These are the best MongoDB alternatives:
- Tinybird
- PostgreSQL
- ClickHouse
- Amazon DynamoDB
- Couchbase
- Apache Cassandra
- Firebase/Firestore
- Azure Cosmos DB
MongoDB has become one of the most popular NoSQL databases, offering flexible document storage, horizontal scalability, and developer-friendly APIs for operational workloads. However, MongoDB is frequently misused for analytics, aggregating metrics, powering dashboards, serving APIs with analytical queries, where its architecture creates significant performance and scalability problems.
Organizations using MongoDB need to understand a critical distinction: MongoDB excels at operational workloads (CRUD operations, document retrieval, transactional updates) but struggles with analytical queries. Running aggregations across millions of documents, calculating metrics for dashboards, or serving APIs with complex analytics creates performance bottlenecks, expensive infrastructure requirements, and frustrated users waiting for slow queries.
Modern data teams need to ask fundamental questions: are you using MongoDB for its intended operational workloads, or are you forcing it to handle analytics? Are you looking for operational database alternatives with better performance or relational capabilities? Or do you need analytics capabilities that MongoDB simply cannot deliver? The answers guide you to the right alternatives.
In this comprehensive guide, we'll explore the best alternatives to MongoDB for 2025, with particular focus on when Tinybird's real-time analytics platform solves the analytics problems that plague MongoDB deployments. We'll help you understand what MongoDB provides, where it falls short, and which alternatives match your actual requirements, whether operational or analytical.
The 8 Best MongoDB Alternatives
1. Tinybird
Tinybird represents a fundamentally different approach for organizations misusing MongoDB for analytics: instead of forcing a document database to handle analytical queries, Tinybird provides a purpose-built real-time analytics platform with columnar storage, sub-100ms queries on billions of rows, and instant APIs.
If you're using MongoDB for dashboards, metrics, aggregations, or analytical APIs, not operational document storage, Tinybird eliminates the performance problems and infrastructure costs.
Key Features:
- Sub-100ms query latency on billions of rows
- Managed ClickHouse® infrastructure with automatic scaling
- Instant SQL-to-API transformation with authentication
- Real-time continuous data ingestion
- SQL-based analytics accessible to analysts
- Columnar storage optimized for analytics
- Incremental materialized views for efficient aggregations
- Zero database infrastructure management
- Built-in monitoring and observability
Pros
Purpose-Built for Analytics vs. Document Database:
- Columnar storage architecture designed for analytical queries from ground up
- Sub-100ms queries on billions of rows without MongoDB's document scanning overhead
- No collection scans or document iteration bottlenecks
- Aggregations and metrics calculated efficiently
- Architecture optimized for analytics, not adapted from operational database
Real-Time Analytics MongoDB Cannot Deliver:
- Continuous data ingestion replaces batch loading to MongoDB
- Sub-100ms interactive queries vs. MongoDB's multi-second aggregations
- True real-time dashboards without performance compromises
- Query billions of events instantly, impossible with MongoDB at scale
- Built for analytics workloads MongoDB struggles with
Dramatic Performance Improvement:
- Queries that take seconds or minutes in MongoDB execute in <100ms
- Aggregations across billions of rows performant
- No expensive MongoDB Atlas clusters with massive instances
- Consistent performance as data grows, MongoDB degrades
- Interactive exploration without waiting
Cost-Effective vs. MongoDB for Analytics:
- Usage-based pricing scales with actual value
- No massive MongoDB instances required for analytics
- Columnar compression reduces storage costs
- Better price-performance by orders of magnitude
- Lower total cost of ownership for analytics
Instant APIs MongoDB Doesn't Provide:
- Every SQL query automatically becomes authenticated API. A concrete example of this pattern is shown in building a real-time ecommerce analytics API with Kafka.
- Serve analytics to applications without custom backend development
- Built-in rate limiting and authentication
- Power dashboards, mobile apps, and integrations directly. This sits between traditional BI tools and real-time systems, a gap explained in business intelligence software vs real-time analytics.
