Alternatives to PipelineDB

Tiger Data reimagines PostgreSQL for the modern era — powering everything from IoT and fintech to AI and Web3. As the creator of TimescaleDB, it brings native time-series, event, and analytical capabilities to the world’s most trusted database engine. Through Tiger Cloud, developers gain access to a fully managed, elastic infrastructure with auto-scaling, high availability, and point-in-time recovery. The platform introduces core innovations like Forks (copy-on-write storage branches for CI/CD and testing), Memory (durable agent context and recall), and Search (hybrid BM25 and vector retrieval). Combined with hypertables, continuous aggregates, and materialized views, Tiger delivers the speed of specialized analytical systems without sacrificing SQL simplicity. Teams use Tiger Data to unify real-time and historical analytics, build AI-driven workflows, and streamline data management at scale. It integrates seamlessly with the entire PostgreSQL ecosystem, supporting APIs, CLIs, and modern development frameworks. With over 20,000 GitHub stars and a thriving developer community, Tiger Data stands as the evolution of PostgreSQL for the intelligent data age.

TimescaleDB brings the power of PostgreSQL to time-series and event data at any scale. It extends standard Postgres with features like automatic time-based partitioning (hypertables), incremental materialized views, and native time-series functions, making it the most efficient way to handle analytical workloads. Designed for use cases like IoT, DevOps monitoring, crypto markets, and real-time analytics, it ingests millions of rows per second while maintaining sub-second query speeds. Developers can run complex time-based queries, joins, and aggregations using familiar SQL syntax — no new language or database model required. Built-in compression ensures long-term data retention without high storage costs, and automated data management handles rollups and retention policies effortlessly. Its hybrid storage architecture merges row-based performance for live data with columnar efficiency for historical queries. Open-source and 100% PostgreSQL compatible, TimescaleDB integrates with Kafka, S3, and the entire Postgres ecosystem. Trusted by global enterprises, it delivers the performance of a purpose-built time-series system without sacrificing Postgres reliability or flexibility.

Blueflood is an advanced distributed metric processing system designed for high throughput and low latency, operating as a multi-tenant solution that supports Rackspace Metrics. It is actively utilized by both the Rackspace Monitoring team and the Rackspace public cloud team to effectively manage and store metrics produced by their infrastructure. Beyond its application within Rackspace, Blueflood also sees extensive use in large-scale deployments documented in community resources. The data collected through Blueflood is versatile, allowing users to create dashboards, generate reports, visualize data through graphs, or engage in any activities that involve analyzing time-series data. With a primary emphasis on near-real-time processing, data can be queried just milliseconds after it is ingested, ensuring timely access to information. Users send their metrics to the ingestion service and retrieve them from the Query service, while the system efficiently handles background rollups through offline batch processing, thus facilitating quick responses for queries covering extended time frames. This architecture not only enhances performance but also ensures that users can rely on rapid access to their critical metrics for effective decision-making.

QuasarDB, the core of Quasar's intelligence, is an advanced, distributed, column-oriented database management system specifically engineered for high-performance timeseries data handling, enabling real-time processing for massive petascale applications. It boasts up to 20 times less disk space requirement, making it exceptionally efficient. The unmatched ingestion and compression features of QuasarDB allow for up to 10,000 times quicker feature extraction. This database can perform real-time feature extraction directly from raw data via an integrated map/reduce query engine, a sophisticated aggregation engine that utilizes SIMD capabilities of contemporary CPUs, and stochastic indexes that consume minimal disk storage. Its ultra-efficient resource utilization, ability to integrate with object storage solutions like S3, innovative compression methods, and reasonable pricing structure make it the most economical timeseries solution available. Furthermore, QuasarDB is versatile enough to operate seamlessly across various platforms, from 32-bit ARM devices to high-performance Intel servers, accommodating both Edge Computing environments and traditional cloud or on-premises deployments. Its scalability and efficiency make it an ideal choice for businesses aiming to harness the full potential of their data in real-time.

SurrealDB provides a versatile and flexible platform tailored for businesses. With a comprehensive array of advanced database solutions, tools, and services, SurrealDB enables teams to uncover creative solutions through products specifically designed to align with their needs. The query language utilized by SurrealDB bears a resemblance to traditional SQL, yet it is capable of handling time-series and interconnected graph data with ease. SurrealQL is a sophisticated query language that incorporates programming language features, empowering developers and data analysts to engage with SurrealDB in a manner that suits their preferences. Users can connect directly to SurrealDB from any client device, allowing them to execute SurrealQL queries straight within web browsers, which ensures that data access remains secure and permissions are upheld. The platform boasts highly efficient WebSocket connections that facilitate seamless bi-directional communication for queries, responses, and real-time notifications, enhancing the overall user experience. This ability to maintain constant connectivity and responsiveness sets SurrealDB apart in the realm of database solutions.

