Top 10 Database as a Service (DBaaS) Solutions

In the cloud era, tasks like managing databases and building applications are more accessible than ever by utilizing database-as-a-service (DBaaS) solutions.

Available as software as a service (SaaS) on all major cloud service providers (CSP), DBaaS offers developers and software engineers accessible online databases with managed underlying infrastructure services. Gone are the days of needing on-premises servers to run and manage complex applications; here are the top DBaaS solutions on the market.

Comparing the Best DBaaS Solutions

Amazon Web Services	Cloudera
Google Cloud	IBM
Microsoft Azure	MongoDB
Oracle Cloud	SAP
Snowflake	Teradata

Amazon Web Services (AWS) Databases

Amazon Web Services (AWS) is the undisputed leader among cloud service providers, with a stack of cloud-based databases to consider. Relational databases like Aurora, RDS, and RedShift are famous for traditional applications like ERP and CRM, while DynamoDB is a robust key-value database for high-traffic web apps. AWS’s other DBaaS types include in-memory, document, wide-column, graph, time series, and ledger databases.

AWS DBaaS Features

• Over 15 engines to support every type of data model needed for distributed apps
• Fully managed database services, including monitoring and self-healing storage
• Automated scaling with SaaS subscription pricing
• Security capabilities like network isolation and end-to-end encryption
• High availability with multi-region, multi-primary replication, and data oversight

Pros

• Ideal for application development requirements
• Value and performance for cost
• Integration with other AWS services 

Cons

• Complex management components
• Some new features aren’t tested thoroughly
• Underwhelming customer support

Cloudera Data Platform (CDP)

Based on the open source Apache Hadoop, Cloudera’s enterprise data management system has been a leading pick for over a decade. 

The Cloudera Data Platform (CDP) offers a stack of solutions, including stream processing, data engineering, warehousing, and data hub, alongside its RDBMS, the CDP Operational Database. Designed for developers, the CDP Operational Database can automate database operations and modernize enterprise-class apps.

Cloudera Features

• Wide-column support, SQL, and NoSQL interfaces for developer workflow flexibility
• Lifecycle tracking with Cloudera’s Shared Data Experience (SDX) for governance
• Auto-tune and auto-heal capabilities to reduce manual workloads
• Support for active-active architectures across on-premises and cloud (region, vendors)
• Full compatibility with HBase deployment for real-time replication and migration

Pros

• Scalability and ease of use
• Strong performance and reliability
• An extensive list of use cases and features

Cons

• Complexity and evident learning curve
• Customer support efficacy is hit or miss
• More expensive with resources consumed cost model

Google Cloud Platform (GCP) Databases

Like AWS, Google Cloud Platform (GCP) is another top pick with many options for different cloud database types and use cases. The list of Google DBaaS solutions includes relational, key-value, document, in-memory, and NoSQL databases for a heap of use cases. 

Since 2011, two of GCP’s core products have been Google BigQuery, a serverless, multi-cloud data warehouse solution, and Google Cloud SQL for a managed MySQL, SQL Server, or PostgreSQL database. Other popular DBaaS solutions from Google include Cloud Bigtable for a managed NoSQL database service and Cloud Spanner for a highly available database.

Google Cloud DBaaS Features

• Scale hundreds of nodes and utilize replication to support high demand
• Predictive ML modeling, multi-cloud, and geospatial analysis via BigQuery
• Schemas, SQL queries, and ACID transactions for multi-regional instances
• Bigtable for storing vast amounts of data in key-value storage at low latency
• Easily integrate Cloud SQL with Google’s engine for Apps, Compute, and K8s

Pros

• Speed of queries, scaling, and backups
• Strong documentation and community
• Ease of integration with other tools

Cons

• More expensive than competitors
• Warnings and documentation need improvement
• The user interface is less intuitive

IBM Cloud Databases

In 2013, IBM acquired budding managed cloud service provider SoftLayer, and in 2017 renamed its cloud services platform to IBM Cloud. Today, IBM Cloud has one of the most substantive platforms with a whole stack of DBaaS products. 

The full suite of managed database solutions covers developer tools, confidential data, business intelligence, and enterprise, web, and mobile applications—all natively integrated with IBM Cloud and Red Hat OpenShift.

