Best Free Data Management Software of 2025 - Page 32

Memgraph offers a light and powerful graph platform comprising the Memgraph Graph Database, MAGE Library, and Memgraph Lab Visualization. Memgraph is a dynamic, lightweight graph database optimized for analyzing data, relationships, and dependencies quickly and efficiently. It comes with a rich suite of pre-built deep path traversal algorithms and a library of traditional, dynamic, and ML algorithms tailored for advanced graph analysis, making Memgraph an excellent choice in critical decision-making scenarios such as risk assessment (fraud detection, cybersecurity threat analysis, and criminal risk assessment), 360-degree data and network exploration (Identity and Access Management (IAM), Master Data Management (MDM), Bill of Materials (BOM)), and logistics and network optimization. Memgraph's vibrant open-source community brings together over 150,000 developers in more than 100 countries to exchange ideas and optimize the next generation of in-memory data-driven applications across GenAI/ LLMs and real-time analytics performed with streaming data.

Datastore serves as a robust NoSQL database designed to accommodate the needs of your applications with remarkable scalability. It efficiently manages sharding and replication on its own, ensuring that you benefit from a database that is both highly durable and readily available, adapting to your application's demands as they grow. This platform offers a wealth of features, including ACID transactions, SQL-like querying capabilities, and indexing options, among others. By utilizing Datastore's RESTful interface, you can conveniently access your data across any deployment target. It enables the creation of solutions that integrate seamlessly between App Engine and Compute Engine, positioning Datastore as a central hub for your data needs. Concentrate on the development of your applications without the hassle of provisioning or anticipating load requirements. As your data expands, Datastore automatically and effortlessly adjusts, ensuring that performance remains optimal even during periods of increased traffic. Additionally, this flexibility allows developers to focus on innovation rather than infrastructure management.

Count is an interactive data whiteboard that allows for full collaboration. It combines the flexibility and creativity that a whiteboard offers with the power and reactivity of BI Notebooks. It is easy to break down complex SQL queries and data model into interconnected cells for better understanding the logic. Use sticky notes and graphics to help stakeholders understand your work. As you build, you can collaborate with other analysts or stakeholders to get faster feedback. Turn any canvas into a slideshow or interactive report.

GUN is a real-time, decentralized, and offline-first graph database engine that allows for seamless data storage, loading, and sharing within applications without the need to manage servers, network requests, or handle issues related to offline changes or concurrency conflicts. This lightweight, efficient data synchronization and storage system operates wherever JavaScript is supported, enabling developers to concentrate on their app's data needs rather than technical hurdles. By using GUN, you can rapidly create innovative applications while harnessing powerful internet capabilities such as decentralization and genuine privacy, ultimately contributing to a freer and more open web. GUN serves as a versatile database engine that operates across browsers, mobile devices, and servers, empowering you to configure your ideal data management system. With GUN, you can build applications with enhanced functionality while ensuring user privacy and eliminating dependencies on traditional infrastructures.

Blazegraph™ DB is an exceptionally high-performance graph database that offers support for Blueprints, along with RDF and SPARQL APIs. Capable of handling up to 50 billion edges on a single server, it has been adopted by numerous Fortune 500 companies, including EMC and Autodesk. This database is integral to various Precision Medicine applications and enjoys extensive use in the life sciences sector. Additionally, it plays a crucial role in cyber analytics for both commercial enterprises and government agencies. Moreover, Blazegraph powers the Wikidata Query Service for the Wikimedia Foundation. Users have the option to download it as an executable jar, a war file, or a tar.gz distribution. Designed with user-friendliness in mind, Blazegraph allows for a quick start, although it comes with SSL and authentication turned off by default. For those deploying in a production environment, it is highly advisable to activate SSL, establish authentication, and implement suitable network configurations to ensure security. Below, you will find valuable resources to assist you in making these configurations effectively. Furthermore, the documentation provides a comprehensive guide for new users to navigate setup and support effectively.