Detecting anomalies in time series data is critical for the daily functions of numerous organizations. The Timeseries Insights API Preview enables you to extract real-time insights from your time-series datasets effectively. It provides comprehensive information necessary for interpreting your API query results, including details on anomaly occurrences, projected value ranges, and segments of analyzed events. This capability allows for the real-time streaming of data, facilitating the identification of anomalies as they occur. With over 15 years of innovation in security through widely-used consumer applications like Gmail and Search, Google Cloud offers a robust end-to-end infrastructure and a layered security approach. The Timeseries Insights API is seamlessly integrated with other Google Cloud Storage services, ensuring a uniform access method across various storage solutions. You can analyze trends and anomalies across multiple event dimensions and manage datasets that encompass tens of billions of events. Additionally, the system is capable of executing thousands of queries every second, making it a powerful tool for real-time data analysis and decision-making. Such capabilities are invaluable for businesses aiming to enhance their operational efficiency and responsiveness.

Anticipate issues before they arise by utilizing an Azure AI anomaly detection service. This service allows for the seamless integration of time-series anomaly detection features into applications, enabling users to quickly pinpoint problems. The AI Anomaly Detector processes various types of time-series data and intelligently chooses the most effective anomaly detection algorithm tailored to your specific dataset, ensuring superior accuracy. It can identify sudden spikes, drops, deviations from established patterns, and changes in trends using both univariate and multivariate APIs. Users can personalize the service to recognize different levels of anomalies based on their needs. The anomaly detection service can be deployed flexibly, whether in the cloud or at the intelligent edge. With a robust inference engine, the service evaluates your time-series dataset and automatically determines the ideal detection algorithm, enhancing accuracy for your unique context. This automatic detection process removes the necessity for labeled training data, enabling you to save valuable time and concentrate on addressing issues promptly as they arise. By leveraging advanced technology, organizations can enhance their operational efficiency and maintain a proactive approach to problem-solving.

OpenTSDB comprises a Time Series Daemon (TSD) along with a suite of command line tools. Users primarily engage with OpenTSDB by operating one or more independent TSDs, as there is no centralized master or shared state, allowing for the scalability to run multiple TSDs as necessary to meet varying loads. Each TSD utilizes HBase, an open-source database, or the hosted Google Bigtable service for the storage and retrieval of time-series data. The schema designed for the data is highly efficient, enabling rapid aggregations of similar time series while minimizing storage requirements. Users interact with the TSD without needing direct access to the underlying storage system. Communication with the TSD can be accomplished through a straightforward telnet-style protocol, an HTTP API, or a user-friendly built-in graphical interface. To begin utilizing OpenTSDB, the initial task is to send time series data to the TSDs, and there are various tools available to facilitate the import of data from different sources into OpenTSDB. Overall, OpenTSDB's design emphasizes flexibility and efficiency for time series data management.

Nixtla is a cutting-edge platform designed for time-series forecasting and anomaly detection, centered on its innovative model, TimeGPT, which is recognized as the first generative AI foundation model tailored for time-series information. This model has been trained on an extensive dataset comprising over 100 billion data points across various sectors, including retail, energy, finance, IoT, healthcare, weather, and web traffic, enabling it to make precise zero-shot predictions for numerous applications. Users can effortlessly generate forecasts or identify anomalies in their data with just a few lines of code through the provided Python SDK, even when dealing with irregular or sparse time series, and without the need to construct or train models from the ground up. TimeGPT also boasts advanced capabilities such as accommodating external factors (like events and pricing), enabling simultaneous forecasting of multiple time series, employing custom loss functions, conducting cross-validation, providing prediction intervals, and allowing fine-tuning on specific datasets. This versatility makes Nixtla an invaluable tool for professionals seeking to enhance their time-series analysis and forecasting accuracy.

Machbase is a leading time-series database designed for real-time storage and analysis of vast amounts of sensor data from various facilities. It stands out as the only database management system (DBMS) capable of processing and analyzing large datasets at remarkable speeds, showcasing its impressive capabilities. Experience the extraordinary processing speeds that Machbase offers! This innovative product allows for immediate handling, storage, and analysis of sensor information. It achieves rapid storage and querying of sensor data by integrating the DBMS directly into Edge devices. Additionally, it provides exceptional performance in data storage and extraction when operating on a single server. With the ability to configure multi-node clusters, Machbase offers enhanced availability and scalability. Furthermore, it serves as a comprehensive management solution for Edge computing, addressing device management, connectivity, and data handling needs effectively. In a fast-paced data-driven world, Machbase proves to be an essential tool for industries relying on real-time sensor data analysis.