IBM Cloud DBaaS Features

• IBM Db2, PostgreSQL, and MySQL solutions for critical relational databases
• Safeguard data with Hyper Protect environments for PostgreSQL and MongoDB
• JSON document indexing and full-text search engine via the Elasticsearch database
• Build robust web apps with IBM Cloudant, MongoDB, and DataStax
• Supports all data types including SQL, NoSQL, IoT, blockchain, structured, and unstructured

Pros

• Constantly improving products
• Flexible and easy to customize
• Data management and data recovery capabilities

Cons

• Lack of documentation
• Frequency of new patches
• Technical support is weak

Microsoft Azure Databases

Rounding out the Big 3 of cloud computing, Microsoft Azure is another enterprise CSP offering SaaS, platform-as-a-service (PaaS), and infrastructure-as-a-service (IaaS) solutions to a universe of clients. 

In 2009, one of Microsoft’s earliest releases was the Azure SQL Database to build and scale apps in the cloud platform. Another notable DBaaS solution from Microsoft includes its NoSQL database, Azure Cosmos DB, and the Azure Synapse Analytics service can unite data integration, warehousing, and analytics to round out the stack.

Azure DBaaS Features

• Fully managed, auto-patching relational databases through the Azure SQL family
• Native support for Azure Functions and App Service for flexible developer workflows
• SLA offers 99.999% availability and enterprise-grade security with Cosmos DB
• Automatic scaling and real-time backups ensure app performance through Hyperscale
• Industry-specific database templates and converged analytics via Synapse

Pros

• Strong user interface
• User-friendly and easy to implement
• Accessible from anywhere

Cons

• Not as interoperable
• More expensive
• Long initial loading

Read more: Oracle and Microsoft Rolling Out Database Service for Azure | Datamation

MongoDB Atlas

Developed initially as a PaaS before shifting to an open source distribution model, MongoDB is available as a community server, enterprise server, and on-demand managed service. Run on major CSPs (AWS, GCP, and Azure), MongoDB Atlas is a developer-first platform for transforming data into intelligent applications. Teams can spin up databases in seconds using an infrastructure-as-code (IaC) provider, K8s Operator, CLI, or using the Atlas Developer Data Platform.

MongoDB Atlas Features

• Multi-region and multi-cloud access across 90 regions on AWS, GCP, and Azure
• Automated data tiering with archival rules to store data efficiently
• Point-in-time backups and end-to-end encryption for securing data
• Full-text search and real-time analytics for enhancing developer workflows
• Manage arrays, geospatial, and time series data formats with Query API

Pros

• Fast queries and storage of unstructured data
• Capable of managing vast amounts of data
• Robust development capabilities and customer service

Cons

• Lagging with larger datasets
• Lacks ACID compliance
• Less ideal for complex transactions

Oracle Cloud Databases

Considering Oracle’s roots in enterprise database management, it’s no surprise the vendor’s cloud services—only released in 2016—are already one of the dominant cloud platforms. 

Oracle Cloud includes a full suite of IaaS, PaaS, SaaS, and Data-as-a-Service (DaaS) solutions and DBaaS products like the Oracle Autonomous Database and NoSQL Database. Within the Oracle Cloud Infrastructure (OCI) portfolio, teams can choose from a stack of managed databases for multi-model database management, NoSQL, and MySQL workflows.

Oracle Cloud DBaaS Features

• Execute transaction, batch, and analytical workloads at the same time
• Maximum security and availability with the Oracle Exadata Database Service
• Storage-based provisioning for document, column, and key-value models
• Preserve ACID properties while developing and deploying applications
• Run OLTP and OLAP workloads via Oracle MySQL HeatWeave

Pros

• Ease of deployment and performance
• Flexible and scalable database solution
• Not resource-intensive for CPU or memory

Cons

• Initial and maintenance costs
• Complexity for non-technical users
• Setup and connectivity can be a time-consuming task

SAP HANA Cloud

In 2012, the enterprise application vendor released the SAP Cloud Platform (SCP), including the in-memory SAP HANA database management system. The SAP HANA Cloud offers teams a central platform for database management, multi-model processing, security, data virtualization, and more. 

SAP HANA is ideal for enterprise organizations and offers continuous monitoring, performance management, disaster recovery, and development tools for database administration.

SAP HANA Cloud Features

• Platform for local, virtualized, and distributed data across hybrid environments
• Store, process, and analyze JSON, document, graph, and geospatial data formats
• Built-in machine learning modeling to improve business processes and performance
• Support for transactional and analytics processing and multimeter storage
• Data anonymization tools to protect the privacy of sensitive data and communications

Pros

• Enterprise favorite for data management
• Speed of processing
• Ease of integration with third-party apps

Cons

• Complex and sometimes long deployment period
• Documentation for troubleshooting and customization capabilities
• Initial and maintenance expenses run high

Snowflake Data Platform

The Snowflake Data Platform includes a cloud-based stack of managed services for warehousing, data lakes, engineering, data science, applications, and cybersecurity. The platform’s three layers consist of a scalable performance engine, automated management, and the Snowgrid to establish cross-cloud and cross-region connectivity and eliminate data silos. A decade after Snowflake’s launch, the data-centric vendor is a favorite partner for a long list of enterprise companies. 

