Best Data Management Software - Page 91
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Compare the Top Data Management Software as of May 2026 - Page 91
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1
TerarkDB
Terark
TerarkDB is a core product of Terark. It is a RocksDB distribution that powered by ©™Terark algorithms. with these algorithms, TerarkDB is able to store more data and access much faster than official RocksDB(3+X more data and 10+X faster) on same hardware. TerarkDB is completely compatible(binary compatible) with official RocksDB. We forked RocksDB and made a few changes to fit our algorithms, we've added it as submodule rocksdb here. Our changes for RocksDB does not change any RocksDB API, and does not have any extra dependencies, say, Terark modified RocksDB does not depend on TerarkZipTable(Without TerarkZipTable, Terark RocksDB works exactly same as official RocksDB). -
2
Oracle Exadata
Oracle
Oracle Exadata is the best place to run Oracle Database, simplifying digital transformations, increasing database performance, and reducing costs. Customers achieve higher availability, greater performance, and up to 40% lower cost with Oracle Exadata, as described in Wikibon’s analysis. Oracle Cloud Infrastructure, Oracle Cloud@Customer, and on-premises deployment options enable customers to modernize database infrastructure, move enterprise applications to the cloud, and rapidly implement digital transformations. Oracle Exadata allows customers to run Oracle Database with the same high performance, scale, and availability wherever needed. Workloads easily move between on-premises data centers, Cloud@Customer deployments, and Oracle Cloud Infrastructure, enabling customers to modernize operations and reduce costs. -
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TiDB
PingCAP
An open-source, cloud-native, distributed SQL database for elastic scale and real-time analytics. Supported by a wealth of open-source data migration tools in the ecosystem, TiDB gives you the freedom to choose your own vendor and avoid lock-in. Purposely built to deliver SQL at scale, TiDB eliminates the scaling problems of traditional relational databases without intrusion to your application. HTAP database platform that enables real-time situation awareness and decision making on live transactional data and eliminates friction between IT and business goals. TiDB is ACID-compliant and strongly consistent. You can use TiDB as a scale-out MySQL database with familiar SQL syntaxes and ecosystem tools. TiDB automatically shards your data so you don’t have to do it manually. You can simply add new nodes to scale horizontally and elastically to meet your business growth. TiDB simplifies the ETL process and automatically recovers from errors. -
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Vitess
Vitess
A database clustering system for horizontal scaling of MySQL. Vitess combines many important MySQL features with the scalability of a NoSQL database. Its built-in sharding features let you grow your database without adding sharding logic to your application. Vitess automatically rewrites queries that hurt database performance. It also uses caching mechanisms to mediate queries and prevent duplicate queries from simultaneously reaching your database. Vitess automatically handles functions like master failovers and backups. It uses a lock server to track and administer servers, letting your application be blissfully ignorant of database topology. Vitess eliminates the high-memory overhead of MySQL connections. Vitess servers easily handle thousands of connections at once. MySQL doesn’t natively support sharding, but you will likely need it as your database grows. -
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Alibaba Cloud DRDS
Alibaba
Distributed Relational Database Service (DRDS) is a lightweight, flexible, stable, and efficient middleware product developed by Alibaba Cloud. DRDS focuses on expanding standalone relational databases, and has been tested by core transaction links in Tmall, such as during the Singles’ Day Shopping Festival. DRDS has been used for ten years and is a trusted database service provider. Supports cluster-based data read and write and data storage. DRDS operates on multiple standalone servers, and performance is not affected by the number of user connections. Supports upgraded and downgraded data configurations, and the visualized scale-up and scale-out of data storage. Provides read and write splitting to linearly improve the reading performance. Supports multiple data splitting methods based on data types, such as parallel data splitting. Focuses on the primary shards of the database and supports parallel query execution. -
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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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Google Cloud Memorystore
Google
Reduce latency with scalable, secure, and highly available in-memory service for Redis and Memcached. Memorystore automates complex tasks for open source Redis and Memcached like enabling high availability, failover, patching, and monitoring so you can spend more time coding. Start with the lowest tier and smallest size and then grow your instance with minimal impact. Memorystore for Memcached can support clusters as large as 5 TB supporting millions of QPS at very low latency. Memorystore for Redis instances are replicated across two zones and provide a 99.9% availability SLA. Instances are monitored constantly and with automatic failover—applications experience minimal disruption. Choose from the two most popular open source caching engines to build your applications. Memorystore supports both Redis and Memcached and is fully protocol compatible. Choose the right engine that fits your cost and availability requirements. -