Historian is a software solution designed for the comprehensive and ongoing tracking of various metrics. It utilizes an InfluxDB time-series database to facilitate long-term monitoring applications seamlessly. You can oversee data related to vibration, temperature, inclination, strain, pressure, and more, using either a self-hosted setup or a completely managed cloud service. The system is compatible with the standard OPC UA protocol, ensuring efficient data access and enabling integration with DewesoftX data acquisition software, SCADAs, ERPs, or any other OPC UA-enabled clients. The data is securely housed within a cutting-edge open-source InfluxDB database, which is crafted by InfluxData and written in Go, allowing for rapid and high-availability storage and retrieval of time series data relevant to operational monitoring, application metrics, IoT sensor data, and real-time analytics. Users can choose to install the Historian service either locally on the measurement unit or within their local intranet, or opt for a fully managed cloud service tailored to their needs. This flexibility makes Historian a versatile choice for organizations looking to enhance their data monitoring capabilities.

Enhance your metrics and alerting capabilities using a top-tier open-source monitoring tool. Prometheus inherently organizes all data as time series, which consist of sequences of timestamped values associated with the same metric and a specific set of labeled dimensions. In addition to the stored time series, Prometheus has the capability to create temporary derived time series based on query outcomes. The tool features a powerful query language known as PromQL (Prometheus Query Language), allowing users to select and aggregate time series data in real time. The output from an expression can be displayed as a graph, viewed in tabular format through Prometheus’s expression browser, or accessed by external systems through the HTTP API. Configuration of Prometheus is achieved through a combination of command-line flags and a configuration file, where the flags are used to set immutable system parameters like storage locations and retention limits for both disk and memory. This dual method of configuration ensures a flexible and tailored monitoring setup that can adapt to various user needs. For those interested in exploring this robust tool, further details can be found at: https://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Fsourceforge.net%2Fprojects%2Fprometheus.mirror%2F

At the core of our innovative approach lies in-data computing, a cutting-edge technology aimed at efficiently processing substantial volumes of data. Our leading product, BigObject, is a prime example of this technology; it is a time series database purposefully created to enable rapid storage and management of vast data sets. Leveraging in-data computing, BigObject has the capability to swiftly and continuously address diverse data streams without interruption. This time series database excels in both high-speed storage and data analysis, showcasing remarkable performance alongside robust complex query functionalities. By transitioning from a traditional relational data structure to a time-series model, it harnesses in-data computing to enhance overall database efficiency. The foundation of our technology is an abstract model, wherein all data resides within an infinite and persistent memory space, facilitating seamless storage and computation. This unique architecture not only optimizes performance but also paves the way for future advancements in data processing capabilities.

Analyzing time-series data is crucial for the daily functions of numerous businesses. Common applications involve assessing consumer foot traffic and conversion rates for retailers, identifying anomalies in data, discovering real-time correlations within sensor information, and producing accurate recommendations. With the Cloud Inference API Alpha, businesses can derive real-time insights from their time-series datasets that they input. This tool provides comprehensive details about API query results, including the various groups of events analyzed, the total number of event groups, and the baseline probability associated with each event returned. It enables real-time streaming of data, facilitating the computation of correlations as events occur. Leveraging Google Cloud’s robust infrastructure and a comprehensive security strategy that has been fine-tuned over 15 years through various consumer applications ensures reliability. The Cloud Inference API is seamlessly integrated with Google Cloud Storage services, enhancing its functionality and user experience. This integration allows for more efficient data handling and analysis, positioning businesses to make informed decisions faster.

The ITTIA DB suite brings together advanced features for time series, real-time data streaming, and analytics tailored for embedded systems, ultimately streamlining development processes while minimizing expenses. With ITTIA DB IoT, users can access a compact embedded database designed for real-time operations on resource-limited 32-bit microcontrollers (MCUs), while ITTIA DB SQL serves as a robust time-series embedded database that operates efficiently on both single and multicore microprocessors (MPUs). These ITTIA DB offerings empower devices to effectively monitor, process, and retain real-time data. Additionally, the products are specifically engineered to meet the needs of Electronic Control Units (ECUs) within the automotive sector. To ensure data security, ITTIA DB incorporates comprehensive protection mechanisms against unauthorized access, leveraging encryption, authentication, and the DB SEAL feature. Furthermore, ITTIA SDL adheres to the standards set forth by IEC/ISO 62443, reinforcing its commitment to safety. By integrating ITTIA DB, developers can seamlessly collect, process, and enhance incoming real-time data streams through a specialized SDK designed for edge devices, allowing for efficient searching, filtering, joining, and aggregating of data right at the edge. This comprehensive approach not only optimizes performance but also supports the growing demand for real-time data handling in today's technology landscape.

