KairosDB stores time series using Cassandra NoSQL datastore. The schema is composed of three column families. This API allows you to list existing metric names, value tags, store metric data, and query for metric points. KairosDB will automatically install the query page. This page allows you to query data stored in the data store. It is intended for development purposes only. Aggregators can perform operations on data points and down samples. You can use standard functions such as min, max, sum and count to calculate the mean, mean, count, mean, and other parameters. The command line allows you to import and export data from the KairosDB server. The server's performance can be monitored internally by the data store.

Circonus IRONdb makes it simple to store and manage unlimited volumes of Telemetry data. It can also handle billions of metric streams. Circonus IRONdb allows users to identify areas where there is opportunity and challenges in real-time. It provides forensic, predictive and automated analytics capabilities that are unmatched by any other product. Machine learning can automatically establish a "new norm" for your data and operations as they change. Circonus IRONdb is compatible with Grafana which supports our analytics query language. We also work with Graphite-web and other visualization apps. Circonus IRONdb protects your data by storing multiple copies in a cluster IRONdb nodes. Clustering is often managed by system administrators, who spend considerable time maintaining it and keeping the system running. Circonus IRONdb allows operators the ability to set and forget their cluster and stop manually managing their time series data stores.

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.

Machbase is a time-series database that stores, analyzes, and displays a lot sensor data from different facilities in real time. It is the only DBMS that can process big data at high speed. Machbase's incredible speed is amazing! It is the most innovative product to enable real-time processing, storage and analysis of sensor data. DBMS embedded in Edge devices allows for high speed storage and inquiry of sensor data. DBMS running on a single server provides the best data storage and extraction performance. Multi-node cluster configuration with the benefits of availability and scaleability. Edge computing is a complete management solution for device, connectivity, and data.

Time Series Database (TSDB), supports high-speed data writing and reading. It has high compression ratios to ensure data storage costs are low. This service supports visualization of precision reduction and interpolation, multimetric aggregate computing, as well as query results. The TSDB service lowers storage costs and improves data writing, query, analysis efficiency. This allows you to manage large numbers of data points and collect more data. This service is widely used in a variety of industries, including IoT monitoring systems and enterprise energy management systems (EMSs), power supply monitoring systems, production security monitoring systems, and IoT monitoring. Optimizes database architectures. TSDB can read and write millions of data points in seconds. Uses an efficient compression algorithm that reduces the data point's size to 2 bytes. This saves more than 90% on storage costs.

PeerDB is the fastest, easiest, and most cost-effective solution to replicate data between Postgres and data warehouses, queues and storage. Designed to scale at any size and tailored for data stores. PeerDB replays the schema messages using replication messages from Postgres replication slots. Alerts for slot expansion and connections. Native support for Postgres toast columns and large JSONB for IoT. Optimized query designs to reduce warehouse costs. Especially useful for Snowflake or BigQuery. Support for partitioned table via both publish. Transaction snapshotting and CTID scanning provide a consistent and fast initial load. High-availability with autoscaling, advanced logs, metrics, dashboards and monitoring dashboards.

VictoriaMetrics Enterprise, a commercial product designed by the creators VictoriaMetrics, is a solution for monitoring and observability in complex environments. It's perfect for organizations with large or rapidly scaling monitoring environments. The Enterprise edition includes all of the features in the Community Edition plus additional enhancements like Downsampling Automated backups / Backup manager Data Retention Per Label/Tenant Multi Tenant Statistic & Anomaly detection. It provides stable releases and long-term support to ensure critical bug fixes, security patches, and other enhancements. The package also includes enterprise security compliance and prioritised feature requests. We can help you reduce storage costs while improving performance of historical data queries. Multiple retentions allow different storage durations for various datasets. Automatic discovery of storage updates the list without restarting services at insert and vmselect.

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.