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AsparaDB
Alibaba
ApsaraDB for Redis is an automated and scalable tool for developers to manage data storage shared across multiple processes, applications or servers. As a Redis protocol compatible tool, ApsaraDB for Redis offers exceptional read-write capabilities and ensures data persistence by using memory and hard disk storage. ApsaraDB for Redis provides data read-write capabilities at high speed by retrieving data from in-memory caches and ensures data persistence by using both memory and hard disk storage mode. ApsaraDB for Redis supports advanced data structures such as leaderboard, counting, session, and tracking, which are not readily achievable through ordinary databases. ApsaraDB for Redis also has an enhanced edition called "Tair" . Tair has officially handled the data caching scenarios of Alibaba Group since 2009 and has proven its outstanding performance in scenarios such as Double 11 Shopping Festival. -
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Amazon Timestream
Amazon
Amazon Timestream is a fast, scalable, and serverless time series database service for IoT and operational applications that makes it easy to store and analyze trillions of events per day up to 1,000 times faster and at as little as 1/10th the cost of relational databases. Amazon Timestream saves you time and cost in managing the lifecycle of time series data by keeping recent data in memory and moving historical data to a cost optimized storage tier based upon user defined policies. Amazon Timestream’s purpose-built query engine lets you access and analyze recent and historical data together, without needing to specify explicitly in the query whether the data resides in the in-memory or cost-optimized tier. Amazon Timestream has built-in time series analytics functions, helping you identify trends and patterns in your data in near real-time. -
10
Palantir Foundry
Palantir Technologies
Foundry is a transformative data platform built to help solve the modern enterprise’s most critical problems by creating a central operating system for an organization’s data, while securely integrating siloed data sources into a common analytics and operations picture. Palantir works with commercial companies and government organizations alike to close the operational loop, feeding real-time data into your data science models and updating source systems. With a breadth of industry-leading capabilities, Palantir can help enterprises traverse and operationalize data to enable and scale decision-making, alongside best-in-class security, data protection, and governance. Foundry was named by Forrester as a leader in the The Forrester Wave™: AI/ML Platforms, Q3 2022. Scoring the highest marks possible in product vision, performance, market approach, and applications criteria. As a Dresner-Award winning platform, Foundry is the overall leader in the BI and Analytics market and rate -
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Elucidata Polly
Elucidata
Harness the power of biomedical data with Polly. The Polly Platform helps to scale batch jobs, workflows, coding environments and visualization applications. Polly allows resource pooling and provides optimal resource allocation based on your usage requirements and makes use of spot instances whenever possible. All this leads to optimization, efficiency, faster response time and lower costs for the resources. Get access to a dashboard to monitor resource usage and cost real time and minimize overhead of resource management by your IT team. Version control is integral to Polly’s infrastructure. Polly ensures version control for your workflows and analyses through a combination of dockers and interactive notebooks. We have built a mechanism that allows the data, code and the environment co-exist. This coupled with data storage on the cloud and the ability to share projects ensures reproducibility of every analysis you perform. -
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Quantifind Graphyte
Quantifind
Quantifind’s data analytics platform has been used for over a decade by governments and Fortune 50 companies to gain insights from a comprehensive array of public sources. Its success is rooted in its fusion of science with design; machine learning innovations with intuitive, feature-rich web applications and APIs. Today, Graphyte is used to combat financial crime risk. Its accuracy and features enable our customers to improve the efficiency of their AML investigations by 40% or more. Corporate data, law enforcement, regulatory, registrations, leaks, PEPs, sanctions, enforcement actions, barred and banned lists, and social media. Quantifind technology is leveraged throughout the investigation workflow to gain efficiencies in every step of the process. A powerful web application with a consumer-grade UX lets investigators find what they’re looking for fast. -
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JanusGraph
JanusGraph
JanusGraph is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. JanusGraph is a project under The Linux Foundation, and includes participants from Expero, Google, GRAKN.AI, Hortonworks, IBM and Amazon. Elastic and linear scalability for a growing data and user base. Data distribution and replication for performance and fault tolerance. Multi-datacenter high availability and hot backups. All functionality is totally free. No need to buy commercial licenses. JanusGraph is fully open source under the Apache 2 license. JanusGraph is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time. Support for ACID and eventual consistency. In addition to online transactional processing (OLTP), JanusGraph supports global graph analytics (OLAP) with its Apache Spark integration. -