ZeusDB represents a cutting-edge, high-efficiency data platform tailored to meet the complexities of contemporary analytics, machine learning, real-time data insights, and hybrid data management needs. This innovative system seamlessly integrates vector, structured, and time-series data within a single engine, empowering applications such as recommendation systems, semantic searches, retrieval-augmented generation workflows, live dashboards, and ML model deployment to function from one centralized store. With its ultra-low latency querying capabilities and real-time analytics, ZeusDB removes the necessity for disparate databases or caching solutions. Additionally, developers and data engineers have the flexibility to enhance its functionality using Rust or Python, with deployment options available in on-premises, hybrid, or cloud environments while adhering to GitOps/CI-CD practices and incorporating built-in observability. Its robust features, including native vector indexing (such as HNSW), metadata filtering, and advanced query semantics, facilitate similarity searching, hybrid retrieval processes, and swift application development cycles. Overall, ZeusDB is poised to revolutionize how organizations approach data management and analytics, making it an indispensable tool in the modern data landscape.

AVEVA Historian streamlines the complex demands of data reporting and analysis. This powerful tool can be utilized to oversee either a single process or an entire facility, effectively storing data on-site while also consolidating information at a corporate level. By preventing the existence of various versions of plant operational data, it enhances productivity, minimizes errors, and cuts down on operating expenses. In contrast to traditional relational databases that struggle in production settings, Historian is specifically designed to manage time-series data alongside alarm and event data seamlessly. Its innovative “history block” technology records plant data significantly quicker than standard database systems while consuming only a small fraction of the typical storage space. Furthermore, Historian upholds the data integrity necessary to meet the highest standards of requirement. It adeptly handles low bandwidth data communications, accommodates delayed information, and processes data from systems that may have inconsistent clock settings. This ensures that high-resolution data is captured accurately every single time, contributing to reliable operational insights and decision-making.

The Environmental Management system we offer is exceptionally suited for Ministries focused on Agriculture, Natural Resources, and the Environment. It adeptly handles Geospatial Information, time-series data, licenses, permits, applications, and ensures the integrity of quality data pertaining to water, land, and air. The EDAMS Government Environmental Management system is particularly beneficial for these sectors, as it streamlines the management of essential data. By facilitating integration at various levels—database, business process, and transaction—it effectively prevents data duplication and supports demand management. Additionally, EDAMS products feature an embedded GIS while also providing seamless access to ESRI ArcGIS, Quantum GIS (QGIS), and SuperMap GIS. Furthermore, the modular design of the EDAMS system allows for scalable implementation, making it adaptable to the evolving needs and capacity growth of the organization. This flexibility ensures that as the organization's requirements expand, the system can grow alongside them, maintaining efficiency and effectiveness.

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.

Introducing a robust cross-platform columnar database designed for high-performance historical time-series data, which includes: - A compute engine optimized for in-memory operations - A streaming processor that functions in real time - A powerful query and programming language known as q Kdb+ drives the kdb Insights portfolio and KDB.AI, offering advanced time-focused data analysis and generative AI functionalities to many of the world's top enterprises. Recognized for its unparalleled speed, kdb+ has been independently benchmarked* as the leading in-memory columnar analytics database, providing exceptional benefits for organizations confronting complex data challenges. This innovative solution significantly enhances decision-making capabilities, enabling businesses to adeptly respond to the ever-evolving data landscape. By leveraging kdb+, companies can gain deeper insights that lead to more informed strategies.

Apache Doris serves as a cutting-edge data warehouse tailored for real-time analytics, enabling exceptionally rapid analysis of data at scale. It features both push-based micro-batch and pull-based streaming data ingestion that occurs within a second, alongside a storage engine capable of real-time upserts, appends, and pre-aggregation. With its columnar storage architecture, MPP design, cost-based query optimization, and vectorized execution engine, it is optimized for handling high-concurrency and high-throughput queries efficiently. Moreover, it allows for federated querying across various data lakes, including Hive, Iceberg, and Hudi, as well as relational databases such as MySQL and PostgreSQL. Doris supports complex data types like Array, Map, and JSON, and includes a Variant data type that facilitates automatic inference for JSON structures, along with advanced text search capabilities through NGram bloomfilters and inverted indexes. Its distributed architecture ensures linear scalability and incorporates workload isolation and tiered storage to enhance resource management. Additionally, it accommodates both shared-nothing clusters and the separation of storage from compute resources, providing flexibility in deployment and management.

