- Lightweight Database Management Tool – A single PHP file acts as a full-featured database management interface.
- Supports Multiple Databases – Compatible with MySQL, PostgreSQL, SQLite, MS SQL, and Oracle.
- Secure Alternative to phpMyAdmin – Offers better performance and security in a compact form.
- Portable and Easy to Deploy – No installation needed; just upload and access through a web browser.
- Efficient for Quick Admin Tasks – Ideal for small-scale web projects or developers needing fast database access.
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- No-Code Database Platform – Combines the simplicity of spreadsheets with the power of relational databases.
- Supports Collaboration and Automation – Allows users to share, sync, and automate workflows via scripts and integrations.
- Rich Field Types and Views – Includes attachments, checkboxes, dropdowns, kanban boards, calendars, and more.
- APIs and App Integrations – Easily integrates with platforms like Slack, Zapier, and external systems via REST API.
- Great for Lightweight Business Applications – Popular for project management, content calendars, and CRM solutions.
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- Enterprise Content Management (ECM) – Offers structured storage of business documents and workflows.
- Supports DBMS Backends – Works with relational databases like PostgreSQL, MySQL, and Oracle for metadata storage.
- Document Lifecycle Control – Features versioning, auditing, permissions, and retention policies.
- Integration with Business Apps – Connects to Microsoft Office, SharePoint, and Google Drive.
- Used for Compliance and Records Management – Suitable for enterprise-level digital content governance.
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- High-Performance Graph Database – Supports RDF and OWL for semantic web applications and linked data.
- Uses SPARQL for Queries – Provides rich query capabilities over RDF triples and reasoning support.
- AI and Knowledge Graph Ready – Enables building intelligent applications using ontologies and inference.
- Supports GeoTemporal Data – Handles geospatial and temporal relationships, essential for complex datasets.
- Scales with Multi-Server Clustering – Designed for enterprise-level deployment in knowledge-driven domains.
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- Fully Managed Relational Database – Compatible with MySQL and PostgreSQL, but offers improved performance.
- High Availability and Fault Tolerance – Replicates data across multiple AZs and auto-repairs storage.
- Auto-Scaling and Serverless Option – Dynamically adjusts compute capacity based on workload.
- Integrated with AWS Ecosystem – Works seamlessly with Lambda, S3, CloudWatch, and more.
- Designed for Enterprise-Grade Applications – Ideal for high-throughput OLTP and SaaS workloads.
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- Fully Managed NoSQL Database – Key-value and document model for fast and predictable performance.
- Built for Scale – Automatically scales throughput and storage based on traffic and size.
- Microsecond Latency with DAX – DynamoDB Accelerator (DAX) enables in-memory caching for ultra-low latency.
- Integrated Security and IAM – Offers fine-grained access control, encryption, and monitoring.
- Ideal for Real-Time Applications – Frequently used in gaming, IoT, mobile apps, and serverless architectures.
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- Managed Cassandra-Compatible Service – Allows running Apache Cassandra workloads without managing servers.
- Scalable and Highly Available – Automatically handles replication, partitions, and scaling.
- CQL-Based Access – Uses Cassandra Query Language (CQL) for interacting with data.
- Serverless Architecture – No provisioning required; users pay only for read/write throughput and storage.
- Best for Time-Series and Telemetry Data – Common in apps needing fast writes and fault tolerance.
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- Purpose-Built Graph Database – Supports both property graph (Gremlin) and RDF (SPARQL) models.
- Used for Knowledge Graphs and Social Networks – Great for applications requiring complex relationship modeling.
- High Performance and Fully Managed – Offers fast queries with built-in backup and failover.
- Secure and Compliant – Encryption at rest and in transit, VPC integration, and audit logging.
- Commonly Used in AI/ML Systems – Supports deep link analysis, fraud detection, and recommendation engines.
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- Sorted, Distributed Key/Value Store – Built on Apache Hadoop, Zookeeper, and Thrift for scalability.
- Cell-Level Access Control – Fine-grained security tagging for sensitive data classification.
- Supports Large-Scale Analytics – Designed for massive-scale data ingestion and querying.
- Flexible Schema with Iterators – Allows custom logic during scans for filtering, transformation, etc.
- Used in Government and Defense – Suited for high-security, high-ingestion environments.
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- Highly Scalable NoSQL Database – Optimized for high availability and horizontal scalability.
- Peer-to-Peer Architecture – Eliminates single points of failure and supports multi-data center deployment.
- Column-Family Data Model – Enables efficient storage of wide-row and time-series data.
- Eventual Consistency with Tunable Levels – Allows trade-offs between consistency and availability per query.
- Ideal for IoT, Logs, and Messaging Systems – Used by Netflix, Instagram, and others for large-scale deployments.
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- Real-Time Analytics Engine: Optimized for high-performance OLAP (Online Analytical Processing) queries.
- Column-Oriented Storage: Enables fast aggregation and filtering on large-scale time-series data.
- Hybrid Architecture: Supports both streaming and batch ingestion using Apache Kafka or Hadoop.
- Used in Dashboards: Ideal for powering BI tools with sub-second response times.
- Scalable & Distributed: Designed to scale horizontally across multiple nodes for large datasets.