Snowflake Data Platform Features

• Near-unlimited workload scalability and a virtually unlimited number of concurrent users
• A single platform for combining data pipelines, engineering, apps, and analytics
• Multi-cluster resource isolation for reliable, fast performance
• Secure data sharing across a client’s business ecosystem for visibility into insights
• Reduced risk with managed security, governance, and data resiliency

Pros

• Flexibility and performance
• Executing multiple queries at the same time
• Compute and storage management

Cons

• Easy to stack up costs with scaling
• Features overwhelming for new users
• Software updates can be obtrusive

Teradata Vantage

With over 40 years as a database specialist, Teradata offers plenty to consider with its cloud data platform, Teradata Vantage. Vantage brings together data lakes, data warehouses, analytics, and different data sources, offering clients enterprise analytics for the multi-cloud, hybrid infrastructure era. 

Companies have several deployment options, including AWS, GCP, and Azure for public cloud platforms, IntelliFlex and VMware for on-premises, Teradata Cloud, or customers’ existing private cloud.

Teradata Vantage Features

• Ensure uptime and availability with distributed multi-cloud networks
• Support for all common data types including JSON, BSON, XML, CSV, Avro, and Parquet
• Utilize existing on-prem or cloud infrastructure to deploy Vantage
• Two options for pricing: pay by consumption or with a blended model
• Machine learning and AI to scale, power, and proof workflows

Pros

• Sophisticated capabilities for complex tasks
• Robust workload management
• Scalability and capacity for large amounts of data

Cons

• High learning curve for new users
• Expensive relative to competitors
• Difficulty migrating data from the platform

Honorable Mention DBaaS Solutions

• Alibaba Cloud
• ArangoDB
• Couchbase
• Databricks
• DataStax
• InterSystems
• MariaDB
• Redis
• SingleStore
• Tencent Cloud

Read more: 5 Trends in the Database Job Market | Datamation

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What Is Database-as-a-Service (DBaaS)?

Database as a service—often known as DBaaS, cloud database, and managed database—is a managed, online service for creating and building databases through cloud computing. Whereas developers previously relied on physical, on-premises databases to store and manage data, DBaaS offers teams the ability to outsource the underlying software and infrastructure management.

Benefits of DBaaS Solutions

DBaaS solutions offer a range of features, including:

• Ability to scale up and down as needed with provisional storage and compute capacity.
• Remote access, ease of management, and SaaS subscription pricing.
• Avoid long-term database deployments and spin up a DBaaS within minutes.
• Range of database types and purpose-built engines for development use cases.
• Service-level agreement (SLA) to ensure database uptime and availability.
• Enterprise-grade security, including encryption for data at rest and in transit.
• Reduce overhead with simpler cost management relative to in-house teams.

Also read: Top Cloud Managed Service Providers | Channel Insider

How to Choose a DBaaS Solution

Choosing a DBaaS is no small task between the well-known cloud service providers and a mix of new and legacy innovators in enterprise database technology.

As cloud scalability is a double-edged sword, make sure to consider initial, maintenance, and any recurring costs for solutions. Too many nightmares come to mind where users accidentally or frivolously stack up expenses without realizing it.

For flexibility in types of databases, it’s difficult arguing against the portfolios offered by AWS, Google Cloud, and Microsoft Azure. While each offers a stack of DBaaS engines, the more extensive portfolios and different types of databases can also be overwhelming for targeted-use teams. The market of alternatives offer deep specializations and innovative features that are hard not to consider.

Other considerations include existing cloud services and whether the prospective DBaaS provider specializes in the team’s intended use case. For organizations already working with a CSP, bundling the corresponding cloud database will ease integration and reduce costs. Similarly, the range of DBaaS solutions means plenty of use cases for each, offering purpose-driven teams a narrower list to consider. 

Additional Coverage

• Cloud Customer Beware: 3 Challenges with Cloud Migrations | eWeek
• Companies Using Cloud Database: Top Use Cases | Datamation
• Understanding Database Virtualization: 5 Keys Points | eWeek
• How to Migrate On-Premises SQL Database to Azure | CodeGuru
• 5 Best Data Storage Solutions for BI | TechnologyAdvice