Alternatives to BigObject
Compare BigObject alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to BigObject in 2026. Compare features, ratings, user reviews, pricing, and more from BigObject competitors and alternatives in order to make an informed decision for your business.
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Google Cloud Platform
Google
Google Cloud is a cloud-based service that allows you to create anything from simple websites to complex applications for businesses of all sizes. New customers get $300 in free credits to run, test, and deploy workloads. All customers can use 25+ products for free, up to monthly usage limits. Use Google's core infrastructure, data analytics & machine learning. Secure and fully featured for all enterprises. Tap into big data to find answers faster and build better products. Grow from prototype to production to planet-scale, without having to think about capacity, reliability or performance. From virtual machines with proven price/performance advantages to a fully managed app development platform. Scalable, resilient, high performance object storage and databases for your applications. State-of-the-art software-defined networking products on Google’s private fiber network. Fully managed data warehousing, batch and stream processing, data exploration, Hadoop/Spark, and messaging. -
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Teradata VantageCloud
Teradata
Teradata VantageCloud: The complete cloud analytics and data platform for AI. Teradata VantageCloud is an enterprise-grade, cloud-native data and analytics platform that unifies data management, advanced analytics, and AI/ML capabilities in a single environment. Designed for scalability and flexibility, VantageCloud supports multi-cloud and hybrid deployments, enabling organizations to manage structured and semi-structured data across AWS, Azure, Google Cloud, and on-premises systems. It offers full ANSI SQL support, integrates with open-source tools like Python and R, and provides built-in governance for secure, trusted AI. VantageCloud empowers users to run complex queries, build data pipelines, and operationalize machine learning models—all while maintaining interoperability with modern data ecosystems. -
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Google Cloud BigQuery
Google
BigQuery is a serverless, multicloud data warehouse that simplifies the process of working with all types of data so you can focus on getting valuable business insights quickly. At the core of Google’s data cloud, BigQuery allows you to simplify data integration, cost effectively and securely scale analytics, share rich data experiences with built-in business intelligence, and train and deploy ML models with a simple SQL interface, helping to make your organization’s operations more data-driven. Gemini in BigQuery offers AI-driven tools for assistance and collaboration, such as code suggestions, visual data preparation, and smart recommendations designed to boost efficiency and reduce costs. BigQuery delivers an integrated platform featuring SQL, a notebook, and a natural language-based canvas interface, catering to data professionals with varying coding expertise. This unified workspace streamlines the entire analytics process. -
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RaimaDB
Raima
RaimaDB is an embedded time series database for IoT and Edge devices that can run in-memory. It is an extremely powerful, lightweight and secure RDBMS. Field tested by over 20 000 developers worldwide and has more than 25 000 000 deployments. RaimaDB is a high-performance, cross-platform embedded database designed for mission-critical applications, particularly in the Internet of Things (IoT) and edge computing markets. It offers a small footprint, making it suitable for resource-constrained environments, and supports both in-memory and persistent storage configurations. RaimaDB provides developers with multiple data modeling options, including traditional relational models and direct relationships through network model sets. It ensures data integrity with ACID-compliant transactions and supports various indexing methods such as B+Tree, Hash Table, R-Tree, and AVL-Tree. -
5
StarTree
StarTree
StarTree, powered by Apache Pinot™, is a fully managed real-time analytics platform built for customer-facing applications that demand instant insights on the freshest data. Unlike traditional data warehouses or OLTP databases—optimized for back-office reporting or transactions—StarTree is engineered for real-time OLAP at true scale, meaning: - Data Volume: query performance sustained at petabyte scale - Ingest Rates: millions of events per second, continuously indexed for freshness - Concurrency: thousands to millions of simultaneous users served with sub-second latency With StarTree, businesses deliver always-fresh insights at interactive speed, enabling applications that personalize, monitor, and act in real time.Starting Price: Free -
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Snowflake
Snowflake
Snowflake is a comprehensive AI Data Cloud platform designed to eliminate data silos and simplify data architectures, enabling organizations to get more value from their data. The platform offers interoperable storage that provides near-infinite scale and access to diverse data sources, both inside and outside Snowflake. Its elastic compute engine delivers high performance for any number of users, workloads, and data volumes with seamless scalability. Snowflake’s Cortex AI accelerates enterprise AI by providing secure access to leading large language models (LLMs) and data chat services. The platform’s cloud services automate complex resource management, ensuring reliability and cost efficiency. Trusted by over 11,000 global customers across industries, Snowflake helps businesses collaborate on data, build data applications, and maintain a competitive edge.Starting Price: $2 compute/month -
