Alternatives to Voldemort

QuantaStor, a unified Software Defined Storage platform, is designed to scale up and down to simplify storage management and reduce overall storage costs. QuantaStor storage grids can be configured to support complex workflows that span datacenters and sites. QuantaStor's storage technology includes a built-in Federated Management System that allows QuantaStor servers and clients to be combined to make management and automation easier via CLI and RESTAPIs. QuantaStor's layered architecture gives solution engineers unprecedented flexibility and allows them to design applications that maximize workload performance and fault tolerance for a wide variety of storage workloads. QuantaStor provides end-to-end security coverage that allows multi-layer data protection for cloud and enterprise storage deployments.

RaimaDB, an embedded time series database that can be used for Edge and IoT devices, can run in-memory. It is a lightweight, secure, and extremely powerful RDBMS. It has been field tested by more than 20 000 developers around the world and has been deployed in excess of 25 000 000 times. RaimaDB is a high-performance, cross-platform embedded database optimized for mission-critical applications in industries such as IoT and edge computing. Its lightweight design makes it ideal for resource-constrained environments, supporting both in-memory and persistent storage options. RaimaDB offers flexible data modeling, including traditional relational models and direct relationships through network model sets. With ACID-compliant transactions and advanced indexing methods like B+Tree, Hash Table, R-Tree, and AVL-Tree, it ensures data reliability and efficiency. Built for real-time processing, it incorporates multi-version concurrency control (MVCC) and snapshot isolation, making it a robust solution for applications demanding speed and reliability.

When seeking a database that ensures both scalability and high availability without sacrificing performance, Apache Cassandra stands out as an ideal option. Its linear scalability paired with proven fault tolerance on standard hardware or cloud services positions it as an excellent choice for handling mission-critical data effectively. Additionally, Cassandra's superior capability to replicate data across several datacenters not only enhances user experience by reducing latency but also offers reassurance in the event of regional failures. This combination of features makes it a robust solution for organizations that prioritize data resilience and efficiency.

FairCom DB is ideal to handle large-scale, mission critical core-business applications that demand performance, reliability, and scalability that cannot easily be achieved with other databases. FairCom DB provides predictable high-velocity transactions with big data analytics and massively parallel big-data processing. It provides developers with NoSQL APIs that allow them to process binary data at machine speed. ANSI SQL allows for simple queries and analysis over the same binary data. Verizon is one of the companies that has taken advantage of FairCom DB's flexibility. Verizon recently selected FairCom DB to be its in-memory database for the Verizon Intelligent Network Control Platform Transaction Server Migrating. FairCom DB, an advanced database engine, gives you a Continuum of Control that allows you to achieve unparalleled performance at a low total cost of ownership (TCO). FairCom DB doesn't conform to you. FairCom DB conforms. FairCom DB doesn't force you to conform to the database's limitations.

Greetings! BergDB is an efficient database built on Java and .NET, crafted for developers who want to concentrate on their tasks without getting bogged down by database complexities. It features straightforward key-value storage, ACID-compliant transactions, the ability to perform historic queries, effective concurrency management, secondary indices, swift append-only storage, replication capabilities, and seamless object serialization among other attributes. As an embedded, open-source, document-oriented, schemaless NoSQL database, BergDB is purposefully designed to deliver rapid transaction processing. Importantly, it ensures that all database writes adhere to ACID transactions, maintaining the highest consistency level available, which is akin to the serializable isolation level in SQL. The functionality of historic queries is beneficial for retrieving previous data states and managing concurrency efficiently, as read operations in BergDB are executed without locking any resources, allowing for smooth and uninterrupted access to data. This unique approach ensures that developers can work more productively, leveraging BergDB’s robust features to enhance application performance.

Couchbase distinguishes itself from other NoSQL databases by delivering an enterprise-grade, multicloud to edge solution that is equipped with the powerful features essential for mission-critical applications on a platform that is both highly scalable and reliable. This distributed cloud-native database operates seamlessly in contemporary dynamic settings, accommodating any cloud environment, whether it be customer-managed or a fully managed service. Leveraging open standards, Couchbase merges the advantages of NoSQL with the familiar structure of SQL, thereby facilitating a smoother transition from traditional mainframe and relational databases. Couchbase Server serves as a versatile, distributed database that integrates the benefits of relational database capabilities, including SQL and ACID transactions, with the adaptability of JSON, all built on a foundation that is remarkably fast and scalable. Its applications span various industries, catering to needs such as user profiles, dynamic product catalogs, generative AI applications, vector search, high-speed caching, and much more, making it an invaluable asset for organizations seeking efficiency and innovation.