Narrator represents a groundbreaking method for handling data, providing answers to any inquiry without the need for creating new models or modifying SQL. While other data companies focus on assisting with the development and management of models and tables, we completely eliminate that requirement. Narrator simplifies the process by modeling each business concept once, utilizing any source data in your warehouse, all organized within a single time-series table. Constructing each model takes only five minutes, and once established, it remains valid for answering new questions without necessitating additional adjustments. This means that new inquiries or metrics can be addressed without any further data preparation. Every business concept can be queried, analyzed, visualized, and interconnected seamlessly, all without SQL, leveraging any data available in your warehouse. At Narrator, we prioritize security, integrating it into every step of our development process rather than treating it as an afterthought. By fostering an environment where your team feels encouraged to pose an endless array of follow-up questions, we cultivate a culture that stimulates curiosity and empowers the business to explore, adapt, and innovate continuously. This approach not only enhances data accessibility but also drives collaborative growth across all departments.

The RemoteAware™ GenAI Analytics Platform for IoT revolutionizes the interpretation of intricate sensor and device data streams by delivering clear and actionable insights through cutting-edge generative AI techniques. This platform is capable of ingesting and normalizing massive volumes of diverse IoT data sourced from edge gateways, cloud APIs, or remote assets, utilizing scalable AI pipelines to identify anomalies, predict equipment malfunctions, and produce prescriptive recommendations articulated in straightforward narratives. With a cohesive, web-based dashboard, users benefit from immediate access to crucial performance metrics, customizable alerts, and notifications based on set thresholds, along with the ability to dynamically drill down for time-series analysis. Additionally, the platform's generative summary reports distill extensive datasets into succinct operational briefs, while its capabilities in root-cause analysis and what-if simulations support proactive maintenance and optimal resource distribution. Ultimately, this platform empowers organizations to make data-driven decisions efficiently and effectively.

DataLux is an innovative platform designed for effective data management and analytics, specifically created to tackle various data-related issues while facilitating real-time decision-making. Equipped with user-friendly plug-and-play adaptors, it enables the aggregation of extensive data collections and offers the capability to collect and visualize insights instantaneously. Utilize the data lake to anticipate and drive new innovations, while ensuring that data is stored in a manner conducive to modeling. The platform allows for the development of portable applications by leveraging containerization, whether in a public cloud, private cloud, or on-premise environment. It seamlessly integrates diverse time-series market data and inferred information, including stock exchange tick data, market policy actions, relevant cross-industry news, and alternative datasets, to derive causal insights regarding stock markets and macroeconomic factors. By providing valuable insights, DataLux empowers businesses to shape their decisions and foster product innovations effectively. Additionally, it supports interdisciplinary A/B testing throughout the product development lifecycle, from initial ideation to final decision-making, ensuring a comprehensive approach to enhancing design and engineering processes.

Sider Scan is an incredibly efficient tool specifically designed for software developers to swiftly detect and monitor issues related to code duplication. It integrates seamlessly with platforms such as GitLab CI/CD, GitHub Actions, Jenkins, and CircleCI®, and offers installation through a Docker image. The tool facilitates easy sharing of analysis results among team members and conducts continuous, rapid assessments that operate in the background. Users also benefit from dedicated support via email and phone, which enhances their overall experience. By providing comprehensive analyses of duplicate code, Sider Scan significantly improves long-term code quality and maintenance practices. It is engineered to work in tandem with other analysis tools, enabling development teams to create more refined code while supporting a continuous delivery workflow. The tool identifies duplicate code segments within a project and organizes them into groups. For every pair of duplicates, a diff library is generated, and pattern analyses are launched to uncover any potential issues. This process is known as the 'pattern' analysis method. Furthermore, to enable time-series analysis, it is crucial that the scans are executed at regular intervals, ensuring consistent monitoring over time. By encouraging routine evaluations, Sider Scan empowers teams to maintain high coding standards and proactively address duplications.

We developed a comprehensive business intelligence platform aimed at empowering business users to derive insights from their data. CUBOT Business Intelligence serves as a unified solution that consolidates data and offers valuable intelligence to those who require it. The platform supports the import of ETL-processed tables for the creation of robust data models. Within CUBOT Designer, analysts have the capability to link transaction tables with pertinent master tables, thereby bridging information across various silos. CUBOT is specifically engineered to enhance the utilization of organizational data effectively. Setting up scheduling actions and configuring performance metrics is straightforward, which ultimately aids in tracking and fostering business growth. Analysts can also define specific variables for deeper analysis. The CUBOT Configurator handles data aggregations, computes measures, categorizes dimension values, and much more. By informing CUBOT about any geographic or time-series data you possess, you will unlock additional analytical potential down the line. Furthermore, users can examine figures across different data attributes and drill down to the most detailed levels of granularity, enabling a thorough exploration of their data landscape. This holistic approach not only streamlines data management but also enhances decision-making capabilities across the organization.