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SAP HANA Cloud
SAP
SAP HANA Cloud is a fully managed in-memory cloud database as a service (DBaaS). As the cloud-based data foundation for SAP Business Technology Platform, it integrates data from across the enterprise, enabling faster decisions based on live data. Build data solutions with modern architectures and gain business-ready insights in real-time. As the data foundation for SAP Business Technology Platform, the SAP HANA Cloud database offers the power of SAP HANA in the cloud. Scale to your needs, process business data of all types, and perform advanced analytics on live transactions without tuning for fast, improved decision-making. Connect to distributed data with native integration, develop applications and tools across clouds and on-premise, and store volatile data. Tap business-ready information by creating one source of truth and enable security, privacy, and anonymization with enterprise reliability. -
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HEAVY.AI
HEAVY.AI
HEAVY.AI is the pioneer in accelerated analytics. The HEAVY.AI platform is used in business and government to find insights in data beyond the limits of mainstream analytics tools. Harnessing the massive parallelism of modern CPU and GPU hardware, the platform is available in the cloud and on-premise. HEAVY.AI originated from research at Harvard and MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). Expand beyond the limitations of traditional BI and GIS by leveraging the full power of modern GPU and CPU hardware so you can extract decision-quality information from your massive datasets without lag. Unify and explore your largest geospatial and time-series datasets to get the complete picture of the what, when, and where. Combine interactive visual analytics, hardware-accelerated SQL, and an advanced analytics & data science framework to find opportunity and risk hidden in your enterprise when you need to most. -
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Apache Druid
Druid
Apache Druid is an open source distributed data store. Druid’s core design combines ideas from data warehouses, timeseries databases, and search systems to create a high performance real-time analytics database for a broad range of use cases. Druid merges key characteristics of each of the 3 systems into its ingestion layer, storage format, querying layer, and core architecture. Druid stores and compresses each column individually, and only needs to read the ones needed for a particular query, which supports fast scans, rankings, and groupBys. Druid creates inverted indexes for string values for fast search and filter. Out-of-the-box connectors for Apache Kafka, HDFS, AWS S3, stream processors, and more. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures. -
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OpenText Analytics Database is a high-performance, scalable analytics platform that enables organizations to analyze massive data sets quickly and cost-effectively. It supports real-time analytics and in-database machine learning to deliver actionable business insights. The platform can be deployed flexibly across hybrid, multi-cloud, and on-premises environments to optimize infrastructure and reduce total cost of ownership. Its massively parallel processing (MPP) architecture handles complex queries efficiently, regardless of data size. OpenText Analytics Database also features compatibility with data lakehouse architectures, supporting formats like Parquet and ORC. With built-in machine learning and broad language support, it empowers users from SQL experts to Python developers to derive predictive insights.
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QuestDB
QuestDB
QuestDB is a relational column-oriented database designed for time series and event data. It uses SQL with extensions for time series to assist with real-time analytics. These pages cover core concepts of QuestDB, including setup steps, usage guides, and reference documentation for syntax, APIs and configuration. This section describes the architecture of QuestDB, how it stores and queries data, and introduces features and capabilities unique to the system. Designated timestamp is a core feature that enables time-oriented language capabilities and partitioning. Symbol type makes storing and retrieving repetitive strings efficient. Storage model describes how QuestDB stores records and partitions within tables. Indexes can be used for faster read access on specific columns. Partitions can be used for significant performance benefits on calculations and queries. SQL extensions allow performant time series analysis with a concise syntax. -
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Oxla
Oxla
Purpose-built for compute, memory, and storage efficiency, Oxla is a self-hosted data warehouse optimized for large-scale, low-latency analytics with robust time-series support. Cloud data warehouses aren’t for everyone. At scale, long-term cloud compute costs outweigh short-term infrastructure savings, and regulated industries require full control over data beyond VPC and BYOC deployments. Oxla outperforms both legacy and cloud warehouses through efficiency, enabling scale for growing datasets with predictable costs, on-prem or in any cloud. Easily deploy, run, and maintain Oxla with Docker and YAML to power diverse workloads in a single, self-hosted data warehouse.Starting Price: $50 per CPU core / monthly -
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Exasol