Hawkular Metrics, a scalable, asynchronous multi-tenant, long term metrics storage engine, uses Cassandra to store the data and REST to interface. This section will provide an overview of Hawkular Metrics' key features. These and other features will be discussed in detail in the following sections. Hawkular Metrics is all based on scalability. A single instance can be backed by one Cassandra node. To handle increasing loads, Cassandra can be scaled to multiple nodes. Hawkular Metrics uses a stateless architecture which makes it easy for you to scale out. This diagram shows the many deployment options that Hawkular Metrics' scalable architecture allows. The top left picture shows the simplest deployment using a single Cassandra and one Hawkular Metrics node. The bottom right image shows that you can run more Hawkular Metrics Nodes than Cassandra.

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.

Cortex is an open-source project that adds horizontal scaling. Prometheus can scale upto 1 million samples/sec, but Cortex's horizontal scalability makes it practically inexhaustible. You need other methods to monitor individual servers or VMs in a dynamic environment. Prometheus' pull-based, service-discovery-driven metrics system was created for the dynamic nature microservices. It allows you to monitor your entire environment, regardless of how many moving parts. You can use the standard Prometheus client library to create custom metrics or you can take advantage of the many Prometheus exporters that collect data from applications such as MySQL, Redis Java, Java, ElasticSearch, and many others.

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.

InfluxDB is a purpose-built data platform designed to handle all time series data, from users, sensors, applications and infrastructure — seamlessly collecting, storing, visualizing, and turning insight into action. With a library of more than 250 open source Telegraf plugins, importing and monitoring data from any system is easy. InfluxDB empowers developers to build transformative IoT, monitoring and analytics services and applications. InfluxDB’s flexible architecture fits any implementation — whether in the cloud, at the edge or on-premises — and its versatility, accessibility and supporting tools (client libraries, APIs, etc.) make it easy for developers at any level to quickly build applications and services with time series data. Optimized for developer efficiency and productivity, the InfluxDB platform gives builders time to focus on the features and functionalities that give their internal projects value and their applications a competitive edge. To get started, InfluxData offers free training through InfluxDB University.

KX Streaming Analytics allows you to ingest and store historical and time series data. This data can then be processed, stored, processed, and analyzed instantly to provide analytics, insights, or visualizations. The platform offers the complete lifecycle of data services to ensure that your applications and users can be productive quickly. This includes query processing, query processing, tiering and migration, archiving and data protection. Our advanced analytics and visualization tools are widely used in finance and industry. They allow you to create and execute queries, calculations, aggregations and machine learning on any streaming or historical data. Data can be used across multiple hardware environments and can come from high-volume sources such as clickstreams, radio frequency identification, GPS systems and social networking sites.

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.

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

The ITTIA DB family of products combines time series, real time data streaming and analytics to reduce development costs and time. ITTIA DB IoT, a small embedded database designed for 32-bit microcontrollers with limited resources for real-time data streaming, and ITTIA DB SQL are high-performance embedded databases for time-series for single-core or multicore microprocessors. Both ITTIA DB product enable devices to monitor real-time data, process it, and store it. ITTIA DB also offers products for Electronic Control Units in the automotive industry. ITTIA DB's data security protocols protect data from malicious access through encryption, authentication and DB Seal. ITTIA SDL conforms to the principles of IEC/ISO 62443. ITTIA DB can be embedded in a SDK designed for edge devices to collect, enrich, and process real-time data streams. Search, filter, combine, and aggregate data at the edge.

OpenTSDB is composed of a Time Series Daemon, (TSD), and a set of command-line utilities. OpenTSDB can only be interacted with by one or more TSDs. Each TSD can be run independently. There is no master or shared state, so you can run as many TSDs you need to handle any load. Each TSD uses the HBase open-source database or hosted Google Bigtable service for time-series data storage and retrieval. The data schema is optimized for fast aggregations and retrieval of similar time series, minimizing storage space. The TSD does not require users to directly access the underlying store. The TSD can be communicated with via a simple telnet protocol, an HTTP API, or a built-in GUI. OpenTSDB's first step is to send time series data directly to the TSDs. OpenTSDB has many tools that allow you to pull data from different sources.

