Cloud Database (Cloud DBMS) Requirements Template, Checklist and Gathering Document

This requirements table for Cloud DBMS products clearly outlines the key features and functionalities considered when evaluating vendors. We include security of the underlying data, the ability to handle high resource demands and scale automatically, support for different data-related use cases, and performance tuning and optimization. Other important considerations may include the ability to handle large data sets, replication and disaster recovery, and the level of integration with other cloud services.

Cloud DBMS Requirements Table

Cloud DBMS

Customize these requirements in Taloflow and get expert ratings for 17 different vendors against all of the features in the table below, including Amazon RDS, Google Cloud SQL, and other important vendors.

Must protect the data
Must secure data whether it is stored or in transmission.
Data Masking, Data Encryption, Row-level Security, Column-level Security
Must provide identity management
Must support identity management features for strong and adaptive authentication, privacy management, cross-protocol, SSO, and more.
Identity Management, User Authentication
Must provide strong security at the transport layer
Must provide security at the network level to control access to database servers.
Transport Layer Security, SQL Proxy, Key Management
Must have access control
Must have access control for different groups based on roles within the organization.
RBAC, Group-level Security
Must provide query performance optimization
Must optimize query performance for faster results with less resource consumption and fewer failures.
Rule-based Query Optimizer, Cost-based Query Optimizer, Parallel Query Execution, Automated Query Governance
Must handle changing resource demands
Must provide the ability for the database to adjust to demand by adding or removing resources, or altering the amount of data, volume of requests, and size of requests.
Storage Control, Auto-scaling, Regional Scalability, Data Partitioning, Clustering
Must have fast deployment
Must support different deployment models as well as eliminate the need to purchase hardware and set up a network.
Serverless Architecture, dbPaaS, Self-hosted Cloud, Hybrid Cloud
Use Case Fit
Must have remote data access
Must have features that allow users to have quick access to cloud databases remotely through the provider's API or web interface.
API Access, Integrated Search
Use Case Fit
Must support different methods for data loading and extracting
Must support loading or extracting different types of data with scalability and performance.
Bulk Data Loading, Real-time Data Loading, Streaming Data Support
Use Case Fit
Must support different databases
Must support different types of databases to accommodate non-relational types of data.
Graph Database, OLAP Database Support, Graph Language Support, Multidimensional Views, Multidimensional Tables
Use Case Fit
Must provide analytics and integrations
Must perform analytics and generate insights from the data available in the database.
Operational Reporting, Power BI, Knime, Tableau, Apache Spark, Splunk, SAS BI, Pentaho, Domo, Zoho, Metabase
Use Case Fit
Must be suitable for database administrators
Must help database administrators effectively manage and monitor the transactions in the database and alert the users of any problems.
Workload Management, Capacity Planning, Monitoring, Alerting, Change Management
Must provide easy integration with third party services
Must integrate with analytics, metadata management tools or data integration tools so users can analyze the data, send the data to different systems and manage the metadata.
Informatica, Talend, Boomi, SnapLogic, Azure Data Factory, Denodo, OneTrust, Qlik, Alteryx, Collibra, Power BI, Knime, Tableau, Apache Spark, Splunk, SAS BI, Pentaho, Domo, Zoho, Metabase

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