Exasol
With an in-memory, columnar database and MPP architecture, you can query billions of rows in seconds. Queries are distributed across all nodes in a cluster, providing linear scalability for more users and advanced analytics. MPP, in-memory, and columnar storage add up to the fastest database built for data analytics. With SaaS, cloud, on premises and hybrid deployment options you can analyze data wherever it lives. Automatic query tuning reduces maintenance and overhead. Seamless integrations and performance efficiency gets you more power at a fraction of normal infrastructure costs. Smart, in-memory query processing allowed this social networking company to boost performance, processing 10B data sets a year. A single data repository and speed engine to accelerate critical analytics, delivering improved patient outcome and bottom line. -
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Trino
Trino
Trino is a query engine that runs at ludicrous speed. Fast-distributed SQL query engine for big data analytics that helps you explore your data universe. Trino is a highly parallel and distributed query engine, that is built from the ground up for efficient, low-latency analytics. The largest organizations in the world use Trino to query exabyte-scale data lakes and massive data warehouses alike. Supports diverse use cases, ad-hoc analytics at interactive speeds, massive multi-hour batch queries, and high-volume apps that perform sub-second queries. Trino is an ANSI SQL-compliant query engine, that works with BI tools such as R, Tableau, Power BI, Superset, and many others. You can natively query data in Hadoop, S3, Cassandra, MySQL, and many others, without the need for complex, slow, and error-prone processes for copying the data. Access data from multiple systems within a single query.Starting Price: Free -
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TimescaleDB
Tiger Data
TimescaleDB is the leading time-series database built on PostgreSQL, designed to handle massive volumes of real-time data efficiently. It enables organizations to store, analyze, and query time-series data — such as IoT sensor data, financial transactions, or event logs — using standard SQL. With hypertables, TimescaleDB automatically partitions data by time and ID for fast ingestion and predictable query performance. Its compression engine reduces storage costs by up to 95%, while continuous aggregates make real-time dashboards instantly responsive. Fully compatible with PostgreSQL, it integrates seamlessly with existing tools and applications. TimescaleDB combines the simplicity of Postgres with the scalability and speed of a specialized analytical system. -
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SAP HANA
SAP
SAP HANA in-memory database is for transactional and analytical workloads with any data type — on a single data copy. It breaks down the transactional and analytical silos in organizations, for quick decision-making, on premise and in the cloud. Innovate without boundaries on a database management system, where you can develop intelligent and live solutions for quick decision-making on a single data copy. And with advanced analytics, you can support next-generation transactional processing. Build data solutions with cloud-native scalability, speed, and performance. With the SAP HANA Cloud database, you can gain trusted, business-ready information from a single solution, while enabling security, privacy, and anonymization with proven enterprise reliability. An intelligent enterprise runs on insight from data – and more than ever, this insight must be delivered in real time. -
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Machbase
Machbase
Machbase, a time-series database that stores and analyzes a lot of sensor data from various facilities in real time, is the only DBMS solution that can process and analyze big data at high speed. Experience the amazing speed of Machbase! It is the most innovative product that enables real-time processing, storage, and analysis of sensor data. High speed sensor data storage and inquiry for sensor data by embedding DBMS in an Edge devices. Best data storage and extraction performance by DBMS running in a single server. Configuring Multi-node cluster with the advantages of availability and scalability. Total management solution of Edge computing for device, connectivity and data. -
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Alibaba Cloud TSDB
Alibaba
Time Series Database (TSDB) supports high-speed data reading and writing. It offers high compression ratios for cost-efficient data storage. This service also supports visualization of precision reduction, interpolation, multi-metric aggregate computing, and query results. The TSDB service reduces storage costs and improves the efficiency of data writing, query, and analysis. This enables you to handle large amounts of data points and collect data more frequently. This service has been widely applied to systems in different industries, such as IoT monitoring systems, enterprise energy management systems (EMSs), production security monitoring systems, and power supply monitoring systems. Optimizes database architectures and algorithms. TSDB can read or write millions of data points within seconds. Applies an efficient compression algorithm to reduce the size of each data point to 2 bytes, saving more than 90% in storage costs. -
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Apache Doris
The Apache Software Foundation
