Best Data Management Software - Page 100
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Compare the Top Data Management Software as of June 2026 - Page 100
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IBM ProtecTIER
IBM
ProtecTIER® is a disk-based data storage system. It uses data deduplication technology to store data to disk arrays. With Feature Code 9022, the ProtecTIER Virtual Tape Library (VTL) service emulates traditional automated tape libraries. With Feature Code 9024, a stand-alone TS7650G can be configured as FSI. Several software applications run on various TS7650G components and configurations. The ProtecTIER Manager workstation is a customer-supplied workstation that runs the ProtecTIER Manager software. The ProtecTIER Manager software provides the management GUI interface to the TS7650G. The ProtecTIER VTL service emulates traditional tape libraries. By emulating tape libraries, ProtecTIER VTL provides the capability to transition to disk backup without having to replace your entire backup environment. Your existing backup application can access virtual robots to move virtual cartridges between virtual slots and drives. -
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Piiano
Piiano
Emerging privacy policies often conflict with the architectures of enterprise systems that were not designed with sensitive data protection in mind. Piiano pioneers data privacy engineering for the cloud, offering the industry’s first personal data protection and management platform to transform how enterprises build privacy-forward architecture and operationalize privacy practices. Piiano provides a pre-built, developer-friendly infrastructure to dramatically ease the adoption or acceleration of enterprise privacy engineering and help developers build privacy-by-design architecture. This engineering infrastructure safeguards sensitive customers’ data, preempts breaches, and helps enterprises comply with privacy regulations as they evolve. The Vault is a dedicated, protected database for centralizing sensitive information that developers can install into enterprise VPC (Virtual Private Cloud). This ensures that the vault–and everything in it–is only accessible to the enterprise. -
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SAP BW/4HANA
SAP
SAP BW/4HANA is a packaged data warehouse based on SAP HANA. As the on-premise data warehouse layer of SAP’s Business Technology Platform, it allows you to consolidate data across the enterprise to get a consistent, agreed-upon view of your data. Streamline processes and support innovations with a single source for real-time insights. Based on SAP HANA, our next-generation data warehouse solution can help you capitalize on the full value of all your data from SAP applications or third-party solutions, as well as unstructured, geospatial, or Hadoop-based. Transform data practices to gain the efficiency and agility to deploy live insights at scale, both on premise or in the cloud. Drive digitization across all lines of business with a Big Data warehouse, while leveraging digital business platform solutions from SAP. -
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Captain Data
Captain Data
Captain Data manages your most ambitious sales & marketing workflows by extracting, enriching and automating data from 30+ sources on the web. The automation platform that doesn't let your marketing, sales and operations teams down when you need to scale your most advanced sales & marketing workflows. Choose a single app for simple automation or pick multiple apps for more complex workflows. Choose from hundreds of automations. From simple automations to advanced workflows that include multiple applications, Captain Data got you covered. You’ll love Captain Data with its beautiful interface that allows even non-tech people to use it without any issue. Captain Data complies with application limits, whether it's the number of actions you can run on your social media account or API rate limiting. That way, your automations always work like a charm and you don’t have to worry about it again.Starting Price: $99 per month -
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Cauliflower
Cauliflower
Whether for a service or a product, whether a snapshot or monitoring over time - Cauliflower processes feedback and comments from various application areas. Using Artificial Intelligence (AI), Cauliflower identifies the most important topics, their relevance, evaluation and relationships. In-house developed machine learning models for the extraction of content and evaluation of sentiment. Intuitive dashboards with filter options and drill-downs. Use included variables for language, weight, ID, time or location. Define your own filter variables in the dropdown. Cauliflower translates the results into a uniform language if required. Define a company-wide language about customer feedback instead of reading it sporadically and quoting individual opinions. -
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Apache Kudu
The Apache Software Foundation