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xtendr
xtendr
xtendr unhides detailed, privacy-preserving insights across multiple independent data sources. xtendr enables access to thus far inaccessible data, and protects you during your entire data lifecycle, giving you confidence in complete privacy and regulatory compliance. xtendr is more than anonymity, it’s the critical missing piece for multi-party data sharing with true privacy protection - it is cryptography on duty so you can reach your full potential. The most advanced privacy-enhancing data collaboration technology. xtendr solved the decades-long cryptography challenge of data sharing between mutually mistrustful parties. Take your business further with an enterprise-grade data protection offering that allows individual organizations to form data partnerships while protecting sensitive data. Data is the currency of our digital age. Some argue that it is replacing oil as the world's most valuable resource and there is no doubt about its growing importance. -
15
Nebula Graph
vesoft
The graph database built for super large-scale graphs with milliseconds of latency. We are continuing to collaborate with the community to prepare, popularize and promote the graph database. Nebula Graph only allows authenticated access via role-based access control. Nebula Graph supports multiple storage engine types and the query language can be extended to support new algorithms. Nebula Graph provides low latency read and write , while still maintaining high throughput to simplify the most complex data sets. With a shared-nothing distributed architecture , Nebula Graph offers linear scalability. Nebula Graph's SQL-like query language is easy to understand and powerful enough to meet complex business needs. With horizontal scalability and a snapshot feature, Nebula Graph guarantees high availability even in case of failures. Large Internet companies like JD, Meituan, and Xiaohongshu have deployed Nebula Graph in production environments. -
16
Cayley
Cayley
Cayley is an open-source database for Linked Data. It is inspired by the graph database behind Google's Knowledge Graph (formerly Freebase). Cayley is an open-source graph database designed for ease of use and storing complex data. Built-in query editor, visualizer and REPL. Cayley can use multiple query languages like Gizmo, a query language inspired by Gremlin, GraphQL-inspired query language, MQL a simplified version for Freebase fans. Cayley is modular, easy to connect to your favorite programming languages and back-end stores, production ready, well tested and used by various companies for their production workloads and fast with optimized specifically for usage in applications. Rough performance testing shows that, on 2014 consumer hardware and an average disk, 134m quads in LevelDB is no problem and a multi-hop intersection query- films starring X and Y - takes ~150ms. Cayley is configured by default to run in memory (That's what backend memstore means). -
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GraphBase
FactNexus
GraphBase is a Graph Database Management System (Graph DBMS) engineered to simplify the creation and maintenance of complex data graphs. Complex and highly-connected structures are a challenge for the Relational Database Management System (RDBMS). A graph database provides much better modelling utility, performance and scalability. The current crop of graph database products - the triplestores and property graphs - have been around for nearly two decades. They're powerful tools, they have many uses, but they're still not suited to the management of complex data structures. With GraphBase, our goal was to simplify the management of complex data structures, so that your data could become something more. It could become Knowledge. We achieved this by redefining how graph data should be managed. In GraphBase, the graph is a first-class citizen. You get a graph equivalent of the "rows and tables" paradigm that makes a Relational Database so easy to use. -
18
Graph Engine
Microsoft
Graph Engine (GE) is a distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine. The distributed RAM store provides a globally addressable high-performance key-value store over a cluster of machines. Through the RAM store, GE enables the fast random data access power over a large distributed data set. The capability of fast data exploration and distributed parallel computing makes GE a natural large graph processing platform. GE supports both low-latency online query processing and high-throughput offline analytics on billion-node large graphs. Schema does matter when we need to process data efficiently. Strongly-typed data modeling is crucial for compact data storage, fast data access, and clear data semantics. GE is good at managing billions of run-time objects of varied sizes. One byte counts as the number of objects goes large. GE provides fast memory allocation and reallocation with high memory ratios. -
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AnzoGraph DB
Cambridge Semantics