Apache Giraph is a scalable iterative graph processing framework designed to handle large datasets efficiently. It has gained prominence at Facebook, where it is employed to analyze the intricate social graph created by user interactions and relationships. Developed as an open-source alternative to Google's Pregel, which was introduced in a seminal 2010 paper, Giraph draws inspiration from the Bulk Synchronous Parallel model of distributed computing proposed by Leslie Valiant. Beyond the foundational Pregel model, Giraph incorporates numerous enhancements such as master computation, sharded aggregators, edge-focused input methods, and capabilities for out-of-core processing. The ongoing enhancements and active support from a growing global community make Giraph an ideal solution for maximizing the analytical potential of structured datasets on a grand scale. Additionally, built upon the robust infrastructure of Apache Hadoop, Giraph is well-equipped to tackle complex graph processing challenges efficiently.

Fluree is an immutable RDF graph database written in Clojure and adhering to W3C standards, supporting JSON and JSON-LD while accommodating various RDF ontologies. It operates with an immutable ledger that secures transactions with cryptographic integrity, alongside a rich RDF graph database capable of various queries. It employs SmartFunctions for enforcing data management rules, including identity and access management and data quality. Additionally, It boasts a scalable, cloud-native architecture utilizing a lightweight Java runtime, with individually scalable ledger and graph database components, embodying a "Data-Centric" ideology that treats data as a reusable asset independent of singular applications.

The foundation of creating intelligent systems lies in the database, and Grakn serves as a sophisticated knowledge graph database. It features an incredibly user-friendly and expressive data schema that allows for the definition of hierarchies, hyper-entities, hyper-relations, and rules to establish detailed knowledge models. With its intelligent language, Grakn executes logical inferences on data types, relationships, attributes, and intricate patterns in real-time across distributed and stored data. It also offers built-in distributed analytics algorithms, such as Pregel and MapReduce, which can be accessed using straightforward queries within the language. The system provides a high level of abstraction over low-level patterns, simplifying the expression of complex constructs while optimizing query execution automatically. By utilizing Grakn KGMS and Workbase, enterprises can effectively scale their knowledge graphs. Furthermore, this distributed database is engineered to function efficiently across a network of computers through techniques like partitioning and replication, ensuring seamless scalability and performance.

HyperGraphDB serves as a versatile, open-source data storage solution founded on the sophisticated knowledge management framework of directed hypergraphs. Primarily created for persistent memory applications in knowledge management, artificial intelligence, and semantic web initiatives, it can also function as an embedded object-oriented database suitable for Java applications of varying scales, in addition to serving as a graph database or a non-SQL relational database. Built upon a foundation of generalized hypergraphs, HyperGraphDB utilizes tuples as its fundamental storage units, which can consist of zero or more other tuples; these individual tuples are referred to as atoms. The data model can be perceived as relational, permitting higher-order, n-ary relationships, or as graph-based, where edges can connect to an arbitrary assortment of nodes and other edges. Each atom is associated with a strongly-typed value that can be customized extensively, as the type system that governs these values is inherently embedded within the hypergraph structure. This flexibility allows developers to tailor the database according to specific project requirements, making it a robust choice for a wide range of applications.

RecallGraph is a versioned graph data store. It retains all changes its data (vertices, edges) have undergone to get to their current state. It supports point-in time graph traversals that allow the user to query any past state of a graph as well as the present. RecallGraph can be used in situations where data is best represented using a network of edges and vertices (i.e., as a graph). 1. Both edges and vertices can contain properties in the form attribute/value pairs (equivalent of JSON objects). 2. Documents (vertices/edges), can change throughout their lives (both in their individual attributes/values as well as in their relationships to each other). 3. Documents from the past are just as important as their current states, so it is essential to retain and queryable their change history. Also see this blog post for an intro - https://blog.recallgraph.tech/never-lose-your-old-data-again.