A Kudu cluster stores tables that look just like tables you're used to from relational (SQL) databases. A table can be as simple as a binary key and value, or as complex as a few hundred different strongly-typed attributes. Just like SQL, every table has a primary key made up of one or more columns. This might be a single column like a unique user identifier, or a compound key such as a (host, metric, timestamp) tuple for a machine time-series database. Rows can be efficiently read, updated, or deleted by their primary key. Kudu's simple data model makes it a breeze to port legacy applications or build new ones, no need to worry about how to encode your data into binary blobs or make sense of a huge database full of hard-to-interpret JSON. Tables are self-describing, so you can use standard tools like SQL engines or Spark to analyze your data. Kudu's APIs are designed to be easy to use. -
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Apache Parquet
The Apache Software Foundation
We created Parquet to make the advantages of compressed, efficient columnar data representation available to any project in the Hadoop ecosystem. Parquet is built from the ground up with complex nested data structures in mind, and uses the record shredding and assembly algorithm described in the Dremel paper. We believe this approach is superior to simple flattening of nested namespaces. Parquet is built to support very efficient compression and encoding schemes. Multiple projects have demonstrated the performance impact of applying the right compression and encoding scheme to the data. Parquet allows compression schemes to be specified on a per-column level, and is future-proofed to allow adding more encodings as they are invented and implemented. Parquet is built to be used by anyone. The Hadoop ecosystem is rich with data processing frameworks, and we are not interested in playing favorites. -
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Hypertable
Hypertable
Hypertable delivers scalable database capacity at maximum performance to speed up your big data application and reduce your hardware footprint. Hypertable delivers maximum efficiency and superior performance over the competition which translates into major cost savings. A proven scalable design that powers hundreds of Google services. All the benefits of open source with a strong and thriving community. C++ implementation for optimum performance. 24/7/365 support for your business-critical big data application. Unparalleled access to Hypertable brain power by the employer of all core Hypertable developers. Hypertable was designed for the express purpose of solving the scalability problem, a problem that is not handled well by a traditional RDBMS. Hypertable is based on a design developed by Google to meet their scalability requirements and solves the scale problem better than any of the other NoSQL solutions out there. -
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InfiniDB
Database of Databases
InfiniDB is a column-store DBMS optimized for OLAP workloads. It has a distributed architecture to support Massive Paralllel Processing (MPP). It uses MySQL as its front-end such that users familiar with MySQL can quickly migrate to InfiniDB. Due to this fact, users can connect to InfiniDB using any MySQL connector. InfiniDB applies MVCC to do concurrency control. It uses term System Change Number (SCN) to indicate a version of the system. In its Block Resolution Manager (BRM), it utilizes three structures, version buffer, version substitution structure, and version buffer block manager, to manage multiple versions. InfiniDB applies deadlock detection to resolve conflicts. InfiniDB uses MySQL as its front-end and supports all MySQL syntaxes, including foreign keys. InfiniDB is a columnar DBMS. For each column, InfiniDB applies range partitioning and stores the minimum and maximum value of each partition in a small structure called extent map. -
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qikkDB
qikkDB
QikkDB is a GPU accelerated columnar database, delivering stellar performance for complex polygon operations and big data analytics. When you count your data in billions and want to see real-time results you need qikkDB. We support Windows and Linux operating systems. We use Google Tests as the testing framework. There are hundreds of unit tests and tens of integration tests in the project. For development on Windows, Microsoft Visual Studio 2019 is recommended, and its dependencies are CUDA version 10.2 minimal, CMake 3.15 or newer, vcpkg, boost. For development on Linux, the dependencies are CUDA version 10.2 minimal, CMake 3.15 or newer, and boost. This project is licensed under the Apache License, Version 2.0. You can use an installation script or dockerfile to install qikkDB. -
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Oracle Autonomous Data Warehouse is a cloud data warehouse service that eliminates all the complexities of operating a data warehouse, dw cloud, data warehouse center, securing data, and developing data-driven applications. It automates provisioning, configuring, securing, tuning, scaling, and backing up of the data warehouse. It includes tools for self-service data loading, data transformations, business models, automatic insights, and built-in converged database capabilities that enable simpler queries across multiple data types and machine learning analysis. It’s available in both the Oracle public cloud and customers' data centers with Oracle Cloud@Customer. Detailed analysis by industry expert DSC illustrates why Oracle Autonomous Data Warehouse is a better pick for the majority of global organizations. Learn about applications and tools that are compatible with Autonomous Data Warehouse.