KDB.AI serves as a robust knowledge-centric vector database and search engine, enabling developers to create applications that are scalable, dependable, and operate in real-time by offering sophisticated search, recommendation, and personalization features tailored for AI needs. Vector databases represent an innovative approach to data management, particularly suited for generative AI, IoT, and time-series applications, highlighting their significance, distinctive characteristics, operational mechanisms, emerging use cases, and guidance on how to begin utilizing them effectively. Additionally, understanding these elements can help organizations harness the full potential of modern data solutions.

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.

Hydra is an innovative, open-source solution that transforms Postgres into a column-oriented database, enabling instant queries over billions of rows without necessitating any alterations to your existing code. By employing advanced techniques such as parallelization and vectorization for aggregate functions like COUNT, SUM, and AVG, Hydra significantly enhances the speed and efficiency of data processing in Postgres. In just five minutes, you can set up Hydra without modifying your syntax, tools, data model, or extensions, ensuring a hassle-free integration. For those seeking a fully managed experience, Hydra Cloud offers seamless operations and optimal performance. Various industries can benefit from tailored analytics by leveraging powerful Postgres extensions and custom functions, allowing you to take charge of your data needs. Designed with user requirements in mind, Hydra stands out as the fastest Postgres solution available for analytical tasks, making it an essential tool for data-driven decision-making. With features like columnar storage, query parallelization, and vectorization, Hydra is poised to redefine the analytics landscape.

It's time to move on from outdated clipboards and the monotonous transcription of essential plant performance metrics. The FactoryTalk® Historian software efficiently gathers operational process data from various sources at incredible speeds. This software provides an unparalleled degree of supervisory control, performance tracking, and quality assurance, with the capability to scale from individual machines to the entire enterprise. Recording time-series data at this speed would be unfeasible, even for the most energetic record keeper on the plant floor. The dashboards offered by FactoryTalk Historian simplify this process. Additionally, the enhanced ability to forecast trends using dependable data will boost productivity to new heights. With FactoryTalk Historian Site Edition (SE), no data across your plant and enterprise can remain concealed. Its redundancy and high availability guarantee uninterrupted access to vital plant information, ensuring your operations run smoothly without downtime. This transition to a more advanced system not only streamlines processes but also empowers your team to focus on strategic improvements.

Cloudera Data Warehouse is a cloud-native, self-service analytics platform designed to empower IT departments to quickly provide query functionalities to BI analysts, allowing users to transition from no query capabilities to active querying within minutes. It accommodates all forms of data, including structured, semi-structured, unstructured, real-time, and batch data, and it scales efficiently from gigabytes to petabytes based on demand. This solution is seamlessly integrated with various services, including streaming, data engineering, and AI, while maintaining a cohesive framework for security, governance, and metadata across private, public, or hybrid cloud environments. Each virtual warehouse, whether a data warehouse or mart, is autonomously configured and optimized, ensuring that different workloads remain independent and do not disrupt one another. Cloudera utilizes a range of open-source engines, such as Hive, Impala, Kudu, and Druid, along with tools like Hue, to facilitate diverse analytical tasks, which span from creating dashboards and conducting operational analytics to engaging in research and exploration of extensive event or time-series data. This comprehensive approach not only enhances data accessibility but also significantly improves the efficiency of data analysis across various sectors.

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.

The Cisco® ASR 920 Series Aggregation Services Router (ASR) stands as a versatile access platform engineered for the economical provision of both wireline and wireless solutions. These routers, characterized by their ability to withstand extreme temperatures, high throughput, compact design, and low energy consumption, are specifically tailored for mobile backhaul and business uses. Within a small form factor, the Cisco ASR 920 Router delivers an extensive and adaptable array of Layer 2 VPN (L2VPN) and Layer 3 VPN (L3VPN) functionalities. Additionally, it empowers service providers to implement Multiprotocol Label Switching (MPLS)-based VPN services directly from the access layer. This series features essential Carrier Ethernet capabilities that streamline network management, making it suitable for premium offerings with robust service-level agreements (SLAs). Moreover, the Cisco ASR 920 Series incorporates an optional "pay-as-you-grow" model, enhancing its flexibility and cost efficiency while accommodating future expansions. With such features, it is well-positioned to meet the evolving needs of modern service providers.

