Page 97 | Best Data Management Software of 2026

M3

M3 is the obvious choice for Cloud Native companies looking to scale up their Prometheus based monitoring systems. M3 can be used as Prometheus Remote Storage and has 100% PromQL compatibility. M3 was originally developed at Uber in order to provide visibility into Uber’s business operations, microservices and infrastructure. With its ability to horizontally scale with ease, M3 provides a single centralized storage solution for all monitoring use cases. Three replicas of data with quorum writes and reads for consistency. Proven in production to ingest more than one billion datapoints per second while serving more than two billion datapoint reads per second. Open sourced under the Apache 2 license with a highly active community.

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IBM InfoSphere Data Architect

IBM

A data design solution that enables you to discover, model, relate, standardize and integrate diverse and distributed data assets throughout the enterprise. IBM InfoSphere® Data Architect is a collaborative enterprise data modeling and design solution that can simplify and accelerate integration design for business intelligence, master data management and service-oriented architecture initiatives. InfoSphere Data Architect enables you to work with users at every step of the data design process, from project management to application design to data design. The tool helps to align processes, services, applications and data architectures. Simple warehouse design, dimensional modeling and change management tasks help reduce development time and give you the tools to design and manage warehouses from an enterprise logical model. Time stamped, column-organized tables offer a better understanding of data assets to help increase efficiency and reduce time to market.

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Graviti

Unstructured data is the future of AI. Unlock this future now and build an ML/AI pipeline that scales all of your unstructured data in one place. Use better data to deliver better models, only with Graviti. Get to know the data platform that enables AI developers with management, query, and version control features that are designed for unstructured data. Quality data is no longer a pricey dream. Manage your metadata, annotation, and predictions in one place. Customize filters and visualize filtering results to get you straight to the data that best match your needs. Utilize a Git-like structure to manage data versions and collaborate with your teammates. Role-based access control and visualization of version differences allows your team to work together safely and flexibly. Automate your data pipeline with Graviti’s built-in marketplace and workflow builder. Level-up to fast model iterations with no more grinding.

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Above Data

The investment landscape has changed. Each day humans produce 2.5 quintillion bytes of data. From credit cards to satellites, the quantity and granularity of information can improve forecasting and real-time decision-making for those able to harness it. That’s where we come in. Above Data procures and packages unique data assets in a flexible, intuitive and no-code environment allowing you to get quick answers before the competition. The investment landscape has changed. Each day humans produce 2.5 quintillion bytes of data. From credit cards to satellites, the quantity and granularity of information can improve forecasting and real-time decision-making for those able to harness it. That’s where we come in. Above Data procures and packages unique data assets in a flexible, intuitive and no-code environment allowing you to get quick answers before the competition.

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Visible Systems

Looking for searchable solutions in a pile of unstructured data is like looking for a needle in a haystack. Our technicians are trained to spot hidden trends and patterns in that tangled web. Through this process, we will gather, catalogue, annotate, and combine it into an understandable and user-friendly format to streamline critical decisions. This allows us to create results that unlock actionable insights for business growth. At Visible Systems, we understand that traditional data analysis tools are only designed to analyze data that is in a specific format. However, most data is formless since it is sourced from different locations. Using data discovery, we can aggregate and format it from various sources to streamline analysis. This results in data that is in the right format, which can ensure timely deliverables. We realize that data discovery is a continuous process and old data is as valuable as fresh data.

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PySpark

PySpark is an interface for Apache Spark in Python. It not only allows you to write Spark applications using Python APIs, but also provides the PySpark shell for interactively analyzing your data in a distributed environment. PySpark supports most of Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning) and Spark Core. Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrame and can also act as distributed SQL query engine. Running on top of Spark, the streaming feature in Apache Spark enables powerful interactive and analytical applications across both streaming and historical data, while inheriting Spark’s ease of use and fault tolerance characteristics.

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Apache Arrow

The Apache Software Foundation

Apache Arrow defines a language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs. The Arrow memory format also supports zero-copy reads for lightning-fast data access without serialization overhead. Arrow's libraries implement the format and provide building blocks for a range of use cases, including high performance analytics. Many popular projects use Arrow to ship columnar data efficiently or as the basis for analytic engines. Apache Arrow is software created by and for the developer community. We are dedicated to open, kind communication and consensus decisionmaking. Our committers come from a range of organizations and backgrounds, and we welcome all to participate with us.