- MongoDB provides database; Tinybird provides complete platform
Managed Infrastructure:
- Fully managed service eliminates database operations
- No MongoDB cluster management, sharding configuration, or replica sets
- Automatic scaling handles growth without intervention
- Zero operational overhead compared to MongoDB complexity
- Focus on analytics, not database administration
SQL Instead of MongoDB Aggregation Framework:
- Standard SQL familiar to analysts and engineers
- No learning MongoDB's aggregation pipeline syntax
- Window functions, CTEs, and complex analytics native
- More expressive than MongoDB query language
- Easier to write, test, and maintain
Incremental Materialized Views:
- Maintain aggregations efficiently as data changes
- Update incrementally without full recomputation
- Replace expensive MongoDB aggregation pipelines
- Fresh results with minimal processing overhead
- Eliminate repeated expensive computations
Developer-First Experience:
- Local development environment with CLI
- Version control with Git for collaboration
- CI/CD integration for automated deployment
- Modern workflows vs. traditional database management
- Test queries locally before deploying
Handles What MongoDB Can't:
- Billions of rows with consistent sub-100ms queries
- Complex aggregations and analytical queries
- High-concurrency analytical workloads
- Real-time metrics and dashboards
- API-backed analytics features
Best for: Organizations using MongoDB for analytics, dashboards showing metrics aggregated from documents, APIs serving analytical queries, operational monitoring with aggregations, usage-based billing calculations, customer-facing analytics, any scenario where MongoDB's analytical performance is inadequate.
When to Consider Tinybird Instead of MongoDB:
- Using MongoDB for analytics (dashboards, metrics, aggregations)
- MongoDB aggregation queries taking seconds or minutes
- Need real-time analytics, not slow document scans
- Infrastructure costs excessive for analytics workloads
- Building APIs serving analytical data
- Analytical query performance critical
- Don't actually need document storage, need analytics
2. PostgreSQL
PostgreSQL is the leading open-source relational database offering ACID transactions, robust SQL support, and mature ecosystem for operational workloads requiring relational capabilities.
Key Features:
- Full ACID transactions
- Relational data model with foreign keys
- Rich SQL support with CTEs, window functions
- Extensibility with custom functions and types
- JSON/JSONB support for semi-structured data
- Mature ecosystem and tooling
- Open source (PostgreSQL license)
Pros
Relational Capabilities:
- Strong consistency and ACID transactions
- Foreign keys and referential integrity
- Joins across tables performant
- Better than MongoDB for relational data
Mature and Stable:
- Decades of development and production use
- Extensive documentation and community
- Well-understood patterns
- Enterprise-ready features
SQL Standard:
- Standard SQL familiar to most developers
- Powerful query capabilities
- Window functions and advanced analytics
- Better query language than MongoDB
Cost-Effective:
- Open source and free
- Managed options available (RDS, Cloud SQL, Aiven)
- Lower licensing costs than commercial alternatives
Cons
Not Designed for Analytics at Scale:
- Row-oriented storage limits analytical query performance
- Multi-second queries on large datasets
- Cannot match columnar databases for analytics
- Struggles with billions of rows
Vertical Scaling Limitations:
- Scales vertically (bigger instances) not horizontally
- Limited for massive datasets
- More expensive to scale than MongoDB
- Single-node bottlenecks
Operational Complexity:
- Self-hosted requires infrastructure management
- Replication and backup configuration
- Performance tuning necessary
- Database administration expertise
Not Real-Time Analytics:
- Better than MongoDB for analytics but still slow
- Not sub-second on large analytical workloads
- Batch-oriented for many use cases
- Can't deliver real-time dashboards at scale
When to Consider Tinybird Instead: If you're using PostgreSQL for analytics, building dashboards, calculating metrics, serving APIs with analytical queries, Tinybird provides dramatically better performance. PostgreSQL better than MongoDB for analytics but still row-oriented with multi-second queries. Tinybird delivers sub-100ms analytical queries that neither PostgreSQL nor MongoDB can match.
3. ClickHouse
ClickHouse is the open-source columnar analytics database that Tinybird uses under the hood, offering exceptional analytical query performance for self-hosted deployments.