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- Columnar Storage for Hadoop: Enables real-time analytics on fast-changing data.
- Hybrid Workloads: Supports both fast inserts/updates and analytics—bridging OLTP and OLAP.
- Seamless with Apache Impala: Works closely with Impala for interactive SQL queries.
- Efficient for Time-Series: Useful in log analytics, financial data processing, and machine metrics.
- Strong Consistency Model: Offers ACID-compliant transactions on a distributed system.
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- Real-Time OLAP Database: Designed for low-latency analytics on event data streams.
- Used at Scale: Deployed by companies like LinkedIn for user-facing analytics dashboards.
- Ingests from Kafka: Built-in support for real-time ingestion from Kafka and batch from Hadoop.
- Pluggable Architecture: Flexible indexing, storage, and query execution engines.
- Scalable Query Performance: Supports thousands of queries per second over billions of records.
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- Multi-Model Database: Supports key-value, document, and graph data models in one engine.
- AQL (Arango Query Language): Powerful query syntax combining SQL-like and graph queries.
- Graph Capabilities: Suitable for recommendation systems, social networks, and fraud detection.
- Horizontal Scalability: Features native sharding and replication for large deployments.
- Open Source with Enterprise Option: Free core with optional enterprise-grade features.
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- Massively Parallel Processing (MPP): Designed for big data analytics across distributed systems.
- Advanced Analytics Engine: Supports SQL, MapReduce, and in-database analytics with machine learning.
- Teradata Integration: Commonly paired with Teradata ecosystems for deep analytical workloads.
- Extensible Functions: Offers built-in analytical functions for text, graph, and geospatial data.
- Enterprise-Grade: Tailored for businesses handling petabyte-scale data with complex analytical needs.
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- Globally Distributed NoSQL DB: Offers low-latency access across multiple regions.
- Multi-API Support: Works with SQL, MongoDB, Cassandra, Gremlin (graph), and Table APIs.
- Elastic Scalability: Auto-scales throughput and storage as per demand.
- Guaranteed SLAs: Offers 99.999% availability, low latency, and consistency options.
- Ideal for Cloud-Native Apps: Supports IoT, mobile, and real-time analytics use cases.
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- Key-Value NoSQL Store: Provides schema-less storage of structured data.
- High Availability: Built on Azure’s globally redundant infrastructure.
- Cost-Effective: Ideal for storing large volumes of semi-structured data at low cost.
- Simple Query Language: Uses OData protocol for filtering and querying entities.
- Common for Logs & Metadata: Often used for telemetry, logs, user settings, and application data.
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- Embedded Key-Value Store: Runs directly within applications—no separate server process.
- ACID-Compliant: Supports transactions, concurrency control, and crash recovery.
- C/C++ Core with APIs: Available bindings for C++, Java, Python, Perl, and more.
- Used in Many Systems: Forms the core of many back-end services, mobile apps, and legacy systems.
- Open Source + Commercial License: Dual-licensed under Oracle for open and enterprise users.
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- Highly Scalable NoSQL DB: Designed for handling large volumes of data across commodity servers.
- Peer-to-Peer Architecture: Eliminates single points of failure; every node is equal.
- Eventual Consistency: Tunable consistency levels for flexible trade-offs in CAP theorem.
- High Write Throughput: Ideal for time-series, sensor data, and messaging platforms.
- Used by Giants: Adopted by Netflix, Facebook, and Uber for mission-critical workloads.
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- Graph Database Engine: Designed for storing and querying linked data using graph structures.
- Inspired by Freebase: Tailored for semantic web and linked data applications.
- Supports Multiple Backends: Can use BoltDB, MongoDB, LevelDB, and more for storage.
- Graph Query Language: Uses Gremlin-like language and supports MQL-style queries.
- Open Source Project: Flexible and lightweight, suitable for custom graph data applications.
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- Columnar Storage Engine: Designed for OLAP workloads, ClickHouse stores data in columns to optimize analytical queries.
- High Performance: Capable of processing billions of rows per second due to vectorized execution and compression.
- Distributed Architecture: Supports horizontal scaling for real-time analytics on large datasets.
- SQL-Like Syntax: Offers familiar query language, simplifying adoption for analysts and developers.
- Use Cases: Ideal for log analytics, telemetry data, time-series analytics, and dashboards.
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- Distributed SQL Database: Clustrix offers MySQL-compatible SQL while ensuring high scalability and availability.
- Shared-Nothing Architecture: Enables horizontal scaling by distributing data and computation across nodes.
- Fault-Tolerant: Automatic data replication ensures high availability with failover capabilities.
- Transactional Integrity: Maintains ACID compliance, suitable for OLTP applications.
- Use Cases: Suited for e-commerce, social networks, and SaaS platforms needing scalable SQL operations.
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- Cloud-Native SQL Database: Designed for resilience and automatic scaling across cloud infrastructure.
- Strong Consistency & ACID Compliance: Built on a distributed architecture using Raft consensus for data integrity.
- Multi-Region Deployment: Automatically replicates data across regions for low-latency and fault tolerance.
- PostgreSQL-Compatible: Offers support for PostgreSQL wire protocol and SQL syntax.
- Ideal For: Global-scale applications, financial systems, and highly available enterprise apps.