With eagle.io, transform your data into actionable insights eagle.io is a tool for system integrators and consultants. It helps you transform time-series data into actionable intelligence. You can instantly acquire data from any text file or data logger, transform it automatically using processing and logic, get alerts for important events, and share your access with clients. Some of the largest companies in the world trust eagle.io to help them understand their natural resources and environmental conditions in real-time.

dbForge Studio for PostgreSQL is a GUI tool for database development and management. The IDE for PostgreSQL allows users to create, develop, and execute queries, edit and adjust the code to your requirements in a convenient and user-friendly interface. SQL Development Save your time and improve code quality when creating and editing queries. Explorer Navigate through the object tree and find any PostgreSQL object you are interested in. Data Editor Users can manage the settings of tables. Data Export and Import Supports 10+ widely used data formats, a number of advanced options, and templates for recurring scenarios. Query Profiler It allows you to identify queries with the longest duration, analyze whether they can be optimized, and compare the results before and after optimization. Data and Schema Compare The tool allows to compare and synchronize PostgreSQL database data and schemas, and synchronize tables between PostgreSQL and Redshift databases. Data Generator Designed to generate huge amounts of realistic test data and accurately visualize the generated data in real time. Pivot Table Visual Pivot Table Designer, advanced filtering, visual data presentation in a graph.

The ASR 900 Series serves as a versatile modular aggregation platform that provides an economical solution for integrating mobile, residential, and business services. Featuring redundancy, compact design, energy efficiency, and high scalability in routers, it is equipped with essential functionalities, making it ideal for small-scale aggregation and remote point-of-presence (POP) applications. This platform significantly enhances the broadband experience for customers. It facilitates broadband aggregation across various services, including voice, video, data, and mobility, accommodating thousands of subscribers while ensuring quality of service (QoS) that scales to numerous queues per device. As a pre-aggregation solution for mobile backhaul, the series can effectively consolidate cell sites and utilize MPLS for transporting RAN backhaul traffic. Additionally, it provides the essential timing services needed in modern converged access networks. The series includes built-in support for multiple interfaces and is capable of serving as a clock source for network synchronization with GPS and other systems, ensuring reliable operations across diverse network environments. This level of integration and capability makes the ASR 900 Series an exceptional choice for organizations looking to optimize their connectivity solutions.

AI CERTs provides certification programs focused on specific roles within the fields of artificial intelligence and blockchain, ensuring that AI education is attainable for individuals regardless of their technical background. Their extensive range of learning paths and credentials caters to various interests and career goals. One notable certification is the “AI+ Developer,” which immerses participants in essential topics such as Python programming, data processing, machine learning, deep learning, natural language processing, computer vision, reinforcement learning, time-series analysis, model interpretability, and cloud deployment. This program includes practical projects, laboratory work, and an online proctored examination to assess learners' skills effectively. Additionally, AI CERTs offers flexible learning options, allowing participants to choose between self-paced or instructor-guided formats, thus accommodating different schedules. With a mission to bridge the AI skills gap, AI CERTs ensures that its curricula are continually updated by industry professionals and academic experts, reflecting the latest trends and demands in the field. As such, learners can expect relevant and practical knowledge that aligns with the evolving landscape of artificial intelligence.

Fujitsu Enterprise Postgres stands out as a highly dependable and powerful relational database tailored for businesses that demand exceptional query performance and consistent availability. Built on the acclaimed open-source platform PostgreSQL, it incorporates additional enterprise-level features that enhance both security and efficiency. The installation and management of Fujitsu Enterprise Postgres are handled by skilled database professionals from Fujitsu, who are also available to support the transition of data from your current database systems. Given its foundation in PostgreSQL, FEP offers remarkable compatibility with various systems and applications. Furthermore, the user-friendly, streamlined graphical interface significantly enhances the experience for database administrators carrying out essential functions like executing queries, conducting scans, and performing backups, ultimately making data management and reporting more user-friendly and effective. This comprehensive approach ensures that organizations can leverage their data to its fullest potential.

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 independently examines intricate data sets, uncovering solutions to business challenges in unexpected areas. It allows for the effortless incorporation of external data into your investigations, enhancing your understanding of the key factors influencing outcomes and providing a comprehensive view of your business landscape. By enabling users to engage with data and insights in natural language, it fosters a stronger collaboration between analytics and business leaders, pushing analytics initiatives beyond mere experimentation. To ensure that the advantages gained from analytics remain relevant, it promotes a continuous cycle of inputs and outputs that adapt to changing circumstances. As the world evolves, so too must your insights. With the ability to automatically connect various data types, from time-series to geo-spatial, in their original detailed form without any coding required, you can gain valuable perspectives effortlessly. Moreover, by integrating a well-curated repository of global knowledge, including maps, demographic data, and Wikipedia, or by tapping into a network of external data partners, you can significantly enrich your analytical capabilities. This holistic approach ensures that organizations are well-equipped to navigate the complexities of modern business environments.