Key Features:
- Columnar storage for analytics
- Vectorized query execution
- SQL interface
- Real-time data ingestion
- Horizontal scalability
- Open source (Apache 2.0)
Pros
Exceptional Analytics Performance:
- Sub-second queries on billions of rows
- Columnar architecture optimized for analytics
- Orders of magnitude faster than MongoDB
- Best-in-class analytical database
Open Source:
- Free to use and deploy
- No licensing costs
- Community development
- Active ecosystem
Purpose-Built for Analytics:
- Architecture designed for analytical workloads
- Handles what MongoDB cannot
- Efficient aggregations and metrics
- Real-time analytics capable
SQL Interface:
- Standard SQL with extensions
- Familiar query language
- Powerful analytical functions
- Better than MongoDB aggregation framework
Cons
Operational Complexity:
- Requires managing ClickHouse® clusters
- Understanding distributed systems necessary
- Complex configuration and tuning
- Significant expertise required
No Built-In APIs:
- Provides database, not API layer
- Must build custom APIs for applications
- No instant API generation
- Additional development required
Production Deployment:
- Non-trivial to deploy production-ready
- High availability requires expertise
- Replication and backup configuration complex
- Monitoring and alerting your responsibility
Learning Curve:
- ClickHouse®-specific syntax and functions
- Understanding table engines and merges
- Performance optimization requires expertise
- Different from MongoDB and relational databases
When to Consider Tinybird Instead: Tinybird is managed ClickHouse® with instant APIs and zero operational overhead. If you want ClickHouse® performance to replace MongoDB analytics without managing clusters, learning database internals, or building API layers, Tinybird provides that. Same performance engine, dramatically simpler operations.
4. Amazon DynamoDB
Amazon DynamoDB is AWS's managed NoSQL database offering single-digit millisecond performance, automatic scaling, and serverless architecture for operational workloads.
Key Features:
- Managed NoSQL key-value and document store
- Single-digit millisecond latency
- Automatic scaling
- Global tables for multi-region
- DynamoDB Streams for change capture
- Serverless with pay-per-request pricing
Pros
Performance for Operational Workloads:
- Consistent single-digit millisecond latency
- Better than MongoDB for key-value access
- High throughput for CRUD operations
- Auto-scaling handles traffic spikes
Fully Managed:
- No infrastructure to operate
- AWS handles scaling and availability
- Zero operational overhead
- Serverless model simplifies architecture
AWS Integration:
- Native integration with Lambda, S3, etc.
- Simplified security with AWS IAM
- Works naturally in AWS architectures
Predictable Performance:
- Consistent latency at any scale
- No performance degradation
- Capacity planning simpler than MongoDB
Cons
Limited Query Capabilities:
- No complex queries or aggregations
- Must query by primary key or secondary index
- Even more limited than MongoDB for analytics
- Not suitable for analytical workloads
AWS Lock-In:
- Completely tied to AWS ecosystem
- No multi-cloud flexibility
- Migration away difficult
- Vendor lock-in concerns
Cost Model:
- Can become expensive with high throughput
- Costs less predictable than MongoDB
- Requires understanding pricing model
- Optimization necessary
Not for Analytics:
- No aggregation capabilities
- Cannot run analytical queries
- Worse than MongoDB for analytics
- Purely operational database
When to Consider Tinybird Instead: DynamoDB excels at operational workloads but has zero analytics capabilities, worse than MongoDB. If you need analytics on your data, DynamoDB can stream changes to Tinybird via DynamoDB Streams. Use DynamoDB for operations, Tinybird for analytics, each excelling at its purpose.
5. Couchbase
Couchbase is a distributed NoSQL document database offering flexible JSON storage with SQL-like queries (N1QL) and built-in caching for operational workloads.