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- Multi-Model NoSQL Database: Combines key-value, document, and SQL for JSON support in a single engine.
- High Throughput & Low Latency: Optimized for responsive, large-scale mobile and web applications.
- N1QL Query Language: Allows SQL-like queries on JSON documents for complex querying needs.
- Offline-First Mobile Sync: Couchbase Mobile supports offline apps with data sync to the cloud.
- Common Uses: Gaming, IoT, real-time personalization, and content management.
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- Document-Oriented NoSQL DB: Uses JSON documents with schema flexibility for web and mobile apps.
- MVCC for Conflict Resolution: Implements Multi-Version Concurrency Control for safe concurrent updates.
- RESTful HTTP API: Easily integrates with web applications through HTTP/JSON interfaces.
- Master-Master Replication: Allows offline-first applications and easy data sync across locations.
- Best For: Decentralized apps, P2P synchronization, and resilient offline data handling.
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- Enterprise Version of Apache Cassandra: Provides advanced features like search, analytics, and security.
- Scalable NoSQL Platform: Designed for massive data volumes with zero downtime scalability.
- Integrated Search & Graph DB: Combines Cassandra with Solr (search) and DSE Graph for powerful use cases.
- Enterprise Security & Compliance: Supports LDAP, Kerberos, auditing, and data encryption.
- Targeted Applications: Banking, IoT, fraud detection, and real-time personalization.
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- Object-Oriented Database: Stores objects directly without needing object-relational mapping (ORM).
- Lightweight & Embedded: Suitable for applications with limited resources like mobile or embedded systems.
- Native Language Integration: Offers tight integration with Java and .NET environments.
- No SQL Layer: Interacts directly with objects, reducing translation overhead.
- Best Suited For: Small-scale applications, prototyping, or when performance and object persistence are priorities.
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- Universal Database Tool: Supports a wide range of databases including MySQL, Oracle, PostgreSQL, and more.
- Visual Query Builder: Allows users to create complex SQL queries without hand-coding.
- Data Exploration & ER Diagrams: Provides insights into schema relationships and table structures.
- Cross-Platform Compatibility: Available on Windows, macOS, and Linux.
- Useful For: Developers, analysts, and DBAs needing a unified interface for multiple databases.
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- Distributed Graph Database: Built for high-performance querying of connected data at scale.
- GraphQL Native Support: Offers native GraphQL endpoints for intuitive querying of graph data.
- Horizontal Scalability: Efficiently scales across nodes for high availability and large datasets.
- Real-Time Graph Queries: Fast traversal for use cases like recommendation systems and social networks.
- Great For: Knowledge graphs, content discovery, fraud detection, and network analysis.
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- Real-Time Analytics Engine: Optimized for high-speed ingestion and querying of streaming data.
- Column-Oriented Storage: Accelerates aggregations and time-based queries.
- Hybrid OLAP/HTAP Model: Supports both batch and real-time analytics on large-scale datasets.
- Integrates with Kafka & Hadoop: Commonly used in modern data pipelines for fast insights.
- Use Cases: Dashboards, clickstream analytics, telemetry, and BI applications with sub-second response times.
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- Content-Driven CMS: Though primarily a CMS, Drupal (often misspelled as “Druppal”) utilizes a robust database backend for managing content, users, and configurations.
- Database Abstraction Layer: Supports multiple databases like MySQL, MariaDB, PostgreSQL via a unified abstraction layer.
- Schema Flexibility: Custom content types and fields are stored in relational tables, enabling scalable content modeling.
- Dynamic Queries: Uses its own database API for secure and optimized SQL query generation across supported DB engines.
- Extensible Storage: Modules can define new database schemas and interact with external data sources as needed.
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- Fully Managed NoSQL DB: AWS DynamoDB is a key-value and document database designed for high performance at scale.
- Auto-Scaling & Serverless: Offers automatic partitioning and seamless throughput scaling with no server management.
- Millisecond Latency: Optimized for low-latency access, suitable for real-time apps, gaming, IoT, and mobile.
- ACID Transactions: Supports atomic operations and full transactions for consistent reads and writes.
- Integrated Security: Offers encryption, IAM-based access control, and VPC endpoint integration for secure data handling.
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- Hybrid DBMS: Merges Apache Cassandra’s distributed storage with Elasticsearch’s powerful search capabilities.
- Dual Query Capability: Enables both CQL (Cassandra Query Language) and full-text Elasticsearch queries on the same data.
- Scalable Architecture: Inherits Cassandra’s linear scalability and fault tolerance across nodes.
- Search + Storage: Ideal for applications needing large-scale data storage with fast, flexible search functionality.
- Integrated Data Model: Automatically indexes Cassandra data into Elasticsearch mappings for real-time analytics.
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- Distributed Search Engine: Primarily used for full-text search and analytics on semi-structured data.
- Document-Oriented Store: Stores data as JSON documents, making it suitable for modern app log and event indexing.
- Real-Time Indexing: Enables near real-time data ingestion and retrieval, critical for logs, metrics, and monitoring.
- Powerful Query DSL: Offers flexible querying with support for text search, filtering, aggregations, and scoring.
- Cluster-Based Design: Supports horizontal scaling, replication, and sharding for high availability and performance.