Infinispan is an open-source, in-memory data grid that provides versatile deployment possibilities and powerful functionalities for data storage, management, and processing. This technology features a key/value data repository capable of accommodating various data types, ranging from Java objects to simple text. Infinispan ensures high availability and fault tolerance by distributing data across elastically scalable clusters, making it suitable for use as either a volatile cache or a persistent data solution. By positioning data closer to the application logic, Infinispan enhances application performance through reduced latency and improved throughput. As a Java library, integrating Infinispan into your project is straightforward; all you need to do is include it in your application's dependencies, allowing you to efficiently manage data within the same memory environment as your executing code. Furthermore, its flexibility makes it an ideal choice for developers seeking to optimize data access in high-demand scenarios.

LeanXcale is a rapidly scalable database that merges the features of both SQL and NoSQL systems. It is designed to handle large volumes of both batch and real-time data pipelines, ensuring that this data is accessible through SQL or GIS for diverse applications, including operational tasks, analytics, dashboard creation, or machine learning processes. Regardless of the technology stack in use, LeanXcale offers users the flexibility of SQL and NoSQL interfaces. The KiVi storage engine functions as a relational key-value data repository, enabling data access not only via the conventional SQL API but also through a direct ACID-compliant key-value interface. This particular interface facilitates high-speed data ingestion, optimizing efficiency by eliminating the overhead associated with SQL processing. Furthermore, its highly scalable and distributed storage engine spreads data across the cluster, thereby enhancing both performance and reliability while accommodating growing data needs seamlessly.

Esper Enterprise Edition offers a robust platform designed for both linear and elastic scalability, as well as reliable event processing that can withstand faults. It comes equipped with an EPL editor and debugger, supports hot deployment, and provides comprehensive reporting on metrics and memory usage, including detailed breakdowns per EPL. Additionally, it features Data Push capabilities for seamless multi-tier delivery from CEP to browsers and manages both logical and physical subscribers and their subscriptions effectively. Its web-based user interface allows users to oversee various distributed engine instances using JavaScript and HTML5, while also enabling the creation of composable and interactive displays for visualizing distributed event streams through charts, gauges, timelines, and grids. Furthermore, it includes JDBC-compliant client and server endpoints to ensure interoperability across systems. Notably, Esper Enterprise Edition is a proprietary commercial product developed by EsperTech, with source code accessibility granted solely for the support of customers. Such versatility and functionality make it a robust choice for enterprises seeking efficient event processing solutions.

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.

LevelDB is a high-performance key-value storage library developed by Google, designed to maintain an ordered mapping between string keys and string values. The keys and values are treated as arbitrary byte arrays, and the stored data is organized in a sorted manner based on the keys. Users have the option to supply a custom comparison function to modify the default sorting behavior. The library allows for multiple changes to be grouped into a single atomic batch, ensuring data integrity during updates. Additionally, users can create a temporary snapshot for a consistent view of the data at any given moment. The library supports both forward and backward iteration through the stored data, enhancing flexibility during data access. Data is automatically compressed using the Snappy compression algorithm to optimize storage efficiency. Moreover, interactions with the external environment, such as file system operations, are managed through a virtual interface, giving users the ability to customize how the library interacts with the operating system. In practical applications, we utilize a database containing one million entries, where each entry consists of a 16-byte key and a 100-byte value. Notably, the values used in benchmarking compress to approximately half of their original size, allowing for significant space savings. We provide detailed performance metrics for sequential reads in both forward and reverse directions, as well as the effectiveness of random lookups, to showcase the library's capabilities. This comprehensive performance analysis aids developers in understanding how to optimize their use of LevelDB in various applications.

ApsaraDB for Redis is a highly automated and scalable solution designed for developers to efficiently manage shared data storage across various applications, processes, or servers. Compatible with the Redis protocol, this tool boasts impressive read-write performance and guarantees data persistence by utilizing both memory and hard disk storage options. By accessing data from in-memory caches, ApsaraDB for Redis delivers rapid read-write capabilities while ensuring that data remains reliable and persistent through its dual storage modes. It also supports sophisticated data structures like leaderboards, counters, sessions, and tracking, which are typically difficult to implement with standard databases. Additionally, ApsaraDB for Redis features an enhanced version known as "Tair." Tair has been effectively managing data caching for Alibaba Group since 2009, showcasing remarkable performance during high-demand events like the Double 11 Shopping Festival, further solidifying its reputation in the field. This makes ApsaraDB for Redis and Tair invaluable tools for developers looking to optimize data handling in large-scale applications.