Apache Doris is a modern data warehouse for real-time analytics. It delivers lightning-fast analytics on real-time data at scale. Push-based micro-batch and pull-based streaming data ingestion within a second. Storage engine with real-time upsert, append and pre-aggregation. Optimize for high-concurrency and high-throughput queries with columnar storage engine, MPP architecture, cost based query optimizer, vectorized execution engine. Federated querying of data lakes such as Hive, Iceberg and Hudi, and databases such as MySQL and PostgreSQL. Compound data types such as Array, Map and JSON. Variant data type to support auto data type inference of JSON data. NGram bloomfilter and inverted index for text searches. Distributed design for linear scalability. Workload isolation and tiered storage for efficient resource management. Supports shared-nothing clusters as well as separation of storage and compute.Starting Price: Free -
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eXtremeDB
McObject
How is platform independent eXtremeDB different? - Hybrid data storage. Unlike other IMDS, eXtremeDB can be all-in-memory, all-persistent, or have a mix of in-memory tables and persistent tables - Active Replication Fabric™ is unique to eXtremeDB, offering bidirectional replication, multi-tier replication (e.g. edge-to-gateway-to-gateway-to-cloud), compression to maximize limited bandwidth networks and more - Row & Columnar Flexibility for Time Series Data supports database designs that combine row-based and column-based layouts, in order to best leverage the CPU cache speed - Embedded and Client/Server. Fast, flexible eXtremeDB is data management wherever you need it, and can be deployed as an embedded database system, and/or as a client/server database system -A hard real-time deterministic option in eXtremeDB/rt Designed for use in resource-constrained, mission-critical embedded systems. Found in everything from routers to satellites to trains to stock markets worldwide -
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Riak KV
Riak
At Riak, we are distributed systems experts and we work with Application teams to overcome these distributed system challenges. Riak’s Riak® is a distributed NoSQL database that delivers unmatched Resiliency beyond typical “high availability” offerings. Innovative technology to ensure data accuracy and never lose a write. Massive scale on commodity hardware. Common code foundation with true multi-model support. Riak® provides all this, while still focused on ease of operations. Chose Riak® KV flexible key-value data model for web scale profile and session management, real-time big data, catalog, content management, customer 360, digital messaging, and more use cases. Chose Riak® TS for IoT and time series use cases. When seconds of latency can cost thousands of dollars and an outage millions, the call for scalable, highly available databases that are easy to operationalize is resoundingly clear. Riak performs as promised and keeps the lights on.Starting Price: $0 -
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IBM Db2 Big SQL
IBM
A hybrid SQL-on-Hadoop engine delivering advanced, security-rich data query across enterprise big data sources, including Hadoop, object storage and data warehouses. IBM Db2 Big SQL is an enterprise-grade, hybrid ANSI-compliant SQL-on-Hadoop engine, delivering massively parallel processing (MPP) and advanced data query. Db2 Big SQL offers a single database connection or query for disparate sources such as Hadoop HDFS and WebHDFS, RDMS, NoSQL databases, and object stores. Benefit from low latency, high performance, data security, SQL compatibility, and federation capabilities to do ad hoc and complex queries. Db2 Big SQL is now available in 2 variations. It can be integrated with Cloudera Data Platform, or accessed as a cloud-native service on the IBM Cloud Pak® for Data platform. Access and analyze data and perform queries on batch and real-time data across sources, like Hadoop, object stores and data warehouses. -
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Dewesoft Historian
DEWESoft
Historian is a database software service for long-term and permanent monitoring. It provides storage in an InfluxDB time-series database for long-term and permanent monitoring applications. Monitor your vibration, temperature, inclination, strain, pressure, and other data with a self-hosted or fully cloud-managed service. Standard OPC UA protocol is supported for data access and integration into our DewesoftX data acquisition software or SCADAs, ERPs, or any other OPC UA clients. Data is stored in a state-of-the-art open-source InfluxDB database. InfluxDB is an open-source time-series database developed by InfluxData. It is written in Go and optimized for fast, high-availability storage and retrieval of time series data in fields such as operations monitoring, application metrics, Internet of Things sensor data, and real-time analytics. Historian service can either be installed locally on the measurement unit, or your local intranet, or we can provide a fully cloud-managed service. -
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IndexedDB
Mozilla
IndexedDB is a low-level API for client-side storage of significant amounts of structured data, including files/blobs. This API uses indexes to enable high-performance searches of this data. While web storage is useful for storing smaller amounts of data, it is less useful for storing larger amounts of structured data. IndexedDB provides a solution. IndexedDB is a transactional database system, like an SQL-based Relational Database Management System (RDBMS). However, unlike SQL-based RDBMSes, which use fixed-column tables, IndexedDB is a JavaScript-based object-oriented database. IndexedDB lets you store and retrieve objects that are indexed with a key; any objects supported by the structured clone algorithm can be stored. You need to specify the database schema, open a connection to your database, and then retrieve and update data within a series of transactions. Like most web storage solutions, IndexedDB follows the same-origin policy.Starting Price: Free -