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- Marketing Data Platform: Oracle Eloqua is a marketing automation tool with strong database-backed lead tracking.
- Behavioral Data Storage: Stores engagement metrics (email opens, link clicks, form submissions) in structured tables.
- Campaign Analytics: Uses integrated data models to visualize campaign performance and customer journey insights.
- CRM Sync: Connects to CRMs like Salesforce, synchronizing contact and lead data in real-time.
- Segmented Querying: Supports dynamic segmentation via SQL-like logic for personalized marketing and targeting.
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- Client-Side Data Handling: Ember.js uses Ember Data to manage local data and synchronize with backend databases via APIs.
- Model Abstraction: Defines models and relationships, abstracting backend database details from the frontend.
- RESTful Integration: Works with REST/JSON:API-compliant databases to perform CRUD operations seamlessly.
- Realtime Sync: Supports live updates through websockets or polling for applications that reflect backend DB changes.
- ORM-Like Features: Ember Data provides an ORM-like interface on the client for querying, filtering, and caching records.
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- Serverless Cloud Database: FaunaDB is globally distributed and eliminates the need to manage database infrastructure.
- Relational + NoSQL: Combines document storage with relational capabilities like joins and strong consistency.
- GraphQL & FQL Support: Offers both GraphQL and its custom FQL for expressive, secure queries.
- ACID Transactions: Supports true multi-document ACID transactions, rare among serverless NoSQL databases.
- Security-First Design: Built-in identity-based access control and region-aware data handling for compliance.
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- Likely a Typo of "FaunaDB" or Unknown Tool: If referring to FaundDB, it appears to be an obscure or misspelled technology.
- Clarification Needed: No mainstream or widely documented database technology exists under this name.
- Possible Misreference: Could be a variant or confusion with FaunaDB or CouchDB, which are document-based databases.
- Database Context Suggestion: If it’s an internal/custom tool, it may support document storage, synchronization, or real-time operations.
- Recommended Action: Verify spelling or source documentation to ensure accurate insights on the tool.
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- Object-Oriented DBMS: GemStone/S is a Smalltalk-based object database system for enterprise-scale object persistence.
- High Consistency: Offers transactional consistency and ACID-compliant object storage across multiple nodes.
- In-Memory + Persistent: Combines in-memory speed with durable backend for object graphs.
- Concurrency Support: Handles concurrent access and versioning of objects with advanced VM-level controls.
- Enterprise Use Cases: Suitable for business applications needing complex object models and distributed data access.
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- NoSQL Columnar Store: Bigtable is a highly scalable database used for large-scale analytical and operational workloads.
- Built for Speed: Powers Google services like Search and Maps, offering single-digit millisecond latency.
- Scalable Architecture: Handles petabytes of data across thousands of nodes with automatic sharding.
- HBase Compatible: Supports the HBase API, making it easy to integrate with existing Hadoop ecosystems.
- Use Cases: Ideal for time-series data, financial analytics, IoT, and personalization engines requiring real-time access.
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- Fully managed relational database service supporting MySQL, PostgreSQL, and SQL Server.
- Handles automated backups, patch management, and high availability configurations.
- Integrated with Google Cloud services for seamless data flow and analytics.
- Offers built-in security via VPC, SSL, and IAM-based access control.
- Ideal for web apps, CMS, and enterprise workloads needing relational consistency.
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- Globally distributed, horizontally scalable relational database with strong consistency.
- Combines features of SQL (ACID compliance) and NoSQL (scalability) systems.
- Used for mission-critical applications with high availability and minimal latency.
- Supports automatic sharding and synchronous replication across regions.
- Ideal for fintech, gaming, and e-commerce systems with high concurrency demands.
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- A semantic graph database engine based on RDF and SPARQL standards.
- Optimized for storing, querying, and reasoning over linked data and ontologies.
- Used in knowledge graphs, healthcare data models, and enterprise metadata management.
- Enables inference-based querying and semantic reasoning through OWL.
- Supports large-scale integration with linked open data and semantic web projects.
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- Time-series database used primarily for monitoring and performance metrics.
- Works with visualization tools like Grafana to plot real-time dashboards.
- Stores numeric time-series data, ideal for DevOps and system health analytics.
- Can be integrated with carbon-relay and statsd for scalable metric collection.
- Suitable for infrastructure monitoring, application telemetry, and trend analysis.
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- In-memory data grid that offers fast, distributed data access across clusters.
- Provides caching, key-value storage, and compute processing on distributed data.
- Supports Java-based APIs and integrates with cloud-native platforms like Kubernetes.
- Used in real-time analytics, microservices, and low-latency transaction processing.
- Offers features like CP subsystem (for strong consistency) and WAN replication.
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- A distributed, column-oriented NoSQL database built on top of Hadoop HDFS.
- Designed to handle billions of rows and millions of columns with high write throughput.
- Supports sparse datasets and real-time read/write access.
- Integrated with Hadoop ecosystem tools like Hive, Pig, and MapReduce.
- Common in IoT, recommendation engines, and time-series big data processing.
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- Combines MOLAP (multidimensional) and ROLAP (relational) capabilities for flexibility.
- Stores summarized data in cubes and detailed data in relational format.