NoSQL refers to a specialized programming language designed for interacting with, managing, and altering non-tabular database systems. This type of database, which stands for "non-SQL" or "non-relational," allows for data storage and retrieval through structures that differ from the traditional tabular formats found in relational databases. Although such databases have been around since the late 1960s, the term "NoSQL" only emerged in the early 2000s as a response to the evolving demands of Web 2.0 applications. These databases have gained popularity for handling big data and supporting real-time web functionalities. Often referred to as Not Only SQL, NoSQL systems highlight their capability to accommodate SQL-like query languages while coexisting with SQL databases in hybrid architectures. Many NoSQL solutions prioritize availability, partition tolerance, and performance over strict consistency, as outlined by the CAP theorem. Despite their advantages, the broader acceptance of NoSQL databases is hindered by the necessity for low-level query languages that may pose challenges for users. As the landscape of data management continues to evolve, the role of NoSQL databases is likely to expand even further.

etcd serves as a highly reliable and consistent distributed key-value store, ideal for managing data required by a cluster or distributed system. It effectively manages leader elections amidst network splits and is resilient to machine failures, including those affecting the leader node. Data can be organized in a hierarchical manner, similar to a traditional filesystem, allowing for structured storage. Additionally, it offers the capability to monitor specific keys or directories for changes, enabling real-time reactions to any alterations in values, ensuring that systems stay synchronized and responsive. This functionality is crucial for maintaining consistency across distributed applications.

SQLAlchemy serves as a Python toolkit for SQL and an object-relational mapper, allowing developers to harness the complete capabilities of SQL with great flexibility. As the size and performance of SQL databases become critical, they tend to deviate from functioning merely as object collections; similarly, when abstraction is prioritized, object collections lose their resemblance to traditional tables and rows. SQLAlchemy seeks to bridge these opposing principles effectively. It views the database as a relational algebra engine rather than simply a set of tables, enabling selection of rows not only from tables but also from joins and various select statements, which can be integrated into more complex structures. The expression language of SQLAlchemy is built upon this foundational idea, enhancing its functionality. Additionally, SQLAlchemy is widely recognized for its object-relational mapper (ORM) feature, which is an optional element that implements the data mapper pattern, providing a robust framework for developers to work with databases seamlessly. This dual functionality of SQLAlchemy makes it a versatile tool for both simple and intricate database interactions.

FoundationDB operates as a multi-model database, enabling the storage of various data types within a single system. Its Key-Value Store component ensures that all information is securely stored, distributed, and replicated. The installation, scaling, and management of FoundationDB are straightforward, benefiting from a distributed architecture that effectively scales and handles failures while maintaining the behavior of a singular ACID database. It delivers impressive performance on standard hardware, making it capable of managing substantial workloads at a minimal cost. With years of production use, FoundationDB has been reinforced through practical experience and insights gained over time. Additionally, its backup system is unparalleled, utilizing a deterministic simulation engine for testing purposes. We invite you to become an active member of our open-source community, where you can engage in both technical and user discussions on our forums and discover ways to contribute to the project. Your involvement can help shape the future of FoundationDB!

InterSystems Cache®, a high-performance database, powers transaction processing applications all over the globe. It's used for everything, from mapping a million stars in the Milky Way to processing a trillion equity trades per day to managing smart energy grids. InterSystems has developed Cache, a multi-model (object-relational, key-value), DBMS and application server. InterSystems Cache offers multiple APIs that allow you to work with the same data simultaneously: key/value, relational/object, document, multidimensional, object, object, and object. Data can be managed using SQL, Java, node.js.NET, C++ and Python. Cache also offers an application server that hosts web apps (CSP, REST, SOAP and other types TCP access for Cache data).