With a huge collection of analytical features, AnzoGraph DB can enhance your analytical framework. Watch this video to learn how AnzoGraph DB is a Massively Parallel Processing (MPP) native graph database that is built for data harmonization and analytics. Horizontally scalable graph database built for online analytics and data harmonization. Take on data harmonization and linked data challenges with AnzoGraph DB, a market-leading analytical graph database. AnzoGraph DB provides industrialized online performance for enterprise-scale graph applications. AnzoGraph DB uses familiar SPARQL*/OWL for semantic graphs but also supports Labeled Property Graphs (LPGs). Access to many analytical, machine learning and data science capabilities help you achieve new insights, delivered at unparalleled speed and scale. Use context and relationships between data as first-class citizens in your analysis. Ultra-fast data loading and analytical queries. -
20
Sparksee
Sparsity Technologies
Sparksee (formerly known as DEX), makes space and performance compatible with a small footprint and a fast analysis of large networks. It is natively available for .Net, C++, Python, Objective-C and Java, and covers the whole spectrum of Operating Systems. The graph is represented through bitmap data structures that allow high compression rates. Each of the bitmaps is partitioned into chunks that fit into disk pages to improve I/O locality. Using bitmaps, operations are computed with binary logic instructions that simplify the execution in pipelined processors. Full native indexing allows an extremely fast access to each of the graph data structures. Node adjacencies are represented by bitmaps to minimize their footprint. The number of times each data page is brought to memory is minimized with advanced I/O policies. Each value in the database is represented only once, avoiding unnecessary replication. -
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TerminusDB
TerminusDB
Making data collaboration easy. If you are a developer looking to innovate or a data person looking for version control, we make collaboration work for everyone. TerminusDB is an open-source knowledge graph database that provides reliable, private & efficient revision control & collaboration. If you want to collaborate with colleagues or build data-intensive applications, nothing will make you more productive. TerminusDB provides the full suite of revision control features. TerminusHub allows users to manage access to databases and collaboratively work on shared resources. Flexible data storage, sharing, and versioning capabilities. Collaboration for your team or integrated into your app. Work locally then sync when you push your changes. Easy querying, cleaning, and visualization. Integrate powerful version control and collaboration for your enterprise and individual customers. Make it easy for remote data teams to work together on data projects. -
22
TIBCO Graph Database
TIBCO
To unveil the true value of constantly evolving business data, you need to understand the relationships in data in a much more profound way. Unlike other databases, a graph database puts relationships at the forefront, using Graph theory and Linear Algebra to traverse and show how complex data webs, data sources, and data points relate. TIBCO® Graph Database allows you to discover, store, and convert complex dynamic data into meaningful insights. Enable users to rapidly build data and computational models that establish dynamic relationships among organizational silos. These knowledge graphs deliver value by connecting your organization’s vast array of data and revealing relationships that let you accelerate optimization of assets and processes. Combined OLTP and OLAP features in a single enterprise-grade database. Optimistic ACID level transaction properties with native storage and access. -
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Vendia
Vendia
Vendia is a SaaS service that makes it easy for companies and organizations to share code and data across clouds, regions, accounts, and technology stacks. Vendia helps enterprises share code and data across companies, clouds, accounts, regions, and technology stacks. Vendia's unique architecture offers a distributed data model that goes everywhere you need it to, and its serverless design enables it to scale seamlessly. Vendia helps businesses create a complete portrait of their data, for example to track and trace items in a supply chain. Often that information spans business parties, such as suppliers, logistics, affiliates, and others. These might be different legal entities, different departments within the same enterprise, or even the same department but divided by their adoption of different public cloud services, such as one using AWS and another using Azure. -
24
Enlyft
Enlyft
Enlyft helps B2B companies generate better leads, close more deals, and acquire more customers - faster. Enlyfts AI-driven customer intelligence platform leverages machine learning to profile and predict the buying behavior of millions of companies worldwide, based on technology use, hundreds of business attributes, and real-time buyer intent signals. Increase sales by quickly discovering, prioritizing and engaging with prospects likely to buy your solution. Enlyft’s proprietary data platform contains real-time information on company firmographics, technology usage, buying intent signals and hundreds of additional account attributes. Leverage dedicated machine learning based models to predict future outcomes, by combining Enlyft’s comprehensive account insights with your customer history. Seamlessly integrate account insights into popular B2B Sales and Marketing platforms like Salesforce, HubSpot, Dynamics 365, LinkedIn, and more. Enrich records and keep data fresh. -
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TiMi
TIMi