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Apache Pinot
Apache Corporation
Pinot is designed to answer OLAP queries with low latency on immutable data. Pluggable indexing technologies - Sorted Index, Bitmap Index, Inverted Index. Joins are currently not supported, but this problem can be overcome by using Trino or PrestoDB for querying. SQL like language that supports selection, aggregation, filtering, group by, order by, distinct queries on data. Consist of of both offline and real-time table. Use real-time table only to cover segments for which offline data may not be available yet. Detect the right anomalies by customizing anomaly detect flow and notification flow. -
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Apache Hudi
Apache Corporation
Hudi is a rich platform to build streaming data lakes with incremental data pipelines on a self-managing database layer, while being optimized for lake engines and regular batch processing. Hudi maintains a timeline of all actions performed on the table at different instants of time that helps provide instantaneous views of the table, while also efficiently supporting retrieval of data in the order of arrival. A Hudi instant consists of the following components. Hudi provides efficient upserts, by mapping a given hoodie key consistently to a file id, via an indexing mechanism. This mapping between record key and file group/file id, never changes once the first version of a record has been written to a file. In short, the mapped file group contains all versions of a group of records. -
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DuckDB
DuckDB
Processing and storing tabular datasets, e.g. from CSV or Parquet files. Large result set transfer to client. Large client/server installations for centralized enterprise data warehousing. Writing to a single database from multiple concurrent processes. DuckDB is a relational database management system (RDBMS). That means it is a system for managing data stored in relations. A relation is essentially a mathematical term for a table. Each table is a named collection of rows. Each row of a given table has the same set of named columns, and each column is of a specific data type. Tables themselves are stored inside schemas, and a collection of schemas constitutes the entire database that you can access. -
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Typo
Typo
TYPO is a data quality solution that provides error correction at the point of entry into information systems. Unlike reactive data quality tools that attempt to resolve data errors after they are saved, Typo uses AI to proactively detect errors in real-time at the initial point of entry. This enables immediate correction of errors prior to storage and propagation into downstream systems and reports. Typo can be used on web applications, mobile apps, devices and data integration tools. Typo inspects data in motion as it enters your enterprise or at rest after storage. Typo provides comprehensive oversight of data origins and points of entry into information systems including devices, APIs and application users. When an error is identified, the user is notified and given the opportunity to correct the error. Typo uses machine learning algorithms to detect errors. Implementation and maintenance of data rules is not necessary. -
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Canoe
Canoe Intelligence
First-of-its-kind AI technology powering the future of alternative investments. Canoe has reimagined the future of alternative investments with cloud-based, machine learning technology for document collection, data extraction and data science initiatives. We transform complex documents into actionable intelligence within seconds, and empower allocators with tools to unlock new efficiencies for their business. Systematically and consistently categorize, rename, and store documents in our cloud-based repository. Leverage AI and machine-learning based collective intelligence to identify, extract, and normalize data. Action hundreds of accounting, business and investment rules to ensure data accuracy. Seamlessly deliver data to any downstream system via API or compatible flat-file formats. Since 2013, our team of industry experts has been building and perfecting Canoe’s technology to transform the way alternative investors and allocators like you can access your data. -
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Staple
Staple AI
Staple AI is a compliance infrastructure for AI-powered document flows. The first mile of document processing. Enterprises processing documents at scale face a growing compliance problem: AI extracts data, but can't prove where it came from. Staple AI fixes that. Every extracted field carries a cryptographic chain of custody through the MSD (Metastructured Data) layer, from the source document to the ERP entry. Auditors get answers. Boards get accountability. Regulators get evidence. Built at the intersection of Artificial Intelligence (AI), Machine Learning, analytics, and enterprise-grade document infrastructure. What Staple AI does: Intelligent Document Processing across invoices, POs, GRNs, bank statements, KYC docs, contracts, payslips, claims, delivery orders, and more. Template-free. Self-learning. 95%+ extraction accuracy. n-Way Document Matching up to 10 document types simultaneously at the line-item level, with fuzzy matching and variance thresholds. -