InterSystems IRIS, a cloud-first data platform, is a multi-model transactional database management engine, application development platform, interoperability engine and open analytics platform. InterSystems IRIS offers a variety of APIs that allow you to work with transactional persistent data simultaneously. These include key-value, relational and object, document, and multidimensional. Data can be managed by SQL, Java, node.js, .NET, C++, Python, and native server-side ObjectScript language. InterSystems IRIS features an Interoperability engine as well as modules for building AI solutions. InterSystems IRIS features horizontal scalability (sharding and ECP), and High Availability features such as Business intelligence, transaction support and backup.

An innovative and more secure approach to developing fault-tolerant cloud applications is offered through the groundbreaking cloud-native DBOS operating system. Drawing from three years of collaborative open-source research and development between MIT and Stanford, DBOS transforms the landscape of cloud-native architecture. This cloud-native operating system leverages a relational database to significantly streamline the intricate application stacks commonly found today. DBOS underpins DBOS Cloud, which serves as a transactional serverless platform that ensures fault tolerance, observability, cyber resilience, and straightforward deployment for stateful TypeScript applications. The services of the operating system are built upon a distributed database management system, featuring integrated transactional and fault-tolerant state management that reduces complexity by eliminating the need for containers, cluster management, or workflow orchestration. It offers seamless scalability, outstanding performance, and consistent availability, while metrics, logs, and traces are conveniently stored in SQL-accessible tables. Additionally, the architecture minimizes the cyber attack surface, incorporates self-detection mechanisms for cyber threats, and enhances overall cyber resilience, making it a robust choice for modern cloud applications. Overall, the DBOS operating system represents a significant leap forward in simplifying cloud application development while ensuring high security and reliability.

Symas LMDB is an incredibly swift and memory-efficient database that we created specifically for the OpenLDAP Project. Utilizing memory-mapped files, it achieves the read speed typical of purely in-memory databases while also providing the durability associated with traditional disk-based systems. In essence, despite its modest size of just 32KB of object code, LMDB packs a significant punch; it is indeed the perfect 32KB. The compact nature and efficiency of LMDB are integral to its remarkable capabilities. For those integrating LMDB into their applications, Symas provides fixed-price commercial support. Development is actively carried out in the mdb.master branch of the OpenLDAP Project’s git repository. Moreover, LMDB has garnered attention across numerous impressive products and publications, highlighting its versatility and effectiveness in various contexts. Its widespread recognition further cements its status as a vital tool for developers.

rqlite is a lightweight and easy-to-use distributed relational database that leverages SQLite’s capabilities. It offers high availability and fault tolerance without the usual complexities. By merging SQLite's user-friendly design with a reliable, robust system, rqlite stands out as a developer-oriented solution. Its straightforward operations ensure that users can deploy it in mere seconds, avoiding intricate configurations. The database effortlessly fits into modern cloud environments and is built on SQLite, which is recognized as the most widely used database globally. It features full-text search, Vector Search, and support for JSON documents, catering to various data needs. Enhanced security is provided through access controls and encryption for secure deployments. The platform benefits from rigorous automated testing processes that guarantee its quality. Clustering capabilities further enhance its availability and fault tolerance, while automatic node discovery streamlines the clustering process, making it even more user-friendly. This combination of features makes rqlite an ideal choice for developers looking for simplicity without sacrificing reliability.

Oracle's database offerings provide clients with cost-effective and high-efficiency options, including the renowned multi-model database management system, as well as in-memory, NoSQL, and MySQL databases. The Oracle Autonomous Database, which can be accessed on-premises through Oracle Cloud@Customer or within the Oracle Cloud Infrastructure, allows users to streamline their relational database systems and lessen management burdens. By removing the intricacies associated with operating and securing Oracle Database, Oracle Autonomous Database ensures customers experience exceptional performance, scalability, and reliability. Furthermore, organizations concerned about data residency and network latency can opt for on-premises deployment of Oracle Database. Additionally, clients who rely on specific versions of Oracle databases maintain full authority over their operational versions and the timing of any updates. This flexibility empowers businesses to tailor their database environments according to their unique requirements.

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.