With TIMi, companies can capitalize on their corporate data to develop new ideas and make critical business decisions faster and easier than ever before. The heart of TIMi’s Integrated Platform. TIMi’s ultimate real-time AUTO-ML engine. 3D VR segmentation and visualization. Unlimited self service business Intelligence. TIMi is several orders of magnitude faster than any other solution to do the 2 most important analytical tasks: the handling of datasets (data cleaning, feature engineering, creation of KPIs) and predictive modeling. TIMi is an “ethical solution”: no “lock-in” situation, just excellence. We guarantee you a work in all serenity and without unexpected extra costs. Thanks to an original & unique software infrastructure, TIMi is optimized to offer you the greatest flexibility for the exploration phase and the highest reliability during the production phase. TIMi is the ultimate “playground” that allows your analysts to test the craziest ideas! -
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IBM DataStage
IBM
Accelerate AI innovation with cloud-native data integration on IBM Cloud Pak for data. AI-powered data integration, anywhere. Your AI and analytics are only as good as the data that fuels them. With a modern container-based architecture, IBM® DataStage® for IBM Cloud Pak® for Data delivers that high-quality data. It combines industry-leading data integration with DataOps, governance and analytics on a single data and AI platform. Automation accelerates administrative tasks to help reduce TCO. AI-based design accelerators and out-of-the-box integration with DataOps and data science services speed AI innovation. Parallelism and multicloud integration let you deliver trusted data at scale across hybrid or multicloud environments. Manage the data and analytics lifecycle on the IBM Cloud Pak for Data platform. Services include data science, event messaging, data virtualization and data warehousing. Parallel engine and automated load balancing. -
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Delta Lake
Delta Lake
Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads. Data lakes typically have multiple data pipelines reading and writing data concurrently, and data engineers have to go through a tedious process to ensure data integrity, due to the lack of transactions. Delta Lake brings ACID transactions to your data lakes. It provides serializability, the strongest level of isolation level. Learn more at Diving into Delta Lake: Unpacking the Transaction Log. In big data, even the metadata itself can be "big data". Delta Lake treats metadata just like data, leveraging Spark's distributed processing power to handle all its metadata. As a result, Delta Lake can handle petabyte-scale tables with billions of partitions and files at ease. Delta Lake provides snapshots of data enabling developers to access and revert to earlier versions of data for audits, rollbacks or to reproduce experiments. -
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Rocket Data Intelligence
Rocket Software
Rocket® Data Intelligence (RDI) delivers comprehensive visibility into enterprise data across mainframe, distributed, and cloud environments. It automatically discovers metadata, lineage, and data relationships so organizations can see where critical data resides, how it moves, and which applications and processes rely on it. RDI supports legacy and modern platforms, including Db2, VSAM, IMS, Adabas, Datacom, relational databases, ETL tools like Informatica and DataStage, code such as COBOL, Python, and Java, and cloud data stores. RDI provides enterprise-grade capabilities including automated data discovery and code parsing, impact analysis, lineage filtering, role/LOB-based categorization and governance, workflow management, business glossary, and dependency mapping. By unifying data asset visibility across hybrid environments, RDI reduces operational risk and accelerates data modernization, compliance reporting, discovery, and rationalization initiatives. -
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Kylo
Teradata
Kylo is an open source enterprise-ready data lake management software platform for self-service data ingest and data preparation with integrated metadata management, governance, security and best practices inspired by Think Big's 150+ big data implementation projects. Self-service data ingest with data cleansing, validation, and automatic profiling. Wrangle data with visual sql and an interactive transform through a simple user interface. Search and explore data and metadata, view lineage, and profile statistics. Monitor health of feeds and services in the data lake. Track SLAs and troubleshoot performance. Design batch or streaming pipeline templates in Apache NiFi and register with Kylo to enable user self-service. Organizations can expend significant engineering effort moving data into Hadoop yet struggle to maintain governance and data quality. Kylo dramatically simplifies data ingest by shifting ingest to data owners through a simple guided UI. -
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Tokern
Tokern
Open source data governance suite for databases and data lakes. Tokern is a simple to use toolkit to collect, organize and analyze data lake's metadata. Run as a command-line app for quick tasks. Run as a service for continuous collection of metadata. Analyze lineage, access control and PII datasets using reporting dashboards or programmatically in Jupyter notebooks. Tokern is an open source data governance suite for databases and data lakes. Improve ROI of your data, comply with regulations like HIPAA, CCPA and GDPR and protect critical data from insider threats with confidence. Centralized metadata management of users, datasets and jobs. Powers other data governance features. Track Column Level Data Lineage for Snowflake, AWS Redshift and BigQuery. Build lineage from query history or ETL scripts. Explore lineage using interactive graphs or programmatically using APIs or SDKs.