- Optimized for performance and storage efficiency in business intelligence.
- Supports ad-hoc queries and drill-down operations for large-scale analytical needs.
- Commonly used in enterprise reporting, budgeting, and financial forecasting systems.
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- Open-source data platform built on Hadoop, enabling distributed storage and processing.
- Includes components like Hive, Pig, HBase, and Spark for versatile data handling.
- Designed for big data analytics, ETL pipelines, and data lake architecture.
- Offers data governance, security, and real-time streaming with Apache NiFi.
- Supports hybrid cloud environments and integrates with machine learning tools.
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- High-performance, open-source NoSQL database modeled after Google’s Bigtable.
- Optimized for high write throughput and large-scale real-time applications.
- Designed to scale out across distributed clusters with fault tolerance.
- Uses a sparse, multidimensional sorted map for storing large datasets.
- Commonly used in analytics, clickstream processing, and telecommunications.
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- Enterprise-grade RDBMS known for its performance, scalability, and reliability.
- Supports both structured (SQL) and unstructured (JSON, XML) data models.
- Offers in-memory computing, advanced indexing, and compression techniques.
- Features robust security, high availability, and disaster recovery options.
- Deployed in banking, healthcare, and retail for mission-critical workloads.
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- InfluxDB is a time-series database optimized for storing and querying time-stamped data such as logs, metrics, and sensor data.
- It supports powerful Flux and InfluxQL query languages for advanced analytics over time intervals.
- Designed for high write throughput and real-time monitoring use cases in IoT, DevOps, and finance.
- Offers retention policies, downsampling, and data compression to manage storage costs.
- Integrates seamlessly with tools like Grafana, Telegraf, and Kapacitor for end-to-end observability.
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- JanusGraph is a distributed graph database optimized for large-scale, complex relationship data.
- Supports property graph model and works with TinkerPop Gremlin for traversal-based queries.
- Scales horizontally using backends like Apache Cassandra, HBase, or ScyllaDB.
- Ideal for applications involving recommendation engines, fraud detection, and knowledge graphs.
- Supports multi-tenancy, geo-spatial, and temporal data, expanding its applicability across domains.
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- JDO is a Java persistence API that provides transparent object persistence for Java applications.
- It is datastore-agnostic, supporting relational, NoSQL, and object databases alike.
- Enables developers to perform CRUD operations using Java objects without writing SQL.
- Offers support for transaction management, lazy loading, and query optimization.
- Competes with JPA (Java Persistence API) and is often used in legacy or flexible persistence architectures.
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- KDB+ is a high-performance column-oriented database used primarily for time-series data analytics.
- Powered by the q language, it supports fast in-memory and on-disk processing.
- Widely used in financial trading systems, where nanosecond-level precision and low-latency queries are critical.
- Offers vectorized computation, making it ideal for analyzing millions of records in real time.
- Combines both real-time (in-memory) and historical (on-disk) data management in a unified system.
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- Kong is primarily an API gateway, but it integrates deeply with database backends to manage routing and authentication data.
- Uses PostgreSQL or Cassandra as its datastore to persist configurations and plugin data.
- Manages API rate-limiting, access control, and logging—important for securing and scaling APIs.
- Supports real-time analytics and monitoring by storing API traffic metadata in the backend database.
- Useful in microservices architectures where scalable API management relies on efficient data storage and retrieval.
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- LevelDB is a lightweight, key-value storage engine developed by Google, optimized for embedded use.
- Employs log-structured merge trees (LSM) to provide fast writes and range queries.
- Not a full database—offers no SQL layer, only basic data retrieval via keys and sorted iteration.
- Commonly used in browsers (like Chrome), blockchains (Bitcoin Core), and mobile apps.
- Offers efficient disk storage, making it ideal for scenarios with limited resources.
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- MariaDB is a community-developed fork of MySQL, offering improved performance and openness.
- Fully compatible with MySQL but includes additional storage engines like Aria and ColumnStore.
- Offers advanced features like thread pooling, temporal tables, and data-at-rest encryption.
- Suitable for a variety of workloads—from small websites to large-scale OLTP systems.
- Frequently chosen by enterprises looking to avoid vendor lock-in while maintaining SQL compatibility.
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- MarkLogic is a multi-model database supporting document, graph, and relational data natively.
- Excels in enterprise data integration, enabling unified access to heterogeneous data sources.
- Built-in search engine, triple store (RDF), and ACID compliance make it powerful for semantic web and compliance-heavy applications.
- Often used in government, publishing, and healthcare sectors for complex queries and regulatory data handling.
- Supports XQuery, SPARQL, and RESTful access for flexible development.
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- Matisse is an object-relational database combining object-oriented models with relational storage.
- Offers tight integration with object-oriented programming languages like Java and C++.
- Useful in CAD, engineering, and simulation applications where complex object hierarchies are modeled.
- Supports ACID transactions, inheritance, and polymorphism, enhancing schema design flexibility.
- Balances performance with object persistence, aiming to reduce impedance mismatch.
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- Memcached is a high-speed, in-memory key-value store used for caching database queries and objects.
- Reduces load on backend databases by storing frequently accessed data in RAM.
- Ideal for read-heavy applications such as e-commerce, social media, and session storage.