Upscaledb is a high-speed key-value database that enhances storage efficiency and algorithms based on the unique characteristics of your data. It features optional compression that minimizes both file size and input/output operations, allowing for more data to reside in memory, which boosts performance and scalability during extensive table scans for querying and analyzing information. Upscaledb is capable of supporting all functionalities typical of a conventional SQL database, customized to align with the specific requirements of your application, and can be seamlessly integrated into your software. With its incredibly swift analytical capabilities and efficient database cursors, it serves as an ideal solution for processing data in scenarios where traditional SQL databases may falter in speed. This versatile database has found its applications across tens of millions of desktops, as well as on cloud servers, mobile devices, and various embedded systems. In a specific benchmark, a comprehensive table scan was conducted over 50 million records, yielding the highest retrieval speed, with the records set up as uint32 values, showcasing its remarkable efficiency. Furthermore, this performance highlights the potential of upscaledb to handle large datasets with ease, making it a preferred choice for developers seeking optimal data management solutions.

What makes eXtremeDB platform independent? - Hybrid storage of data. Unlike other IMDS databases, eXtremeDB databases are all-in-memory or all-persistent. They can also have a mix between persistent tables and in-memory table. eXtremeDB's Active Replication Fabric™, which is unique to eXtremeDB, offers bidirectional replication and multi-tier replication (e.g. edge-to-gateway-to-gateway-to-cloud), compression to maximize limited bandwidth networks and more. - Row and columnar flexibility for time series data. eXtremeDB supports database designs which combine column-based and row-based layouts in order to maximize the CPU cache speed. - Client/Server and embedded. eXtremeDB provides data management that is fast and flexible wherever you need it. It can be deployed as an embedded system and/or as a clients/server database system. eXtremeDB was designed for use in resource-constrained, mission-critical embedded systems. Found in over 30,000,000 deployments, from routers to satellites and trains to stock market world-wide.

OrigoDB allows you to create high-quality, mission-critical systems that deliver real-time performance while significantly reducing time and costs. This is not just empty advertising! We encourage you to continue reading for a straightforward overview of our features. Feel free to reach out with any inquiries or download and give it a try today! Operations performed in memory are significantly quicker than those conducted on disk. An individual OrigoDB engine is capable of processing millions of read transactions and thousands of write transactions per second, all while ensuring synchronous command journaling to a local SSD. This capability is the primary motivation behind the development of OrigoDB. Utilizing a single object-oriented domain model is much more efficient than managing an entire stack that encompasses a relational model, object/relational mapping, data access code, views, and stored procedures, which can lead to unnecessary complexity! Furthermore, the OrigoDB engine provides complete ACID compliance right from the start. Commands are executed sequentially, allowing the in-memory model to transition smoothly from one consistent state to another, ensuring data integrity at all times. This streamlined approach ultimately enhances overall system performance and reliability.

LedisDB is a fast NoSQL database system and library developed in Go. While it shares similarities with Redis, it distinguishes itself by storing data on disk. The library accommodates various data structures such as key-value pairs, lists, hashes, sorted sets, and sets. Additionally, LedisDB has evolved to support multiple backend databases, enhancing its versatility and usability for different applications. This makes it an appealing choice for developers seeking efficient data storage solutions.

AWS Database Migration Service enables swift and secure database migrations to the AWS platform. During this process, the source database continues its operations, which effectively reduces downtime for applications that depend on it. This service is capable of transferring data to and from many of the most popular commercial and open-source databases available today. It facilitates both homogeneous migrations, like Oracle to Oracle, and heterogeneous migrations, such as transitioning from Oracle to Amazon Aurora. The service supports migrations from on-premises databases to Amazon Relational Database Service (Amazon RDS) or Amazon Elastic Compute Cloud (Amazon EC2), as well as transfers between EC2 and RDS, or even from one RDS instance to another. Additionally, it can handle data movement across various types of databases, including SQL, NoSQL, and text-based systems, ensuring versatility in data management. Furthermore, this capability allows businesses to optimize their database strategies while maintaining operational continuity.

IBM Cloud Databases serve as open source data repositories tailored for the development of enterprise applications. Leveraging a Kubernetes-based architecture, these databases support serverless application development. They are engineered to enhance storage and computing capabilities effortlessly, free from the restrictions typically imposed by single server environments. Fully integrated within the IBM Cloud console, they offer a unified approach to consumption, pricing, and user interaction. The goal is to deliver a streamlined experience for developers, encompassing features such as access control, backup orchestration, encryption key management, and comprehensive auditing, monitoring, and logging functionalities. This cohesive framework not only enhances usability but also ensures that developers can focus on building innovative solutions without worrying about underlying infrastructure constraints.