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ThreadDB
Textile
ThreadDB is a multi-party database built on IPFS and Libp2p that provides an alternative architecture for data on the web. ThreadDB aims to help power a new generation of web technologies by combining a novel use of event sourcing, Interplanetary Linked Data (IPLD), and access control to provide a distributed, scalable, and flexible database solution for decentralized applications. There are two implementations of ThreadDB, the first is written in Go. The second implementation is written in JavaScript (Typescript, really). This implementation has some optimizations to make it more ideal when writing web applications. The JavaScript implementation is currently a Client of the Go implementation. You can run it against your own go-threads instance or connect it to the Textile Hub to use one of ours. In general, when building apps that use threads in a remote context, like the browser, it's best to push the networking later to remote services whenever possible. -
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KX Insights
KX
KX Insights is a cloud-native platform for critical real-time performance and continuous actionable intelligence. Using complex event processing, high-speed analytics and machine learning interfaces, it enables fast decision-making and automated responses to events in fractions of a second. It’s not just storage and compute elasticity that have moved to the cloud. It’s everything: data, tools, development, security, connectivity, operations, maintenance. KX can help you leverage that power to make smarter, more insightful decisions by integrating real-time analytics into your business operations. KX Insights leverages industry standards to ensure openness and interoperability with other technologies in order to deliver insights faster and more cost-effectively. It operates a microservices-based architecture for capturing, storing and processing high-volume, high-velocity data using cloud standards, services, and protocols. -
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KX Streaming Analytics provides the ability to ingest, store, process, and analyze historic and time series data to make analytics, insights, and visualizations instantly available. To help ensure your applications and users are productive quickly, the platform provides the full lifecycle of data services, including query processing, tiering, migration, archiving, data protection, and scaling. Our advanced analytics and visualization tools, used widely across finance and industry, enable you to define and perform queries, calculations, aggregations, machine learning and AI on any streaming and historical data. Deployable across multiple hardware environments, data can come from real-time business events and high-volume sources including sensors, clickstreams, radio-frequency identification, GPS systems, social networking sites, and mobile devices.
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Versio.io
Versio.io
Versio.io is an enterprise software to manage the detection and post-processing of changes in a enterprise company. Our unique and innovative approaches have enabled us to build a completely new kind of enterprise product. Below we give you insights into our research and development work. Relationships can exist between assets & configurations. These represent an important extension of information. The original data sources only partially have this information. In Versio.io, we can use the topology service to automatically recognise and map such relationships. This means that relationships or dependencies between instances from any data source can be mapped. All business-relevant assets and configuration items from all levels of an organisation can be captured, historicised, topologised and stored in a central repository. -
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OneTick
OneMarketData
It's performance, superior features and unmatched functionality have led OneTick Database to be embraced by leading banks, brokerages, data vendors, exchanges, hedge funds, market makers and mutual funds. OneTick is the premier enterprise-wide solution for tick data capture, streaming analytics, data management and research. With its superior features and unmatched functionality, OneTick is being embraced enthusiastically by leading hedge funds, mutual funds, banks, brokerages, market makers, data vendors and exchanges. OneTick’s proprietary time series database is a unified, multi-asset class platform that includes a fully integrated streaming analytics engine and built-in business logic to eliminate the need for multiple disparate systems. The system provides the lowest total cost of ownership available. -
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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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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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Blueflood