AlloyDB is a fully managed database service that is compatible with PostgreSQL, designed to meet the needs of the most demanding enterprise workloads. By merging Google's advancements with PostgreSQL, AlloyDB offers enhanced performance, scalability, and reliability. It ensures complete compatibility with PostgreSQL, allowing for both flexibility and genuine workload portability. For transactional workloads, its performance is up to four times faster than standard PostgreSQL, while it provides real-time analytical insights that are up to 100 times quicker. Additionally, AlloyDB AI supports the development of various generative AI applications. For versatile deployment, AlloyDB Omni is available as a downloadable version that can function in any environment. You can easily scale your resources and enjoy predictable performance, backed by a high availability service level agreement of 99.99%, which includes maintenance for the most intense enterprise demands. The automated systems, enhanced with machine learning capabilities, streamline management tasks such as database patching, backups, scaling, and replication, freeing users to focus on other priorities and innovations. This comprehensive approach makes AlloyDB a robust choice for organizations looking to optimize their database solutions.

Swift. Elegant. Innovative. A contemporary high-performance Russian platform designed to address the challenges of describing, visualizing, managing, and analyzing processes along with their respective elements. Drawing upon extensive expertise in IT and telecommunications, this platform integrates the finest concepts from established OSS/BSS/NGOSS (Frameworx) frameworks while employing cutting-edge HTML/AJAX, SQL/noSQL, and TimeSeries technologies. It seamlessly interacts with diverse systems, such as the Internet of Things, and boasts robust business intelligence features. This software, developed in Russia, stands at a global standard. Effective visualization of the IT landscape is crucial for the seamless functioning of any organization. Additionally, maintaining oversight of critical technical metrics ensures consistent performance. Continuous monitoring of the availability of communication channels, equipment, computers, servers, and their components is essential for operational reliability. Overall, this platform lays the foundation for informed decision-making and efficient resource management.

Graphite is a robust monitoring solution suitable for both budget-friendly hardware and cloud environments, making it an attractive choice for various teams. Organizations utilize Graphite to monitor the performance metrics of their websites, applications, business services, and server networks effectively. This tool initiated a new wave of monitoring technologies, simplifying the processes of storing, retrieving, sharing, and visualizing time-series data. Originally developed in 2006 by Chris Davis while working at Orbitz as a side project, Graphite evolved into their core monitoring solution over time. In 2008, Orbitz made the decision to release Graphite under the open-source Apache 2.0 license, broadening its accessibility. Many prominent companies have since integrated Graphite into their production environments to oversee their e-commerce operations and strategize for future growth. The data collected is processed through the Carbon service, which subsequently stores it in Whisper databases for long-term retention and analysis, ensuring that key performance indicators are always available for review. This comprehensive approach to monitoring empowers organizations to make data-driven decisions while scaling their operations.

R is a comprehensive environment and programming language tailored for statistical analysis and graphical representation. As a part of the GNU project, it shares similarities with the S language, which was originally designed by John Chambers and his team at Bell Laboratories, now known as Lucent Technologies. Essentially, R serves as an alternative implementation of S, and while there are notable distinctions between the two, a significant amount of S code can be executed in R without modification. This versatile language offers a broad spectrum of statistical methods, including both linear and nonlinear modeling, classical statistical tests, time-series analytics, classification, and clustering, among others, and it boasts a high level of extensibility. The S language is frequently utilized in research focused on statistical methodologies, and R presents an Open Source avenue for engaging in this field. Moreover, one of R's key advantages lies in its capability to generate high-quality publication-ready graphics, facilitating the inclusion of mathematical symbols and formulas as needed, which enhances its usability for researchers and analysts alike. Ultimately, R continues to be a powerful tool for those seeking to explore and visualize data effectively.

EMS SQL Management Studio for PostgreSQL offers a comprehensive suite for the administration and development of PostgreSQL databases. This all-in-one workbench equips users with essential tools for a variety of tasks, including managing databases and their components, designing databases, performing migrations, and building queries, as well as facilitating data import and export, comparing databases, and executing service tasks. Users can efficiently access and manage all databases and their associated objects through a streamlined console that features an intuitive interface. The software allows for the creation and modification of server and database objects, while enabling users to set required properties and examine detailed information using advanced visual editors. Additionally, with the Compare databases feature, users can automatically transfer structural changes from development environments to production databases. Users can also generate ER diagrams for newly constructed databases, facilitating expedited deployment. Setting up database maintenance tasks is made simple with advanced options, allowing SQL Studio to execute them on a regular schedule, ensuring optimal performance and reliability. This tool not only enhances productivity but also streamlines database management processes significantly.