Do you appreciate your database but find the data replication tools frustrating? You might feel trapped in a situation where you are spending a lot on management packs and add-ons that fail to provide the essential features you require. However, imagine being able to fulfill your database objectives without relying on the native tools. This could allow you to reallocate funds into innovative strategies that propel your business forward. With SharePlex®, you can replicate Oracle data for a much lower cost than native alternatives. This solution enables you to easily achieve high availability, enhance scalability, integrate data, and offload reporting, all with a comprehensive tool that your database vendor might not want you to discover. By choosing affordable database replication software, you can prioritize moving your data over stretching your budget. As companies face growing demands to extract greater value from their data while minimizing expenses, DBAs are tasked with ensuring that database operations run efficiently while maintaining data resilience through high availability (HA) and disaster recovery (DR) strategies. This balance is crucial for meeting organizational goals and maintaining competitive advantage.

Berkeley DB encompasses a suite of embedded key-value database libraries that deliver scalable and high-performance data management functionalities for various applications. Its products utilize straightforward function-call APIs for accessing and managing data efficiently. With Berkeley DB, developers can create tailored data management solutions that bypass the typical complexities linked with custom projects. The library offers a range of reliable building-block technologies that can be adapted to meet diverse application requirements, whether for handheld devices or extensive data centers, catering to both local storage needs and global distribution, handling data volumes that range from kilobytes to petabytes. This versatility makes Berkeley DB a preferred choice for developers looking to implement efficient data solutions.

RocksDB is a high-performance database engine that employs a log-structured design and is entirely implemented in C++. It treats keys and values as byte streams of arbitrary sizes, allowing for flexibility in data representation. Specifically designed for rapid, low-latency storage solutions such as flash memory and high-speed disks, RocksDB capitalizes on the impressive read and write speeds provided by these technologies. The database supports a range of fundamental operations, from basic tasks like opening and closing a database to more complex functions such as merging and applying compaction filters. Its versatility makes RocksDB suitable for various workloads, including database storage engines like MyRocks as well as application data caching and embedded systems. This adaptability ensures that developers can rely on RocksDB for a wide spectrum of data management needs in different environments.

IBM Storage Protect serves as a robust solution for data backup and recovery. It is integrated within the software-defined storage offerings of the IBM Storage suite. For those seeking backup and recovery solutions tailored specifically for SaaS applications, Storage Protect for Cloud is available. Formerly known as IBM Spectrum Protect, IBM Storage Protect ensures extensive data resilience across physical file servers, virtual platforms, and a diverse array of applications. Organizations have the capability to expand and manage billions of objects on each backup server. Clients benefit from reduced costs associated with backup infrastructure, thanks to inherent data efficiency features and options for data migration or copying to various destinations, including tape, public cloud services, and local object storage. Additionally, IBM Storage Protect can accommodate data from IBM Storage Protect Plus, enabling businesses to leverage their prior investments for sustained data retention and effective disaster recovery strategies. This flexibility and integration make IBM Storage Protect a versatile choice for modern data management needs.

When PostgreSQL serves as the foundation of your enterprise and is a key data source, PeerDB offers an efficient, straightforward, and economical solution for replicating data from PostgreSQL to data warehouses, queues, and storage systems. It is engineered to function seamlessly at any scale and is specifically adapted for various data repositories. By utilizing replication messages sourced from the PostgreSQL replication slot, PeerDB adeptly replays schema updates while providing alerts for slot growth and active connections. It also includes native support for PostgreSQL toast columns and large JSONB columns, making it particularly advantageous for IoT applications. The platform features an optimized query architecture aimed at minimizing warehouse expenditures, which is especially beneficial for users of Snowflake and BigQuery. Additionally, it accommodates partitioned tables through both publication mechanisms. PeerDB ensures rapid and reliable initial data loads via transaction snapshotting and CTID scanning techniques. With features such as high availability, in-place upgrades, autoscaling, advanced logging, comprehensive metrics, and monitoring dashboards, as well as burstable instance types, it is also well-suited for development environments. Overall, PeerDB stands out as a versatile tool that effectively meets the diverse needs of modern data management.