Heroic is an open source monitoring system originally developed by Spotify to address problems associated with large-scale gathering and near-real-time analysis. Heroic only uses a few components that are responsible for specific tasks. As long as you have the hardware spending, indefinite retention. Federation support allows you to connect multiple Heroic clusters to a global interface. Heroic has a limited number of components that are responsible for specific tasks. Consumption metrics are performed by consumers. It was difficult to navigate hundreds of millions of time series in a vacuum. Heroic supports federating requests which allows multiple Heroic clusters to provide services through one global interface. This allows one cluster to operate in isolation within its own zone, thereby reducing geographical traffic.

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.

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.

kdb Insights, a cloud native, high-performance analytics solution designed for real-time data analysis of streaming and historical data, is a platform that can be used to analyze both streams and historical information. It allows for intelligent decision making regardless of data volume and velocity. It offers unmatched performance and price, and delivers analytics up to 100-fold faster than other solutions. The platform allows interactive data visualization via real-time dashboards to facilitate instantaneous insight and decision-making. It also integrates machine-learning models to predict and cluster structured data, detect patterns, score it, and enhance AI capabilities for time-series datasets. kdb Insights is scalable enough to handle large volumes of real-time data and historical data. This has been proven with volumes up to 110 Terabytes per Day. Its simple data intake and quick setup accelerate time-to value. Native support for q SQL and Python is also available, as well as compatibility with other programming languages via RESTful interfaces.

ArcadeDB allows you to manage complex models without any compromises. Polyglot Persistence is gone. There is no need to have multiple databases. ArcadeDB Multi-Model databases can store graphs and documents, key values, time series, and key values. Each model is native to the database engine so you don't need to worry about translations slowing down your computer. ArcadeDB's engine was developed with Alien Technology. It can crunch millions upon millions of records per second. ArcadeDB's traversing speed does not depend on the size of the database. It doesn't matter if your database contains a few records or a billion. ArcadeDB can be used as an embedded database on a single server. It can scale up by using Kubernetes to connect multiple servers. It is flexible enough to run on any platform that has a small footprint. Your data is protected. Our unbreakable fully transactional engine ensures durability for mission-critical production database databases. ArcadeDB uses the Raft Consensus Algorithm in order to maintain consistency across multiple servers.

You can deploy production-ready change data capture pipes for high-volume, real time data replication without writing a single line code. Supercharged Change Data Capture. Arcion's distributed Change Data Capture, CDC, allows for automatic schema conversion, flexible deployment, end-to-end replication and much more. Arcion's zero-data loss architecture ensures end-to-end consistency and built-in checkpointing. You can forget about performance and scalability concerns with a distributed, highly parallel architecture that supports 10x faster data replication. Arcion Cloud is the only fully managed CDC offering. You'll enjoy autoscaling, high availability, monitoring console and more. Reduce downtime and simplify data pipelines architecture.

BangDB integrates AI, streaming and graph analytics within its DB to allow users to deal complex data of all types, such as text, images and objects. Real-time data processing and analysis Many types of data are required to be ingested and processed simultaneously for today's use cases. BangDB supports almost all the data formats that are useful to users to solve their problem quickly. The rise of real-time data allows for real-time streaming and predictive analytics to optimize business operations.

The HPE Cloud Volumes service provides enterprise Data Protection, Hybrid, and Multi-Cloud Storage Services. These services are fully compatible with your multi-directional edge, cloud migration and on-premises strategies. Cloud-like agility, scaleability, innovation, security, rapid deployment, and pay-as you-go flexibility. HPE Cloud Volumes backup is a cloud backup service that protects your cloud, on-premises, and edge workloads. Cloud Volumes Backup makes data protection easier, more efficient, reliable, and more agile. It provides a true cloud experience, allowing you to backup directly from any storage array or backup ISV without the need for an appliance. The HPE Cloud Volumes Block multi- and hybrid cloud service is designed to store and manage your data. This data can be attached dynamically without vendor lock-in or egress costs to different cloud compute providers. This data is ideal for hybrid CI/CD, recovery, hybrid migration, hybrid data protection, and bi-directional migration.