HugeGraph is a high-performance and scalable graph database capable of managing billions of vertices and edges efficiently due to its robust OLTP capabilities. This database allows for seamless storage and querying, making it an excellent choice for complex data relationships. It adheres to the Apache TinkerPop 3 framework, enabling users to execute sophisticated graph queries using Gremlin, a versatile graph traversal language. Key features include Schema Metadata Management, which encompasses VertexLabel, EdgeLabel, PropertyKey, and IndexLabel, providing comprehensive control over graph structures. Additionally, it supports Multi-type Indexes that facilitate exact queries, range queries, and complex conditional queries. The platform also boasts a Plug-in Backend Store Driver Framework that currently supports various databases like RocksDB, Cassandra, ScyllaDB, HBase, and MySQL, while also allowing for easy integration of additional backend drivers as necessary. Moreover, HugeGraph integrates smoothly with Hadoop and Spark, enhancing its data processing capabilities. By drawing on the storage structure of Titan and the schema definitions from DataStax, HugeGraph offers a solid foundation for effective graph database management. This combination of features positions HugeGraph as a versatile and powerful solution for handling complex graph data scenarios.

Apache Druid is a distributed data storage solution that is open source. Its fundamental architecture merges concepts from data warehouses, time series databases, and search technologies to deliver a high-performance analytics database capable of handling a diverse array of applications. By integrating the essential features from these three types of systems, Druid optimizes its ingestion process, storage method, querying capabilities, and overall structure. Each column is stored and compressed separately, allowing the system to access only the relevant columns for a specific query, which enhances speed for scans, rankings, and groupings. Additionally, Druid constructs inverted indexes for string data to facilitate rapid searching and filtering. It also includes pre-built connectors for various platforms such as Apache Kafka, HDFS, and AWS S3, as well as stream processors and others. The system adeptly partitions data over time, making queries based on time significantly quicker than those in conventional databases. Users can easily scale resources by simply adding or removing servers, and Druid will manage the rebalancing automatically. Furthermore, its fault-tolerant design ensures resilience by effectively navigating around any server malfunctions that may occur. This combination of features makes Druid a robust choice for organizations seeking efficient and reliable real-time data analytics solutions.

Apache Ranger™ serves as a framework designed to facilitate, oversee, and manage extensive data security within the Hadoop ecosystem. The goal of Ranger is to implement a thorough security solution throughout the Apache Hadoop landscape. With the introduction of Apache YARN, the Hadoop platform can effectively accommodate a genuine data lake architecture, allowing businesses to operate various workloads in a multi-tenant setting. As the need for data security in Hadoop evolves, it must adapt to cater to diverse use cases regarding data access, while also offering a centralized framework for the administration of security policies and the oversight of user access. This centralized security management allows for the execution of all security-related tasks via a unified user interface or through REST APIs. Additionally, Ranger provides fine-grained authorization, enabling specific actions or operations with any Hadoop component or tool managed through a central administration tool. It standardizes authorization methods across all Hadoop components and enhances support for various authorization strategies, including role-based access control, thereby ensuring a robust security framework. By doing so, it significantly strengthens the overall security posture of organizations leveraging Hadoop technologies.

For macOS users seeking a PostgreSQL GUI tool, PSequel stands out with its straightforward and user-friendly interface, allowing users to efficiently execute common PostgreSQL tasks. While pgAdmin is known for its extensive features, it often suffers from a cumbersome and complex user interface. Although there are numerous PostgreSQL GUI tools available, many of them rely on web or Java technologies, or they lack essential features that users desire. In the realm of MySQL, Sequel Pro is often regarded as the top client; however, it has not made strides in supporting PostgreSQL. This gap inspired us to create PSequel, which is built from the ground up using Swift 2, drawing significant inspiration from the design of Sequel Pro. Currently, PSequel is a passion project that we are developing in our free time. By focusing exclusively on macOS versions 10.10 and above, we aim to streamline the codebase and reduce the need for testing on outdated systems, resulting in a more efficient and less error-prone application. As PSequel continues to evolve, we are excited about the potential enhancements and features that will be introduced in future updates. The project remains in its early development phase, promising a bright future for PostgreSQL users on macOS.

xViz offers a sophisticated collection of custom visuals designed specifically for Power BI. The xViz suite features over 16 innovative chart types tailored to meet the diverse needs of enterprises. Among the highly-rated charts, which boast a perfect 5.0 score on Microsoft AppSource, are the Marimekko chart, Hierarchical Chart, Multiple Axis chart, Tag Cloud, and Linear Gauge, demonstrating the suite's versatility and appeal. These charts not only enhance data visualization but also empower businesses to derive deeper insights from their data effectively.