Key Features:
- Document-oriented like MongoDB
- N1QL SQL-like query language
- Built-in caching layer
- Full-text search capabilities
- Mobile sync with Couchbase Lite
- Multi-dimensional scaling
Pros
SQL-Like Queries:
- N1QL more SQL-like than MongoDB
- Easier for SQL-familiar teams
- More expressive queries
- Better analytics than MongoDB aggregations
Performance:
- Built-in caching provides speed
- Good for operational workloads
- Faster than MongoDB for many patterns
- Memory-first architecture
Flexible Deployment:
- Cloud, on-premises, or hybrid
- Multi-cloud support
- Flexible architecture options
Cons
Still Document Database:
- Similar limitations to MongoDB for analytics
- Not designed for analytical workloads at scale
- Row-oriented storage constraints
- Cannot match columnar databases
Expensive:
- Enterprise licensing costs
- More expensive than MongoDB
- Not cost-effective at scale
- Better suited for enterprises with budget
Complex:
- Many components and features
- Learning curve significant
- Configuration and tuning complex
- Operational overhead
Not Real-Time Analytics:
- Better than MongoDB but still inadequate
- Analytical queries slow at scale
- Not designed for dashboard workloads
- Wrong tool for analytics
When to Consider Tinybird Instead: Couchbase improves on MongoDB queries with N1QL but still document database with analytical limitations. If you need real-time analytics, Tinybird purpose-built platform delivers sub-100ms queries that neither MongoDB nor Couchbase can match. Use operational databases for operations, Tinybird for analytics.
6. Apache Cassandra
Apache Cassandra is a distributed wide-column NoSQL database designed for massive scale with no single point of failure, optimized for write-heavy workloads.
Key Features:
- Wide-column store architecture
- Masterless distributed architecture
- Linear scalability
- High availability with no single point of failure
- Tunable consistency
- Open source (Apache 2.0)
Pros
Massive Scale:
- Proven at petabyte scale
- Linear scalability with nodes
- No single point of failure
- Better scalability than MongoDB
Write Performance:
- Optimized for write-heavy workloads
- High write throughput
- Append-only architecture
- Good for event logging
High Availability:
- Masterless architecture eliminates single points of failure
- Multi-datacenter replication
- Better availability than MongoDB
- Designed for always-on systems
Open Source:
- Free Apache license
- Community development
- No licensing costs
Cons
Limited Query Capabilities:
- Must query by partition key
- No joins or aggregations
- Even more limited than MongoDB
- Not suitable for analytical queries
Operational Complexity:
- Complex to deploy and operate
- Understanding consistency levels necessary
- Tuning for performance challenging
- Expertise required
Not for Analytics:
- Wide-column model not designed for analytics
- No aggregation capabilities
- Cannot run analytical queries
- Purely operational database
Data Modeling:
- Requires designing for queries upfront
- Inflexible once data model set
- Migration difficult
- More rigid than MongoDB
When to Consider Tinybird Instead: Cassandra optimized for write-heavy operational workloads with zero analytics capabilities. If you need analytics on Cassandra data, stream changes to Tinybird. Use Cassandra for operational writes at scale, Tinybird for real-time analytics, specialized databases for each purpose.
7. Firebase/Firestore
Firebase (Firestore) is Google's managed NoSQL document database designed for mobile and web applications with real-time synchronization and serverless architecture.
Key Features:
- Managed document database
- Real-time synchronization to clients
- Offline support for mobile apps
- Serverless with automatic scaling
- Firebase ecosystem integration
- Pay-per-operation pricing
Pros
Mobile-First:
- Real-time sync to mobile and web apps
- Offline capabilities built-in
- Good for app development
- Simplified mobile backend
Fully Managed:
- Google handles all operations
- Automatic scaling
- Zero infrastructure management
- Serverless architecture
Easy to Use:
- Simple API for developers
- Quick to get started
- Good documentation
- Low barrier to entry
Google Cloud Integration:
- Works with GCP services
- Simplified authentication
- Cloud Functions integration
Cons
Limited Analytics:
- Not designed for analytical queries
- Aggregations limited and slow
- Cannot handle dashboard workloads
- Similar limitations to MongoDB
Cost at Scale:
- Pay-per-operation becomes expensive
- Costs unpredictable with growth
- Not cost-effective for high-volume
- Better for smaller applications
Vendor Lock-In:
- Tied to Firebase/GCP
- Migration difficult
- Proprietary APIs
- No multi-cloud option
Limited Features:
- Basic compared to MongoDB
- Fewer advanced capabilities
- Simple use cases only
- Not suitable for complex applications
When to Consider Tinybird Instead: Firestore designed for mobile app backends, not analytics. If you need analytics on Firestore data, dashboards, metrics, APIs, stream changes to Tinybird via Cloud Functions. Firestore for app operations, Tinybird for real-time analytics.
8. Azure Cosmos DB
Azure Cosmos DB is Microsoft's globally distributed multi-model database offering multiple APIs (SQL, MongoDB, Cassandra, etc.) with single-digit millisecond latency.