- Simple design with no persistence or replication, optimized solely for speed and scale-out.
- Commonly used alongside databases like MySQL or PostgreSQL to accelerate response times.
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- SingleStore is a distributed SQL database designed for real-time analytics and transactional workloads.
- Combines memory-first architecture with disk storage, enabling low-latency access to large datasets.
- Supports ANSI SQL and MySQL wire protocol, allowing for compatibility with existing tools.
- Used for high-performance use cases like financial analytics, IoT, and predictive dashboards.
- Offers horizontal scalability and cloud-native deployment, supporting Kubernetes and serverless modes.
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- Enterprise-grade RDBMS developed by Microsoft with full support for T-SQL.
- Widely used for data warehousing, OLAP, and transactional systems.
- Offers built-in tools like SSMS, SSRS, SSIS, and integration with Azure.
- Provides high availability options like Always On, replication, and failover clustering.
- Enhanced with machine learning and in-database analytics in newer versions.
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- GUI for MongoDB that simplifies database visualization and query execution.
- Provides schema exploration, aggregation pipeline builder, and performance insights.
- Ideal for developers and data analysts who prefer visual tools over CLI.
- Enables real-time validation of queries and data manipulation.
- Supports connection management, indexes, and user roles setup.
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- Document-oriented NoSQL database known for flexibility and horizontal scalability.
- Stores data in BSON (binary JSON) format for fast query and indexing.
- Ideal for applications with dynamic or unstructured data, such as content management and analytics.
- Supports powerful aggregation frameworks and full-text search.
- Offers cloud-managed service via MongoDB Atlas, including backups and monitoring.
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- Visual tool for designing, modeling, and managing MySQL databases.
- Enables ER diagram design, SQL development, and server configuration.
- Includes performance dashboard, backup tools, and migration utilities.
- Great for developers and DBAs managing small to medium-scale MySQL setups.
- Supports connection to both local and cloud-hosted MySQL servers.
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- Open-source relational database widely used in web and enterprise apps.
- Known for reliability, simplicity, and cross-platform support.
- Uses SQL for query and transaction management, supports ACID compliance.
- Backed by Oracle with wide adoption in LAMP stacks (Linux, Apache, MySQL, PHP).
- Offers replication, sharding, and InnoDB engine for scalability and performance.
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- Graph database that represents data as nodes and relationships.
- Ideal for connected data problems, such as recommendation engines, fraud detection, and social networks.
- Uses Cypher query language for expressive graph traversal.
- Offers strong performance in depth-oriented and relationship-intensive queries.
- Supports enterprise-grade clustering, sharding, and analytics with visualization.
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- Distributed SQL database built for cloud-native and scalable applications.
- Maintains ACID compliance while scaling horizontally across nodes.
- Separates transaction engine and storage manager for high performance.
- Ideal for enterprise-grade transactional workloads with zero-downtime deployment.
- Integrates with container platforms and hybrid cloud infrastructures.
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- Object-oriented database for Java, storing data as native Java objects.
- Compatible with JPA and JDO APIs, making it easy for Java developers.
- Performs better than traditional RDBMS in Java-centric applications.
- Ideal for embedded systems, desktop, and real-time Java applications.
- Supports transactions, indexing, and persistence with minimal configuration.
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- Distributed object-oriented database for complex data modeling.
- Used in high-volume environments like telecom, defense, and sensor networks.
- Allows schema-less design with complex inter-object relationships.
- Offers strong consistency and fault tolerance, even across distributed nodes.
- Scales well for real-time, graph-based, or hierarchical data.
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- Time Series Optimized: Specially built for storing, indexing, and retrieving large volumes of time-series data.
- Built on HBase: Leverages Apache HBase to ensure scalability and high-throughput reads/writes.
- Ideal for Metrics Monitoring: Commonly used in infrastructure monitoring and sensor data logging.
- Tag-Based Data Model: Allows flexible querying with multiple dimensions via key-value tag pairs.
- Integration-Friendly: Compatible with Grafana and other monitoring tools for real-time visualization.
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- Enterprise-Grade RDBMS: Widely used in mission-critical applications across industries.
- Advanced Features: Offers features like partitioning, in-memory processing, and advanced analytics.
- Strong ACID Compliance: Ensures high integrity and consistency for transactional workloads.
- Highly Secure and Scalable: Built-in auditing, data masking, and multi-tenancy support.
- PL/SQL Support: Enables complex logic implementation within the database using procedural SQL.
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- Multi-Model Database: Supports graph, document, key-value, and object models in one engine.
- Graph Capabilities: Ideal for applications requiring relationship modeling like social networks.
- SQL-Like Query Language: Combines SQL syntax with NoSQL flexibility.
- Schema-Less & Schema-Full: Allows dynamic data modeling with optional schema enforcement.
- High Availability Support: Offers clustering, replication, and sharding features for fault tolerance.
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- Cloud-Native Object Store: Designed for big data storage, supporting Hadoop ecosystems.
- Scalable and Fault-Tolerant: Handles petabyte-scale workloads with built-in replication.
- Optimized for Metadata: Separates metadata from data storage for performance and reliability.
- S3-Compatible API: Integrates easily with tools and services expecting S3 object storage.