When you have the expertise of an enterprise partner combined with a scalable, easy-to-use data management suite, you can take back control of your data, access it from anywhere, ensure security and compliance, and unlock its power to grow your business. Why Use Sesame Software? Relational Junction builds, populates, and incrementally refreshes your data automatically. Enhance Data Quality - Convert data from multiple sources into a consistent format – leading to more accurate data, which provides the basis for solid decisions. Gain Insights - Automate the update of information into a central location, you can use your in-house BI tools to build useful reports to avoid costly mistakes. Fixed Price - Avoid high consumption costs with yearly fixed prices and multi-year discounts no matter your data volume.

JaguarDB allows for fast ingestion of time-series data and location-based data. It can also index in both time and space. It is also quick to back-fill time series data (inserting large amounts of data in the past time). Time series are usually a sequence of data points that have been indexed in order of time. JaguarDB uses the term time series to mean both a sequence data points and a set of tick tables that hold aggregated data values over a specified time span. JaguarDB's time series tables can contain a base table that stores data points in time order and tick tables such daily, weekly, monthly, and daily tables to store aggregated information within these time periods. The RETENTION format is identical to the TICK format, but it can have any number or retention periods. The RETENTION indicates how long data points in the base tables should be kept.

Telegraf is an open-source server agent that helps you collect metrics from your sensors, stacks, and systems. Telegraf is a plugin-driven agent that collects and sends metrics and events from systems, databases, and IoT sensors. Telegraf is written in Go. It compiles to a single binary and has no external dependencies. It also requires very little memory. Telegraf can gather metrics from a wide variety of inputs and then write them into a wide range of outputs. It can be easily extended by being plugin-driven for both the collection and output data. It is written in Go and can be run on any system without external dependencies. It is easy to collect metrics from your endpoints with the 300+ plugins that have been created by data experts in the community.

A cross-platform, high-performance historical time-series database featuring: - An in memory compute engine A real-time stream processor - A query and programming language that is expressive called q kdb+ is the engine behind kdb insights portfolio and KDB.AI. Together, they deliver time-oriented data insight and generative AI capabilities for the world's largest enterprise organizations. kdb+ is the fastest columnar analytics database in memory, according to independent benchmarking*. It delivers unmatched value for businesses that operate in the most challenging data environments. kdb+ helps businesses navigate rapidly changing data environments by improving decision-making processes.

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.

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.

Amazon FinSpace simplifies the running of kdb insights applications on AWS. Amazon FinSpace automates undifferentiated tasks to provision, integrate and secure infrastructure for Kdb Insights. Amazon FinSpace also provides easy-to use APIs that allow customers to configure and run new applications for kdb insights in just a few moments. Amazon FinSpace allows customers to move their existing kdb-Insights applications into AWS, allowing them to enjoy the cloud benefits without the hassle of managing the infrastructure themselves. KX's Kdb Insights, a high-performance time-series analytics engine, is optimized for real-time analysis and the analysis of multi-petabytes of historical data. Capital Markets customers use Kdb insights to power their business-critical workloads such as options pricing and transaction cost analysis. Eliminate the need to integrate 15 AWS services in order to deploy kdb.

OneTick Database is embraced by top banks, brokerages and data vendors as well as exchanges, markets, hedge funds, market makers, mutual funds and other financial institutions. OneTick is the best enterprise-wide solution for tick data collection, streaming analytics and data management. OneTick is loved by top hedge funds, mutual funds and banks as well as brokers, banks, brokerages and market makers. OneTick's proprietary time-series database is a unified platform for multi-asset classes. It includes a fully integrated streaming analytics engine, built-in business logic, and eliminates the need to use multiple disparate systems. The system has the lowest total cost-of-ownership.

The beauty of Canary Historian's solution is that it works on-site as well as for the entire enterprise. You can log data locally and send it to your enterprise historian simultaneously. The best part is that the solution grows with you. A single Canary Historian is capable of logging more than two million tags. Multiple Canary Historians can also be clustered to manage tens of thousands of tags. Enterprise historian solutions can be hosted in either your own data centers, or in AWS or Azure. Canary Historians are not dependent on specialized teams of ten or more to maintain, unlike other enterprise history solutions. The Canary Historian, a NoSQL time-series database, uses loss-less compression algorithms for high-speed performance without data interpolation.

