Top 10 Google Cloud Datastore Alternatives & Competitors

Top 10 Alternatives to Google Cloud Datastore Recently Reviewed By G2 Community

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Amazon DynamoDB
By Amazon Web Services (AWS)
4.4/5
(528)
Product Description
Nonrelational database for applications that need performance at any scale
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Categories in common with Google Cloud Datastore:
Document Databases
Database as a Service (DBaaS)

MongoDB Atlas
By MongoDB
4.5/5
(366)
Product Description
MongoDB Atlas empowers innovators to create, transform, and disrupt industries by unleashing the power of software and data.
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Categories in common with Google Cloud Datastore:
Document Databases
Database as a Service (DBaaS)

MongoDB
By MongoDB
4.5/5
(553)
Product Description
MongoDB Atlas is a developer data platform that provides a tightly integrated collection of data and application infrastructure building blocks to enable enterprises to quickly deploy bespoke architectures to address any application need. Atlas supports transactional, full-text search, vector search, time series and stream processing application use cases across mobile, distributed, event-driven, and serverless architectures.
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Categories in common with Google Cloud Datastore:
Document Databases

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DigitalOcean
By DigitalOcean
4.6/5
(745)
Product Description
DigitalOcean provides the best tools to control your virtual server in the cloud. Learn how we deliver the most intuitive interface and features so you can start building your web infrastructure today.
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Categories in common with Google Cloud Datastore:
Database as a Service (DBaaS)

Oracle Database
By Oracle
4.3/5
(1,009)
Product Description
Oracle Database is a comprehensive, multi-model database management system developed by Oracle Corporation. It is designed to handle various data types and workloads, including online transaction processing (OLTP), data warehousing, and mixed database operations. With its robust architecture, Oracle Database supports deployment across on-premises environments, cloud platforms, and hybrid configurations, offering flexibility and scalability to meet diverse business needs. Key Features and Functionality: - Multi-Model Support: Oracle Database accommodates various data models, including relational, document, graph, and key-value, enabling developers to work with diverse data types within a single platform. - Advanced Analytics: The database integrates advanced analytics capabilities, such as in-database machine learning and AI Vector Search, allowing users to perform complex analyses directly within the database environment. - High Availability and Scalability: Designed for mission-critical applications, Oracle Database offers features like data replication, backup, server clustering, and automatic storage management to ensure high availability and seamless scalability. - Security: With comprehensive security measures, including encryption, SQL Firewall, and data masking, Oracle Database safeguards sensitive information and maintains data integrity. - Multicloud Deployment: Oracle Database supports deployment across various cloud platforms, including Oracle Cloud Infrastructure, AWS, Microsoft Azure, and Google Cloud, providing flexibility and compliance with data residency requirements. Primary Value and Solutions Provided: Oracle Database addresses the complex data management needs of modern enterprises by offering a unified platform that supports multiple data models and workloads. Its integration of AI and machine learning capabilities enables organizations to derive actionable insights directly from their data, enhancing decision-making processes. The database's high availability and scalability ensure that businesses can maintain continuous operations and adapt to growing data demands. Additionally, its robust security features protect against data breaches and ensure compliance with regulatory standards. By supporting multicloud deployments, Oracle Database provides the flexibility to operate in various cloud environments, facilitating seamless integration and innovation across different platforms.
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Categories in common with Google Cloud Datastore:
Database as a Service (DBaaS)

Amazon Relational Database Service (RDS)
By Amazon Web Services (AWS)
4.5/5
(904)
Product Description
Amazon Relational Database Service (RDS) is a web service that makes it easy to set up, operate, and scale a relational DB in the cloud: Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle, and Microsoft SQL Server.
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Categories in common with Google Cloud Datastore:
Database as a Service (DBaaS)

IBM Db2
By IBM
4.1/5
(682)
Product Description
IBM® Db2® is the database that offers enterprise-wide solutions handling high-volume workloads. It is optimized to deliver industry-leading performance while lowering costs.
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Categories in common with Google Cloud Datastore:
Database as a Service (DBaaS)

SAP HANA Cloud
By SAP
4.3/5
(612)
Product Description
SAP HANA Cloud is the cloud-native data foundation of SAP Business Technology Platform, it stores, processes and analyzes data in real time at petabyte scale and converges multiple data types in a single system while managing it more efficiently with integrated multitier storage.
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Categories in common with Google Cloud Datastore:
Database as a Service (DBaaS)

Amazon Aurora
By Amazon Web Services (AWS)
4.5/5
(456)
Product Description
Amazon Aurora is a fully managed relational database service that combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. Compatible with MySQL and PostgreSQL, Aurora delivers up to five times the throughput of standard MySQL databases and up to three times that of standard PostgreSQL databases. It is designed for high availability, offering up to 99.99% availability within a single region and up to 99.999% across multiple regions. Aurora's architecture includes a distributed, fault-tolerant storage system that automatically scales up to 128 tebibytes, ensuring continuous data access and durability. Additionally, Aurora provides serverless configurations, allowing automatic scaling based on application needs, and integrates seamlessly with other AWS services for machine learning and analytics. Key Features and Functionality: - High Performance: Delivers up to five times the throughput of MySQL and three times that of PostgreSQL, enabling efficient handling of demanding workloads. - High Availability: Designed for up to 99.99% availability within a single region and up to 99.999% across multiple regions, ensuring continuous data access. - Scalability: Automatically scales storage up to 128 tebibytes and supports up to 15 read replicas for read-intensive applications. - Serverless Configuration: Offers Aurora Serverless, which automatically adjusts capacity based on application demand, eliminating the need for manual provisioning. - Machine Learning Integration: Integrates with Amazon SageMaker and Amazon Comprehend, allowing for in-database machine learning capabilities without data movement. - Security: Provides multiple layers of security, including network isolation, encryption at rest and in transit, and compliance with various industry standards. Primary Value and Solutions Provided: Amazon Aurora addresses the need for a high-performance, highly available, and scalable relational database service that is cost-effective and easy to manage. By offering compatibility with MySQL and PostgreSQL, it allows organizations to migrate existing applications without significant code changes. Aurora's automatic scaling and serverless options cater to applications with variable workloads, reducing operational overhead and costs. Its integration with AWS machine learning services enables real-time analytics and predictive capabilities directly within the database, enhancing application functionality. Overall, Aurora simplifies database management while delivering enterprise-grade performance and reliability.
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Categories in common with Google Cloud Datastore:
Database as a Service (DBaaS)