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Greenplum
Greenplum Database
Greenplum Database® is an advanced, fully featured, open source data warehouse. It provides powerful and rapid analytics on petabyte scale data volumes. Uniquely geared toward big data analytics, Greenplum Database is powered by the world’s most advanced cost-based query optimizer delivering high analytical query performance on large data volumes. Greenplum Database® project is released under the Apache 2 license. We want to thank all our current community contributors and are interested in all new potential contributions. For the Greenplum Database community no contribution is too small, we encourage all types of contributions. An open-source massively parallel data platform for analytics, machine learning and AI. Rapidly create and deploy models for complex applications in cybersecurity, predictive maintenance, risk management, fraud detection, and many other areas. Experience the fully featured, integrated, open source analytics platform. -
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Databricks Data Intelligence Platform
Databricks
The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker. -
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Warp 10
SenX
Warp 10 is a modular open source platform that collects, stores, and analyzes data from sensors. 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 is both a time series database and a powerful analytics environment, allowing 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 analysis environment can be implemented within a large ecosystem of software components such as Spark, Kafka Streams, Hadoop, Jupyter, Zeppelin and many more. It can also access data stored in many existing solutions, relational or NoSQL databases, search engines and S3 type object storage system. -
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Infobright DB
IgniteTech
Infobright DB is a high-performance enterprise database leveraging a columnar storage engine to enable business analysts to dissect data efficiently and more quickly obtain reports. InfoBright DB can be deployed on-premise or in the cloud. Store & analyze big data for interactive business intelligence and complex queries. Improve query performance, reduce storage cost and increase overall efficiency in business analytics and reporting. Easily store up to several hundred TB of data — traditionally not achievable with conventional databases. Run big data applications and eliminate indexing and partitioning — with zero administrative overhead. With the volumes of machine data exploding, IgniteTech’s Infobright DB is specifically designed to achieve high performance for large volumes of machine-generated data. Manage a complex ad hoc analytic environments without the database administration required by other products. -
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InfiniteGraph
Objectivity
InfiniteGraph is a massively scalable graph database specifically designed to excel at high-speed ingest of massive volumes of data (billions of nodes and edges per hour) while supporting complex queries. InfiniteGraph can seamlessly distribute connected graph data across a global enterprise. InfiniteGraph is a schema-based graph database that supports highly complex data models. It also has an advanced schema evolution capability that allows you to modify and evolve the schema of an existing database. InfiniteGraph’s Placement Management Capability allows you to optimize the placement of data items resulting in tremendous performance improvements in both query and ingest. InfiniteGraph has client-side caching which caches frequently used node and edges. InfiniteGraph's DO query language enables complex "beyond graph" queries not supported by other query languages. -
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IBM Storage Scale is software-defined file and object storage that enables organizations to build a global data platform for artificial intelligence (AI), high-performance computing (HPC), advanced analytics, and other demanding workloads. Unlike traditional applications that work with structured data, today’s performance-intensive AI and analytics workloads operate on unstructured data, such as documents, audio, images, videos, and other objects. IBM Storage Scale software provides global data abstraction services that seamlessly connect multiple data sources across multiple locations, including non-IBM storage environments. It’s based on a massively parallel file system and can be deployed on multiple hardware platforms including x86, IBM Power, IBM zSystem mainframes, ARM-based POSIX client, virtual machines, and Kubernetes.Starting Price: $19.10 per terabyte
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kdb Insights
KX
kdb Insights is a cloud-native, high-performance analytics platform designed for real-time analysis of both streaming and historical data. It enables intelligent decision-making regardless of data volume or velocity, offering unmatched price and performance, and delivering analytics up to 100 times faster at 10% of the cost compared to other solutions. The platform supports interactive data visualization through real-time dashboards, facilitating instantaneous insights and decision-making. It also integrates machine learning models to predict, cluster, detect patterns, and score structured data, enhancing AI capabilities on time-series datasets. With supreme scalability, kdb Insights handles extensive real-time and historical data, proven at volumes of up to 110 terabytes per day. Its quick setup and simple data intake accelerate time-to-value, while native support for q, SQL, and Python, along with compatibility with other languages via RESTful APIs. -