- Used in Distributed Data Lakes: Acts as a backend for analytics platforms needing object storage.
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- Graphical PostgreSQL Manager: Provides GUI tools to manage, query, and monitor PostgreSQL databases.
- Cross-Platform Support: Runs on Windows, macOS, and Linux via desktop or web-based interface.
- Powerful Query Tool: Features syntax highlighting, result visualization, and history tracking.
- User & Role Management: Simplifies administrative tasks like permission control and backups.
- Ideal for DBAs and Developers: Bridges the gap between command-line and GUI operations.
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- Open-Source Object-Relational Database: Known for standards compliance, extensibility, and performance.
- Advanced Indexing & Full-Text Search: Supports GIN, GiST, BRIN, and full-text search features natively.
- ACID-Compliant & MVCC Support: Ensures high concurrency and data reliability.
- Extensible with Custom Types and Functions: Supports procedural languages like PL/pgSQL and extensions like PostGIS.
- Highly Scalable: Suitable for both OLTP and OLAP workloads with replication and partitioning options.
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- Time-Series Database for Monitoring: Purpose-built to collect and store metrics data efficiently.
- Pull-Based Data Collection: Queries metrics from endpoints using HTTP for fine-grained control.
- Powerful Query Language (PromQL): Allows complex aggregations and time-based functions.
- Built-In Alerting Rules: Supports real-time alert generation with integration into Alertmanager.
- Used in Cloud Native Observability: Commonly paired with Grafana in Kubernetes and DevOps setups.
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- High-Performance Time Series DB: Designed for ultra-fast ingestion and querying of time-series data.
- SQL-Based Interface: Supports ANSI SQL with time extensions for powerful time-series analysis.
- Zero-GC Architecture: Avoids garbage collection overhead for low-latency performance.
- Optimized for FinTech and IoT: Handles millions of events per second in real-time environments.
- Supports InfluxDB Line Protocol: Easy to integrate with existing time-series pipelines.
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- Document-Oriented NoSQL DB: Focuses on flexibility, fast access, and schema-free data storage.
- Built-In Full-Text Search: Integrated with Lucene for fast and intelligent text queries.
- Transactional (ACID) Support: Offers transactions across documents and even distributed clusters.
- Easy Setup with GUI Dashboard: Offers visual tools for query, data inspection, and clustering.
- Strong for C#/.NET Integration: Ideal for developers in the Microsoft ecosystem.
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- GUI for Redis Management: Offers powerful visualization and data inspection for Redis databases.
- Supports All Redis Data Types: Including strings, hashes, lists, sets, and sorted sets.
- Performance Monitoring Dashboard: Tracks memory usage, command latency, and throughput.
- Query Analyzer Tool: Helps optimize key patterns and slow queries.
- Developer Friendly: Great for debugging, learning, and tuning Redis applications.
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- In-Memory Data Store: Redis stores data in memory for ultra-fast read/write operations, ideal for caching and real-time analytics.
- Supports Diverse Data Types: Offers strings, lists, sets, sorted sets, hashes, streams, and bitmaps.
- High Availability with Sentinel & Cluster: Provides robust failover, replication, and horizontal scalability.
- Use Cases: Widely used for session storage, leaderboards, pub/sub messaging, and rate-limiting.
- Persistence Options: Offers AOF and RDB to persist data on disk for recovery and durability.
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- Distributed Key-Value Store: Built for availability, Riak KV ensures no single point of failure.
- Eventually Consistent: Uses a Dynamo-inspired model for consistency across nodes in distributed systems.
- Horizontal Scalability: Easily scales out by adding nodes, without manual sharding or rebalancing.
- Conflict Resolution: Built-in CRDTs and vector clocks manage data conflicts gracefully.
- Use Cases: Suitable for IoT, session management, and highly available web services.
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- Fault-Tolerant Distributed Database: Offers resilience with automatic data replication and self-healing.
- Multiple Models: Riak includes Riak KV (key-value) and Riak TS (time series).
- Built on Erlang: Its robust runtime ensures high availability and fault isolation.
- Decentralized Architecture: All nodes are equal—no master-slave dependencies, improving uptime.
- Used in Telecom & Banking: Supports mission-critical applications requiring low-latency access.
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- Embedded Key-Value Store: Optimized for SSDs and flash storage with high write throughput.
- Based on LevelDB: A fork of Google’s LevelDB with advanced performance features.
- Supports Compaction and Compression: Efficient storage through background compactions and configurable compression.
- Low-Latency Reads/Writes: Designed for high-speed ingestion and queries in embedded systems.
- Commonly Used In: Blockchain platforms, message queues, and streaming data ingestion.
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- In-Memory Columnar Database: Processes large datasets in-memory for real-time analytics.
- Combines OLAP & OLTP: Enables transactional and analytical processing on the same platform.
- Advanced Features: Includes predictive analytics, graph processing, and machine learning libraries.
- Enterprise-Grade: Powers SAP S/4HANA and other core business applications with high reliability.
- Data Virtualization: Can query external databases without data replication.
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- Cassandra-Compatible NoSQL DB: Drop-in replacement for Apache Cassandra with 10x performance.
- Written in C++ (vs. Java): Low-level memory management gives it a performance edge.