Amazon Aurora is a cloud-based relational database that is compatible with both MySQL and PostgreSQL, merging the high performance and reliability of traditional enterprise databases with the ease and affordability of open-source solutions. Its performance surpasses that of standard MySQL databases by as much as five times and outpaces standard PostgreSQL databases by three times. Additionally, it offers the security, availability, and dependability synonymous with commercial databases, all at a fraction of the cost—specifically, one-tenth. Fully managed by the Amazon Relational Database Service (RDS), Aurora simplifies operations by automating essential tasks such as hardware provisioning, database configuration, applying patches, and conducting backups. The database boasts a self-healing, fault-tolerant storage system that automatically scales to accommodate up to 64TB for each database instance. Furthermore, Amazon Aurora ensures high performance and availability through features like the provision of up to 15 low-latency read replicas, point-in-time recovery options, continuous backups to Amazon S3, and data replication across three distinct Availability Zones, which enhances data resilience and accessibility. This combination of features makes Amazon Aurora an appealing choice for businesses looking to leverage the cloud for their database needs while maintaining robust performance and security.

Dqlite is a high-speed, embedded SQL database that offers persistent storage and utilizes Raft consensus, making it an ideal choice for resilient IoT and Edge devices. Known as "distributed SQLite," Dqlite expands SQLite's capabilities across multiple machines, ensuring automatic failover and high availability to maintain application uptime. It employs C-Raft, an optimized implementation of Raft in C, which provides exceptional performance in transactional consensus and fault tolerance while maintaining SQLite’s renowned efficiency and compact size. C-Raft is specifically designed to reduce transaction latency, enabling faster operations. Both C-Raft and Dqlite are implemented in C, ensuring they are portable across various platforms. Released under the LGPLv3 license with a static linking exception, it guarantees broad compatibility. The system features a standard CLI pattern for initializing databases and managing the joining or leaving of voting members. It also incorporates minimal, configurable delays for failover alongside automatic leader election processes. Additionally, Dqlite supports a disk-backed database option with in-memory capabilities and adheres to SQLite's transaction protocols. The blend of these features makes Dqlite a powerful solution for modern data storage needs.

Stelo is a comprehensive enterprise solution designed to seamlessly transfer data from any source to any destination for purposes such as analysis, reporting, forecasting, and overseeing business operations, B2B exchanges, and supply chain management. It enables effortless data movement among core relational databases and delta lakes in real-time, even across firewalls, ensuring accessibility for various teams and cloud platforms. The Stelo Data Replicator offers dependable, high-speed, cost-effective replication capabilities for any relational database that can be accessed via ODBC, as well as non-relational databases utilizing Kafka, Delta Lakes, and flat file formats. By utilizing native data loading functions and taking advantage of multithreaded processing, Stelo ensures rapid and consistent performance when replicating multiple tables at the same time. With an intuitive installation process that features graphical user interfaces, configuration wizards, and sophisticated tools, setting up and operating the product is simple and requires no programming expertise. Once operational, Stelo runs reliably in the background, eliminating the need for dedicated engineering resources for its maintenance and management. Not only does this streamline operations, but it also allows organizations to focus on leveraging their data effectively.

Nowadays, many organizations are increasingly utilizing object and cloud storage to hold unstructured data, in addition to traditional file systems. The benefits of cloud and object storage, especially for inactive data, have prompted a significant migration or replication of files from legacy NAS systems to these modern solutions. This shift has resulted in a growing amount of data being housed in cloud and object storage; however, it has also introduced an often-overlooked security vulnerability. Typically, the data stored in cloud services or on-premises object storage remains unbacked up due to the common misconception that it is inherently secure. Such an assumption is both negligent and fraught with risk, as the high availability and redundancy provided by these services do not safeguard against issues like human error, ransomware attacks, malware infections, or technology failures. Therefore, it is crucial to implement backup or replication strategies for data kept in cloud and object storage, ideally using a different storage technology located elsewhere, and retaining the original format as it exists in the cloud. By doing so, organizations can enhance their data protection measures and mitigate potential threats to their valuable information.

Google Cloud Bigtable provides a fully managed, scalable NoSQL data service that can handle large operational and analytical workloads. Cloud Bigtable is fast and performant. It's the storage engine that grows with your data, from your first gigabyte up to a petabyte-scale for low latency applications and high-throughput data analysis. Seamless scaling and replicating: You can start with one cluster node and scale up to hundreds of nodes to support peak demand. Replication adds high availability and workload isolation to live-serving apps. Integrated and simple: Fully managed service that easily integrates with big data tools such as Dataflow, Hadoop, and Dataproc. Development teams will find it easy to get started with the support for the open-source HBase API standard.