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ScyllaDB
ScyllaDB
ScyllaDB is the database for data-intensive apps that require high performance and low latency. It enables teams to harness the ever-increasing computing power of modern infrastructures – eliminating barriers to scale as data grows. Unlike any other database, ScyllaDB is a distributed NoSQL database fully compatible with Apache Cassandra and Amazon DynamoDB, yet is built with deep architectural advancements that enable exceptional end-user experiences at radically lower costs. Over 400 game-changing companies like Disney+ Hotstar, Expedia, FireEye, Discord, Zillow, Starbucks, Comcast, and Samsung use ScyllaDB for their toughest database challenges. ScyllaDB is available as free open source software, a fully-supported enterprise product, and a fully managed database-as-a-service (DBaaS) on multiple cloud providers. -
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Anomaly detection in time series data is essential for the day-to-day operation of many companies. With Timeseries Insights API Preview, you can gather insights in real-time from your time-series datasets. Get everything you need to understand your API query results, such as anomaly events, forecasted range of values, and slices of events that were examined. Stream data in real-time, making it possible to detect anomalies while they are happening. Rely on Google Cloud's end-to-end infrastructure and defense-in-depth approach to security that's been innovated for over 15 years through consumer apps like Gmail and Search. At its core, Timeseries Insights API is fully integrated with other Google Cloud Storage services, providing you with a consistent method of access across storage products. Detect trends and anomalies with multiple event dimensions. Handle datasets consisting of tens of billions of events. Run thousands of queries per second.
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Google Cloud Inference API
Google
Time-series analysis is essential for the day-to-day operation of many companies. Most popular use cases include analyzing foot traffic and conversion for retailers, detecting data anomalies, identifying correlations in real-time over sensor data, or generating high-quality recommendations. With Cloud Inference API Alpha, you can gather insights in real-time from your typed time-series datasets. Get everything you need to understand your API queries results, such as groups of events that were examined, the number of groups of events, and the background probability of each returned event. Stream data in real-time, making it possible to compute correlations for real-time events. Rely on Google Cloud’s end-to-end infrastructure and defense-in-depth approach to security that’s been innovated on for over 15 years through consumer apps. At its core, Cloud Inference API is fully integrated with other Google Cloud Storage services. -
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Hazelcast
Hazelcast
In-Memory Computing Platform. The digital world is different. Microseconds matter. That's why the world's largest organizations rely on us to power their most time-sensitive applications at scale. New data-enabled applications can deliver transformative business power – if they meet today’s requirement of immediacy. Hazelcast solutions complement virtually any database to deliver results that are significantly faster than a traditional system of record. Hazelcast’s distributed architecture provides redundancy for continuous cluster up-time and always available data to serve the most demanding applications. Capacity grows elastically with demand, without compromising performance or availability. The fastest in-memory data grid, combined with third-generation high-speed event processing, delivered through the cloud. -
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Azure Synapse Analytics
Microsoft
Azure Synapse is Azure SQL Data Warehouse evolved. Azure Synapse is a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless or provisioned resources—at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs. -
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OpenTSDB
OpenTSDB
OpenTSDB consists of a Time Series Daemon (TSD) as well as set of command line utilities. Interaction with OpenTSDB is primarily achieved by running one or more of the independent TSDs. There is no master, no shared state so you can run as many TSDs as required to handle any load you throw at it. Each TSD uses the open source database HBase or hosted Google Bigtable service to store and retrieve time-series data. The data schema is highly optimized for fast aggregations of similar time series to minimize storage space. Users of the TSD never need to access the underlying store directly. You can communicate with the TSD via a simple telnet-style protocol, an HTTP API or a simple built-in GUI. The first step in using OpenTSDB is to send time series data to the TSDs. A number of tools exist to pull data from various sources into OpenTSDB. -
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SelectDB
SelectDB
SelectDB is a modern data warehouse based on Apache Doris, which supports rapid query analysis on large-scale real-time data. From Clickhouse to Apache Doris, to achieve the separation of the lake warehouse and upgrade to the lake warehouse. The fast-hand OLAP system carries nearly 1 billion query requests every day to provide data services for multiple scenes. Due to the problems of storage redundancy, resource seizure, complicated governance, and difficulty in querying and adjustment, the original lake warehouse separation architecture was decided to introduce Apache Doris lake warehouse, combined with Doris's materialized view rewriting ability and automated services, to achieve high-performance data query and flexible data governance. Write real-time data in seconds, and synchronize flow data from databases and data streams. Data storage engine for real-time update, real-time addition, and real-time pre-polymerization.Starting Price: $0.22 per hour -