Algolytics specializes in delivering software tools and consulting expertise focused on predictive analytics, risk management, data quality, social network analysis, and the intricate analysis of extensive datasets. Discover a versatile tool designed for data processing, analysis, and modeling! With an intuitive workflow interface, you can delve into your data and much more. The platform facilitates data extraction and storage across various database systems, files, and enables seamless data transformations. You can conduct numerous operations on your data, including sampling, merging datasets, and partitioning. AdvancedMiner presents endless capabilities for experienced users, which can be effortlessly developed or modified within the application. Additionally, it provides comprehensive support for SQL, including a variety of analytical functions, enhancing your data manipulation capabilities further. Overall, Algolytics empowers users to harness the full potential of their data efficiently.

AI teams encounter obstacles that necessitate the development of innovative technologies, which we specialize in creating. Traditional data warehouses and lakes struggle to accommodate unstructured data types such as text, images, and videos. Our approach integrates AI with software development, specifically designed for data scientists, machine learning engineers, and data engineers alike. Instead of reinventing existing solutions, we provide a swift and cost-effective route to bring your projects into production. Your data remains securely stored under your control, and model training occurs on your own infrastructure. By addressing the limitations of current data handling methods, we ensure that AI teams can effectively meet their challenges. Our Studio functions as an extension of platforms like GitHub, GitLab, or BitBucket, allowing seamless integration. You can choose to sign up for our online SaaS version or reach out for an on-premise installation tailored to your needs. This flexibility allows organizations of all sizes to adopt our solutions effectively.

The enterprise database for time series, documents, and vectors. Store any type data and combine the simplicity and scalability NoSQL with SQL. CrateDB is a distributed database that runs queries in milliseconds regardless of the complexity, volume, and velocity.

Make your website look professional by creating a banner. This will ensure that your website's use and tracking technologies are compliant with the GDPR and other global privacy laws. One line of code blocks cookies and other tracking technologies until users consent. Avoid ugly banners that don't match your website's style. Use custom fonts and colors just like your website builder. You can create a preference center so that your users can give their consent to each purpose, such as Marketing and Analytics. Display the appropriate cookie banner to users based on their country. Keep searchable records of consent. It can be used as proof of consent in the event of a dispute. Our dashboard gives you a monthly compliance report. You can track new pages, cookies, and fix problems. Modern privacy laws such as the GDPR and CCPA include cookies and IP in their definitions of Personal Data. This means that you must inform users about the use of cookies and the purposes for which they are being used (Analytics, Marketing, etc).

Report Hopper allows you to generate tailored reports for your clients, whether they run a small business, an e-commerce site, or a large enterprise, creating eye-catching and personalized insights. Setting up your Report Hopper account is a breeze; simply connect your Google Analytics, Facebook, or other accounts with just one click, and your data will be seamlessly integrated into your reports. You can also schedule weekly and monthly reports to be automatically generated and sent directly to you or your clients, ensuring that they receive impressive insights consistently. With Report Hopper, customized reports that impress are just a few clicks away! While there are some limitations to the service (after all, we have a team to support), the core features remain completely free. Plus, you can continuously refine your reports to ensure they meet your clients' evolving needs.