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.

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.

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.

Versio.io Enterprise Software is designed to detect and process changes in an enterprise company. We have created a new type of enterprise product thanks to our innovative and unique approaches. We offer insight into our research and development. There can be relationships between assets and configurations. These relationships are an important extension of information. These information is only part of the original data sources. Versio.io can automatically recognize and map such relationships using the topology service. This allows us to map relationships and dependencies between instances, from any data source. All business-relevant assets can be captured, historicalised, topologised, and stored in a central repository.

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.

Parallel query engine with symbol- and time-indexed data access. Extended SQL syntax with advanced filtering, aggregations and more. Consolidate all quotes, trades and snapshots in one place. Strategy backtesting using high-frequency data. Quantitative and market microstructure analysis. Granular transaction cost analysis and rollup report. Market surveillance and anomaly detection. Non-transparent ETF/ETN decomposition. FAST, SBE and proprietary protocols. Plain text protocol. Consolidated and direct feeds. Built-in latency monitoring tools. End-of-day archives. ETL from retail and institutional financial data platforms. Parallel SQL engine with syntax extensions. Advanced filtering via trading session, auction stage, and index composition. Optimized aggregates to OHLCV and VWAP calculations. Interactive SQL console with auto completion. API endpoint for programmatic integrtion. Scheduled SQL reporting via email, file, or web delivery. JDBC and ODBC drivers.

Only send the data that is changed to the destination. Reduce computational overhead and eliminate data latency. Change data capture is a highly effective method of synchronizing data. Reduce compute costs by streaming only the data which has changed. Log-based replication allows you to replicate data in the real-time and without impacting source database performance. Install the end-toend solution in just minutes. No pipeline maintenance is required. Let your data teams focus on more valuable projects. Artie can be set up in a few easy steps. Artie will backfill historical data and stream continuously new changes to the final tables as they occur. Artie ensures high reliability and data consistency. Artie uses offsets to continue where it left off in the event of a failure. This helps maintain high data integrity without the need for re-syncs.

We help organizations complete their cyber resilience strategy with best-in class data security, data recovery, and data freedom. Your backups are the best defense if your data is compromised, or if it is destroyed. Cybercriminals are now focusing on your backups, posing a serious threat to your business. Data disruptions are inevitable. We help you move forward, not just bounce back. Recover faster and become radically more resilient. Own, control, backup and recover all of your data, anywhere. Detect malicious backup activities that could affect your ability to recover after an attack or achieve regulatory compliance. Veeam Data Platform is available in three comprehensive enterprise editions. Our most powerful premium option provides the complete and secure protection that only Veeam Data Platform can provide.

Lyftrondata can help you build a governed lake, data warehouse or migrate from your old database to a modern cloud-based data warehouse. Lyftrondata makes it easy to create and manage all your data workloads from one platform. This includes automatically building your warehouse and pipeline. It's easy to share the data with ANSI SQL, BI/ML and analyze it instantly. You can increase the productivity of your data professionals while reducing your time to value. All data sets can be defined, categorized, and found in one place. These data sets can be shared with experts without coding and used to drive data-driven insights. This data sharing capability is ideal for companies who want to store their data once and share it with others. You can define a dataset, apply SQL transformations, or simply migrate your SQL data processing logic into any cloud data warehouse.

Equalum's continuous data integr & streaming platform is unique in that it natively supports real time, batch, and ETL use case under one platform. There is no coding required. You can move to real time with a fully orchestrated, drag and drop, no-code UI. You will experience rapid deployment, powerful transformations and scalable streaming data pipes in minutes. Multi-modal, robust and scalable CDC enables real-time streaming and data replicating. No matter what source, the CDC is tuned for best-in class performance. The power of open-source big dataset frameworks without the hassle. Equalum leverages the Scalability of Open-Source Data Frameworks like Apache Spark and Kafka in its Platform engine to dramatically improve streaming and batch data processing performance. This best-in-class infrastructure allows organizations to increase data volumes, improve performance, and minimize system impact.