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Quantum DXi
Quantum
High-performance, scalable backup appliances for data protection, cyber and disaster recovery. The requirements for protecting data across the Enterprise continue to get more complex. Our customers are managing massive data growth across databases, virtual environments, and unstructured data sets. They need to meet or exceed service level agreements (SLAs) to the business, both recovery time objective (RTO) and recovery point objective (RPO), with budgets that aren’t growing nearly as fast as storage requirements. And data protection itself has become more demanding, with requirements to protect against operational issues, protect data across sites, provide solutions for disaster recovery and against ransomware and other forms of cyber attacks. The DXi® series backup appliances provide a uniquely powerful solution for meeting your backup needs, SLA requirements, and cyber recovery efforts. -
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Tiger Data
Tiger Data
Tiger Data is the creator of TimescaleDB, the world’s leading PostgreSQL-based time-series and analytics database. It provides a modern data platform purpose-built for developers, devices, and AI agents. Designed to extend PostgreSQL beyond traditional limits, Tiger Data offers built-in primitives for time-series data, search, materialization, and scale. With features like auto-partitioning, hybrid storage, and compression, it helps teams query billions of rows in milliseconds while cutting infrastructure costs. Tiger Cloud delivers these capabilities as a fully managed, elastic environment with enterprise-grade security and compliance. Trusted by innovators like Cloudflare, Toyota, Polymarket, and Hugging Face, Tiger Data powers real-time analytics, observability, and intelligent automation across industries.Starting Price: $30 per month -
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Katana Graph
Katana Graph
Simplified distributed computing drives huge graph-analytics performance gains without the need for major infrastructure. Strengthen insights by bringing in a wider array of data to be standardized and plotted onto the graph. Pairing innovations in graph and deep learning have meant efficiencies that allow timely insights on the world’s biggest graphs. From comprehensive fraud detection in real time to 360° views of the customer, Katana Graph empowers Financial Services organizations to unlock the tremendous potential of graph analytics and AI at scale. Drawing from advances in high-performance parallel computing (HPC), Katana Graph’s intelligence platform assesses risk and draws customer insights from the largest data sources using high-speed analytics and AI that goes well beyond what is possible using other graph technologies. -
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SensorCloud
LORD Corporation
SensorCloud is a unique sensor data storage, visualization and remote management platform that leverages powerful Cloud computing technologies to provide excellent data scalability, rapid visualization, and user programmable analysis. SensorCloud's core features include FastGraph, MathEngine®, LiveConnect, and the OpenData API. SensorCloud allows you to easily create dashboards to visualize all of your data. Dashboards can be as simple as a single Timeseries Graph widget, or advanced with Radial Gauges, Text Charts, Linear Gauges, FFTs, Statistics, etc. Since SensorCloud allows you to upload as much data as you want, and LORD's sensors can sample at very high rates, it was important to be able to quickly visualize massive amounts of data. We struggled to find any application that could handle even a few gigabytes of data, so we started from the ground up with our own unique algorithm.Starting Price: $35 per month -
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AVEVA Historian
AVEVA
AVEVA Historian simplifies the most demanding data reporting and analysis requirements. Historian can be deployed to monitor a single process or an entire facility, storing data locally and aggregating data at the corporate level. Eliminating multiple versions of plant operating data in this way increases productivity, reduces errors, and lowers operating costs. Unlike conventional relational databases that are not well-suited to production environments, Historian handles time-series data, as well as alarm and event data. Unique “history block” technology captures plant data hundreds of times faster than a standard database system and utilizes a fraction of the conventional storage space. Historian will maintain the data integrity needed for the most demanding requirements. AVEVA Historian manages low bandwidth data communications, late-coming information, and even data from systems with mismatched system clocks. Ensuring high-resolution data is captured accurately every time. -
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Azure Data Lake Storage
Microsoft