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pandas

pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format. Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form.Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets. Time series-functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data.

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MagicDraw

Dassault Systèmes

MagicDraw supports the UML 2 metamodel, the latest XMI standard for data storage and the most popular programming languages for implementation. Unlike other UML modeling and architecture environments, MagicDraw makes it easy for you to deploy a Software Development Life Cycle (SDLC) environment that best suits the needs of your business. Our approach to standards and our Open API makes it easy for you to integrate with applications that work together, best supporting the needs of your business. We integrate with many leading products: IDEs, requirements, testing, estimation, MDD, database, and others. MagicDraw provides independence from any specific software development process, conforming nicely to your company process; allowing centralization of business and process modeling, requirements capture and design. MagicDraw is not tied to any one phase of your project.

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Eclipse Papyrus

Eclipse Foundation

To address any specific domain, every part of Eclipse Papyrus may be customized: UML profile, model explorer, diagram notation and style, properties views, palette and creation menus, and much more. Eclipse Papyrus enables model-based techniques: model-based simulation, model-based formal testing, safety analysis, performance/trade-offs analysis, architecture exploration. Eclipse Papyrus is an industrial-grade open source Model-Based Engineering tool. Eclipse Papyrus has notably been used successfuly in industrial projects and is the base platform for several industrial modeling tools. Eclipse Papyrus provides also a complete support to SysML in order to enable model-based system engineering. All the modeling features of Eclipse Papyrus are designed to be customizable and to maximize reuse.

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Iris.ai

Iris.ai is a world-leading and award-winning AI engine for scientific text understanding. It is a comprehensive platform for all research-related knowledge processing needs. Our Researcher Workspace solution provides smart search and a wide range of smart filters, reading list analysis, auto-generated summaries, autonomous extraction, and systematising of data. Iris.ai allows humans to focus on value creation by saving 75% of a researcher’s time, doing specialised, interdisciplinary field analysis to an above human level of accuracy. Its algorithms for text similarity, tabular data extraction, domain-specific entity representation learning, and entity disambiguation and linking measure up to the best in the world. Its machine builds a comprehensive knowledge graph containing all entities and their linkages to allow humans to learn from it, use it, and give feedback to the system. Applying these features to scientific and technical text is a complicated challenge few others can achieve.

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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

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 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

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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DataTerrain

Automation delivers business intelligence reporting upgrades at your fingertips! DataTerrain can help you build Oracle Transactional Business Intelligence (OTBI) reports with extensive usage of HCM extracts. Our expertise in HCM analytics and reports with embedded security features is proven with industry-leading customers in the US and Canada. We can demonstrate with references and pre-built reports and dashboards. Oracle’s fully integrated talent acquisition cloud-based solution (Taleo) includes recruitment marketing and employee referrals to source talent, provide end-to-end recruiting automation, and streamline employee onboarding. We have proven our expertise in building reports and dashboards for over 10 years, with more than 200 customers worldwide. DataTerrain specializes in Snowflake, Tableau Analytics/reporting, Amazon’s Quicksight analytics/reporting and Jasper studio reporting, solutions for Big Data.

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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 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 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

Oracle

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

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 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's unique interface allows viewing and sorting of documents with ease, in an intuitive manner. Multiple users can sort, share and export documents to a variety of systems. Staple's proprietary document viewing system allows simple point and click interactions with documents, delivers lightning-fast processing, and continuous feedback to its consistently improving AI. More than a typical OCR or a text mining solution, our deep technology approach reads and interprets documents just as a human would. Instant, accurate data extraction and document processing means that businesses can substantially automate their workflows and reduce reliance on human data entry. Staple uses a proprietary fusion of machine learning and computer vision to deliver unprecedented extraction performance in terms of speed and precision. Try us out, we'd love to show you what we can do. Staple's data extraction solution can be accessed via Xero or Quickbooks integrations, or directly via our API.

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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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Best Data Management Software - Page 97

Compare the Top Data Management Software as of February 2026 - Page 97