- Supports Shard-Per-Core Architecture: Minimizes context switching and increases CPU utilization.
- Best for High Throughput Applications: Ideal for ad tech, time-series data, and real-time analytics.
- Automatic Tuning: Self-optimizing engine reduces ops complexity and boosts performance.
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- Lightweight Embedded DB: Ideal for mobile apps, browsers, and local desktop software.
- Serverless Architecture: Does not require a separate server process, simplifying deployments.
- ACID Compliant: Ensures data integrity with support for transactions, even in embedded environments.
- Zero Configuration: Ready to use out of the box—no setup required.
- Used in Mobile OSes: Default database engine in Android and iOS app development.
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- Real-Time OLAP Database: Designed for sub-second query latency on large-scale analytics.
- Columnar Storage Engine: Speeds up aggregation, filtering, and compression for analytical workloads.
- Vectorized Execution: Enhances CPU efficiency for large data scans.
- Supports Star Schema Models: Optimized for BI workloads common in enterprise reporting.
- Integration Friendly: Connects with Apache Flink, Spark, and Kubernetes.
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- In-Memory Database & Lua Application Server: Combines high-speed data access with scripting capabilities.
- Transactional Support: Offers ACID-compliant transactions with low latency.
- Flexible Data Model: Supports both key-value and document-style (NoSQL) data structures.
- Message Queue Integration: Can be used as a fast messaging broker for real-time pipelines.
- Lightweight Yet Powerful: Ideal for fintech, ad tech, and gaming backend systems.
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- Oracle-Compatible RDBMS: Designed to provide enterprise-level features at a lower TCO.
- High Performance: Optimized for heavy workloads, including batch jobs and real-time transactions.
- Cloud-Ready Architecture: Supports hybrid deployments in private/public cloud environments.
- Strong Security: Includes audit logs, role-based access, and encryption features.
- Used in Financial Systems: Popular in banking and government applications for mission-critical operations.
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- TiDB is an open-source distributed SQL database that’s MySQL compatible.
- Combines the benefits of OLTP (row-based) and OLAP (column-based) workloads in one system.
- Built on top of Raft consensus for high availability and strong consistency.
- Designed for horizontal scalability, ideal for massive transactional applications.
- Well-suited for cloud-native architectures with real-time data analytics.
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- A native parallel graph database designed for deep link analytics.
- Capable of handling billions of nodes and edges, enabling complex queries at scale.
- Used for fraud detection, recommendation systems, supply chain optimization, etc.
- Supports GSQL, a Turing-complete graph query language.
- Optimized for real-time traversal and graph analytics across large datasets.
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- An open-source time-series database built as an extension to PostgreSQL.
- Offers native SQL support for time-series data ingestion and analysis.
- Ideal for monitoring systems, IoT analytics, financial time-series, etc.
- Supports continuous aggregates, compression, and scalability for large workloads.
- Combines relational integrity with time-series efficiency in a unified solution.
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- A high-performance object-oriented database supporting complex C++/Java object models.
- Directly stores and retrieves objects without conversion to relational tables.
- Used in telecom, aerospace, and real-time systems requiring low latency.
- Supports schema evolution, distributed architecture, and transactional consistency.
- Reduces overhead in object-relational mapping (ORM), enhancing runtime performance.
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- A high-performance, open-source time-series database designed for metrics storage.
- Optimized for large-scale Prometheus monitoring setups and long retention.
- Offers efficient compression and fast querying, even with billions of data points.
- Supports single-node and cluster deployments, with minimal hardware needs.
- Common in DevOps, observability, and telemetry-driven environments.
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- A distributed key-value storage system developed by LinkedIn.
- Designed for horizontal scalability, fault tolerance, and simple data replication.
- Supports pluggable storage engines and serialization, making it highly customizable.
- Suitable for read-heavy workloads and distributed caching.
- Often used as a NoSQL backend for large-scale web applications.
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- A high-speed in-memory NewSQL database for real-time analytics and transactions.
- Combines ACID compliance with low-latency performance using stored procedures.
- Well-suited for telecom billing, fraud detection, IoT event processing, etc.
- Scales linearly with multi-node clusters and stream processing capabilities.
- Supports SQL queries with millisecond-level latency in demanding environments.
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- A time-series database and analytics platform designed for geo-temporal data.
- Includes a unique programming language (WarpScript) for advanced analytics.
- Supports ingestion of both structured and unstructured sensor data.
- Offers powerful statistical, signal processing, and ML functions.
- Ideal for IoT platforms, environmental monitoring, and smart cities.
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- An open-source full-text search engine library, used to build search applications.
- Implements probabilistic information retrieval models for relevance scoring.
- Designed in C++, with bindings for Python, PHP, Perl, Java, etc.
- Powers document search, email search, and knowledge base indexing.
- Efficient for large-scale text indexing with rich query capabilities.
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- A distributed SQL database that offers PostgreSQL compatibility on the front end.
- Supports global-scale apps with automatic sharding, replication, and fault tolerance.
- Provides both strong consistency (via Raft) and high availability.
- Ideal for cloud-native deployments needing multi-region and multi-cloud support.
- Supports both OLTP and HTAP (Hybrid Transactional/Analytical Processing) workloads.
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