Blueflood
Blueflood is a high throughput, low latency, multi-tenant distributed metric processing system behind Rackspace Metrics, which is currently used in production by the Rackspace Monitoring team and Rackspace public cloud team to store metrics generated by their systems. In addition to Rackspace metrics, other large scale deployments of Blueflood can be found at community Wiki. Data from Blueflood can be used to construct dashboards, generate reports, graphs or for any other use involving time-series data. It focuses on near-realtime data, with data that is queryable mere milliseconds after ingestion. You send metrics to the ingestion service. You query your metrics from the Query service. And in the background, rollups are batch-processed offline so that queries for large time-periods are returned quickly. -
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RRDtool
RRDtool
RRDtool is the OpenSource industry standard, high performance data logging and graphing system for time series data. RRDtool can be easily integrated in shell scripts, perl, python, ruby, lua or tcl applications. -
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Hawkular Metrics
Hawkular Metrics
Hawkular Metrics is a scalable, asynchronous, multi tenant, long term metrics storage engine that uses Cassandra as the data store and REST as the primary interface. This section provides an overview of some of the key features of Hawkular Metrics. The following sections provide in-depth discussions on these as well as other features. Hawkular Metrics is all about scalability. You can run a single instance backed by a single Cassandra node. You can also scale out Cassandra to multiple nodes to handle increasing loads. The Hawkular Metrics server employs a stateless architecture, which makes it easy to scale out as well. This diagram illustrates the various deployment options made possible with Hawkular Metrics' scalable architecture. The upper left shows the simplest deployment with a single Cassandra node and single Hawkular Metrics node. The bottom right picture shows that it is possible to run more Hawkular Metrics nodes than Cassandra nodes. -
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Heroic
Heroic
Heroic is an open-source monitoring system originally built at Spotify to address problems faced with large scale gathering and near real-time analysis of metrics. Heroic uses a small set of components which are responsible for very specific things. Indefinite retention, as long as you have the hardware spend. Federation support to connect multiple Heroic clusters into a global interface. Heroic uses a small set of components which are responsible for very specific things. Consumers are the component responsible for consuming metrics. When building Heroic it was quickly realized that navigating hundreds of millions of time series without context is hard. Heroic has support for federating requests, which allows multiple independent Heroic clusters to serve clients through a single global interface. This can be used to reduce the amount of geographical traffic by allowing one cluster to operate completely isolated within its zone. -
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Proficy Historian
GE Vernova
Proficy Historian is a best-in-class historian software solution that collects industrial time-series and A&E data at very high speed, stores it efficiently and securely, distributes it, and allows for fast retrieval and analysis —driving greater business value. With decades of experience and thousands of successful customer installations around the world, Proficy Historian changes the way companies perform and compete by making data available for asset and process performance analysis. The most recent Proficy Historian enhances usability, configurability and maintainability with significant architectural improvements. Take advantage of the solution’s simple yet powerful features to unlock new value from your equipment, process data, and business models. Remote collector management with UX. Horizontal scalability that enables enterprise-wide data visibility. -
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Circonus IRONdb
Circonus
Circonus IRONdb makes it easy to handle and store unlimited volumes of telemetry data, easily handling billions of metric streams. Circonus IRONdb enables users to identify areas of opportunity and challenge in real time, providing forensic, predictive, and automated analytics capabilities that no other product can match. Rely on machine learning to automatically set a “new normal” as your data and operations dynamically change. Circonus IRONdb integrates with Grafana, which has native support for our analytics query language. We are also compatible with other visualization apps, such as Graphite-web. Circonus IRONdb keeps your data safe by storing multiple copies of your data in a cluster of IRONdb nodes. System administrators typically manage clustering, often spending significant time maintaining it and keeping it working. Circonus IRONdb allows operators to set and forget their cluster, and stop wasting resources manually managing their time series data store.