Eliminate data silos with a single storage platform. Optimize costs with tiered storage and policy management. Authenticate data using Azure Active Directory (Azure AD) and role-based access control (RBAC). And help protect data with security features like encryption at rest and advanced threat protection. Highly secure with flexible mechanisms for protection across data access, encryption, and network-level control. Single storage platform for ingestion, processing, and visualization that supports the most common analytics frameworks. Cost optimization via independent scaling of storage and compute, lifecycle policy management, and object-level tiering. Meet any capacity requirements and manage data with ease, with the Azure global infrastructure. Run large-scale analytics queries at consistently high performance. -
45
Dell PowerEdge C Series
Dell Technologies
Dell PowerEdge C-Series servers are a family of high-density, scale-out servers designed for use in hyper-scale and high-performance computing (HPC) environments. These servers are optimized for workloads that demand significant computational power, large storage capacity, and efficient cooling. The C-Series servers offer a modular and flexible design, allowing for customization and configuration to meet the specific needs of various applications, such as big data analytics, artificial intelligence (AI), machine learning (ML), and cloud computing. Key features of the PowerEdge C-Series include support for the latest Intel or AMD processors, high memory capacity, a variety of storage options including NVMe drives, and efficient thermal management. With their combination of performance, scalability, and versatility, Dell PowerEdge C-Series servers provide organizations with the tools to handle data-intensive and compute-heavy workloads in today's dynamic IT landscape. -
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Altair Panopticon
Altair
Altair Panopticon Streaming Analytics lets business users and engineers — the people closest to the action — build, modify, and deploy sophisticated event processing and data visualization applications with a drag-and-drop interface. They can connect to virtually any data source, including real-time streaming feeds and time-series databases, develop complex stream processing programs, and design visual user interfaces that give them the perspectives they need to make insightful, fully-informed decisions based on massive amounts of fast-changing data.Starting Price: $1000.00/one-time/user -
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Databend
Databend
Databend is a modern, cloud-native data warehouse built to deliver high-performance, cost-efficient analytics for large-scale data processing. It is designed with an elastic architecture that scales dynamically to meet the demands of different workloads, ensuring efficient resource utilization and lower operational costs. Written in Rust, Databend offers exceptional performance through features like vectorized query execution and columnar storage, which optimize data retrieval and processing speeds. Its cloud-first design enables seamless integration with cloud platforms, and it emphasizes reliability, data consistency, and fault tolerance. Databend is an open source solution, making it a flexible and accessible choice for data teams looking to handle big data analytics in the cloud.Starting Price: Free -
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Amazon Aurora
Amazon
Amazon Aurora is a MySQL and PostgreSQL-compatible relational database built for the cloud, that combines the performance and availability of traditional enterprise databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora is up to five times faster than standard MySQL databases and three times faster than standard PostgreSQL databases. It provides the security, availability, and reliability of commercial databases at 1/10th the cost. Amazon Aurora is fully managed by Amazon Relational Database Service (RDS), which automates time-consuming administration tasks like hardware provisioning, database setup, patching, and backups. Amazon Aurora features a distributed, fault-tolerant, self-healing storage system that auto-scales up to 64TB per database instance. It delivers high performance and availability with up to 15 low-latency read replicas, point-in-time recovery, continuous backup to Amazon S3, and replication across three Availability Zones.Starting Price: $0.02 per month -
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The KX Delta Platform is a high-performance, enterprise-grade data management system designed to capture, store, and analyze real-time and historical data. Built atop kdb+, the world's leading time-series database, it offers flexible configuration parameters to support key deployment needs such as redundancy, load balancing, and fault tolerance, ensuring high availability. Robust security features, including LDAP authorization, data encryption, and permission controls, ensure strict compliance with data sensitivity and security standards. The platform enables users to visualize data in various formats through a dashboard builder, interactive data playback, and auto-generated reports, efficiently supporting program management. It facilitates the management, manipulation, and exploration of massive real-time and historical datasets, processing at exceptional speeds to support mission-critical applications.
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Edge Intelligence
Edge Intelligence
Start benefiting your business within minutes of installation. Learn how our system works. It's the fastest, easiest way to analyze vast amounts of geographically distributed data. A new approach to analytics. Overcome the architectural constraints associated with traditional big data warehouses, database design and edge computing architectures. Understand details within the platform that allow for centralized command & control, automated software installation & orchestration and geographically distributed data input & storage.