Visplore makes the analysis of large, dirty time series data intuitive and extremely efficient. For process experts, R&D engineers, quality managers, industry consultants, and everyone who has spent a lot of time on the tedious preparation of complex measurement data. Knowing your data is the fundament of unlocking its value. Visplore offers ready-to-use tools to understand correlations, patterns, trends and much more, faster than ever. Cleansing and annotating make the difference between valuable and useless data. In Visplore, you deal with dirty data like outliers, anomalies and process changes as easily as using a drawing program. Integrations with Python, R, Matlab and many other sources makes workflow integration straightforward. And all of that at a performance that is still fun even with millions of data records, and allows for unexpectedly creative analyses.

SQL Workbench/J is a versatile and free SQL query tool that operates independently of any specific database management system and is compatible with various operating systems due to its Java foundation. It is designed to execute SQL scripts, whether interactively or in batch mode, while also offering robust import and export functionalities. Although it does not prioritize graphical query building or complex database administration tasks, it includes a powerful export command that allows users to generate text files, XML, HTML, or SQL formats, including support for BLOB data. Users can easily export all tables from a database to a designated directory using a single command, and the export files can be compressed during the process. The tool also supports efficient import of text, XML, and spreadsheet files, enabling users to bring in sets of files, including compressed versions, from a directory with one command. Additionally, it intelligently detects foreign key constraints to ensure proper data insertion order. Users can also compare two database schemas to identify differences, and the tool provides the capability to convert XML output into the appropriate SQL ALTER statements using XSLT, enhancing its functionality even further. Overall, SQL Workbench/J serves as a comprehensive solution for users seeking a straightforward approach to managing SQL queries across various platforms.

Cube serves as a comprehensive platform that establishes a universal semantic layer aimed at streamlining and consolidating the management and analysis of enterprise data. By revolutionizing data management practices, Cube removes the challenges associated with inconsistent models and metrics, thereby providing users with reliable data that is also primed for AI applications. This innovative platform is instrumental for organizations seeking to expand their data infrastructure by harmonizing various data sources and generating uniform metrics applicable across different teams. Tailored for enterprises, Cube enhances analytical capabilities, ensures data accessibility, and facilitates effortless access to AI-driven insights. With Cube, companies can navigate the complexities of data management more effectively than ever before.

Delivers comprehensive solutions for GDPR, CCPA, LGPD, and other privacy regulations, facilitating effective management of privacy-related tasks while ensuring compliance with legal standards. Streamlines your privacy management initiatives, minimizing the need for manual involvement to achieve compliance. Incorporates smart analytics to evaluate and identify risks, along with recommendations for effective mitigation strategies. The advanced dashboard and reporting features enhance visibility, supporting informed decision-making processes. With ready-to-use PIA/DPIA question templates, automated workflows, and notification setups, you can conduct regular assessments while effectively collaborating with business units and IT. Surveys driven by workflows are designed to pinpoint systems and sources of personal data, aiding in the generation of "Record of Processing Activities (RoPA)." It also includes predefined steps to maintain an efficient data inventory. Furthermore, the approach automates the fulfillment of Data Subject Access Requests (DSAR), supported by integrated workflows and robust data discovery capabilities, making privacy management smoother and more effective than ever. This comprehensive solution not only simplifies regulatory compliance but also empowers organizations to proactively manage their privacy obligations.

We empower retailers to advocate for their customers' privacy rights without sacrificing conversion rates or complicating the processing of Subject Access Requests (SARs). Transform your privacy settings into a benchmark for exceptional user experience and customer service. Ensure that customers have straightforward access to user-friendly privacy settings. Create a centralized hub for all customer privacy concerns, where they can manage their preferences easily. Offer transparent, comprehensible explanations regarding the data you gather and its intended use, fostering trust in your brand. Instead of an all-or-nothing consent model, which is detrimental to both parties, provide options for customers to selectively opt-in or out of specific data collection practices, enhancing their sense of control and potentially boosting your conversion rates. Supply a simple, clear report that illustrates the data you have collected, how it is utilized, and the measures in place to safeguard it. By streamlining your response to Subject Access Requests (SARs), you can improve your customers’ privacy experience while also simplifying the workload for your team. Ultimately, a well-managed privacy framework not only benefits customers but also strengthens your business’s reputation and reliability.