It facilitates device connectivity through widely accepted IoT protocols such as MQTT, CoAP, and HTTP, accommodating both cloud and local installations. ThingsBoard is engineered for scalability, reliability, and high performance, ensuring that your data remains secure and intact. You can provision, monitor, and manage your IoT devices securely by utilizing comprehensive server-side APIs. Establish connections among your devices, assets, customers, or other entities with ease. Efficiently gather and archive telemetry data in a scalable and resilient manner. You can visualize your data using either built-in or personalized widgets and adaptable dashboards, which can also be shared with clients. The platform allows you to create data processing rule chains, enabling you to transform and standardize your device data. It can trigger alerts based on incoming telemetry events, updates to attributes, periods of device inactivity, and user interactions. Build a ThingsBoard cluster to achieve maximum scalability and fault tolerance through a microservices architecture. Furthermore, ThingsBoard accommodates both cloud and on-premises setups, making it a versatile choice for various deployment needs. This flexibility ensures that users can tailor their IoT solutions according to specific requirements and preferences.

Terracotta DB offers a robust, distributed solution for in-memory data management, addressing both caching and operational storage needs while facilitating both transactional and analytical processes. The combination of swift RAM capabilities with extensive data resources empowers businesses significantly. With BigMemory, users benefit from: immediate access to vast amounts of in-memory data, impressive throughput paired with consistently low latency, compatibility with Java®, Microsoft® .NET/C#, and C++ applications, and an outstanding 99.999% uptime. The system boasts linear scalability, ensuring data consistency across various servers, and employs optimized data storage strategies across both RAM and SSDs. Additionally, it provides SQL support for in-memory data queries, lowers infrastructure expenses through enhanced hardware efficiency, and guarantees high-performance, persistent storage that ensures durability and rapid restarts. Comprehensive monitoring, management, and control features are included, alongside ultra-fast data stores that intelligently relocate data as needed. Furthermore, the capacity for data replication across multiple data centers enhances disaster recovery capabilities, enabling real-time management of dynamic data flows. This suite of features positions Terracotta DB as an essential asset for enterprises striving for efficiency and reliability in their data operations.

Effortlessly handle intricate models with ArcadeDB while ensuring no compromises are made. Say goodbye to the concept of Polyglot Persistence; there's no need to juggle multiple databases. With ArcadeDB's Multi-Model database, you can seamlessly store graphs, documents, key values, and time series data in one unified solution. As each model is inherently compatible with the database engine, you can avoid the delays caused by translation processes. Powered by advanced Alien Technology, ArcadeDB's engine can process millions of records every second. Notably, the speed of data traversal remains constant regardless of the database's size, whether it houses a handful of records or billions. ArcadeDB is versatile enough to function as an embedded database on a single server and can easily scale across multiple servers using Kubernetes. Its compact design allows it to operate on any platform while maintaining a minimal footprint. Your data's security is paramount; our robust, fully transactional engine guarantees durability for mission-critical production databases. Additionally, ArcadeDB employs a Raft Consensus Algorithm to ensure consistency and reliability across multiple servers, making it a top choice for data management. In an era where efficiency and reliability are crucial, ArcadeDB stands out as a comprehensive solution for diverse data storage needs.

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.

At the core of our innovative approach lies in-data computing, a cutting-edge technology aimed at efficiently processing substantial volumes of data. Our leading product, BigObject, is a prime example of this technology; it is a time series database purposefully created to enable rapid storage and management of vast data sets. Leveraging in-data computing, BigObject has the capability to swiftly and continuously address diverse data streams without interruption. This time series database excels in both high-speed storage and data analysis, showcasing remarkable performance alongside robust complex query functionalities. By transitioning from a traditional relational data structure to a time-series model, it harnesses in-data computing to enhance overall database efficiency. The foundation of our technology is an abstract model, wherein all data resides within an infinite and persistent memory space, facilitating seamless storage and computation. This unique architecture not only optimizes performance but also paves the way for future advancements in data processing capabilities.

VictoriaMetrics is a cost-effective, scalable monitoring solution that can also be used as a time series database. It can also be used to store Prometheus' long-term data. VictoriaMetrics is a single executable that does not have any external dependencies. All configuration is done using explicit command-line flags and reasonable defaults. It provides global query view. Multiple Prometheus instances, or other data sources, may insert data into VictoriaMetrics. Later this data may be queried via a single query. It can handle high cardinality and high churn rates issues by using a series limiter.