Key Features:
- Multi-model (document, key-value, graph, column)
- Multiple API compatibility (MongoDB, Cassandra, SQL)
- Global distribution with multi-region writes
- Single-digit millisecond latency
- Automatic scaling
- Fully managed on Azure
Pros
Multi-Model Flexibility:
- Support for multiple data models
- MongoDB API compatibility for migration
- Flexibility in data modeling
- One platform for various workloads
Azure Integration:
- Native Azure ecosystem integration
- Azure Active Directory authentication
- Works well in Azure architectures
- Unified Azure experience
Global Distribution:
- Multi-region replication built-in
- Global distribution simpler than MongoDB
- Low-latency worldwide
- Disaster recovery features
Fully Managed:
- Microsoft handles operations
- Automatic scaling and availability
- Zero infrastructure management
Cons
Expensive:
- Significantly more expensive than MongoDB
- Request Units (RU) pricing complex
- Costs scale quickly
- Better for enterprises with budget
Azure Lock-In:
- Tied to Microsoft Azure
- No multi-cloud flexibility
- Migration away difficult
- Vendor lock-in
Not Optimized for Analytics:
- Better than MongoDB but still limited
- Analytical queries slow and expensive
- Not designed for dashboard workloads
- Operational database, not analytics
Complexity:
- Many features and options
- Learning curve significant
- RU capacity planning complex
- Optimization challenging
When to Consider Tinybird Instead: Cosmos DB provides managed NoSQL on Azure but struggles with analytics like MongoDB. If you need real-time analytics on Azure, Tinybird provides multi-cloud platform with better analytical performance. Cosmos DB for operations, Tinybird for analytics, each excelling at purpose.
Understanding MongoDB and Why You Might Need an Alternative
Before exploring specific alternatives, it's essential to understand what MongoDB provides and why organizations seek alternatives.
What Is MongoDB:
MongoDB is a document-oriented NoSQL database providing:
- Flexible schema with JSON-like documents (BSON)
- Horizontal scalability through sharding
- Replication for high availability
- Aggregation framework for queries
- Rich query language
- Developer-friendly APIs
- Atlas managed service option
MongoDB excels at operational workloads with flexible document storage and horizontal scaling for CRUD operations.
Common Reasons for Seeking MongoDB Alternatives
Organizations look beyond MongoDB for several compelling reasons:
Poor Analytics Performance: MongoDB's document-oriented architecture and row-based storage make analytical queries extremely slow. Aggregating across millions of documents, calculating metrics, or running complex queries takes seconds or minutes, unacceptable for real-time analytics or user-facing dashboards.
Expensive for Analytics Workloads: Running analytics on MongoDB requires massive infrastructure, powerful instances with substantial memory and compute. What should be fast analytical queries consume excessive resources, making MongoDB prohibitively expensive for analytics use cases.
Wrong Tool for Analytics: MongoDB designed for operational workloads where you query specific documents by ID or simple criteria. Analytical queries, aggregations across all documents, metrics calculations, dashboard queries, work against MongoDB's architecture.
Scaling Challenges: While MongoDB scales horizontally for writes, analytical queries don't benefit from sharding. Aggregations often require scanning entire collections across shards, creating performance bottlenecks that throwing hardware at doesn't solve.
Need Relational Capabilities: Some workloads need ACID transactions, foreign keys, and joins that relational databases handle better than MongoDB's document model.
Cost: MongoDB Atlas pricing becomes expensive at scale, especially for analytics workloads requiring powerful instances and substantial storage.
The Operational vs. Analytics Database Question
The most critical decision is understanding whether you need operational or analytical capabilities:
You Need Operational Database When:
- CRUD operations on individual documents/records
- Transactional workloads with consistency requirements
- Operational queries by ID or simple criteria
- Real-time reads/writes of specific entities
- Application backend database needs
You Need Tinybird (Analytics Platform) When:
- Aggregating metrics across millions/billions of documents
- Powering dashboards with real-time metrics
- Building APIs serving analytical queries
- Calculating usage, billing, or monitoring metrics
- Real-time operational analytics
- Customer-facing analytics features
The Critical Insight: MongoDB (and alternatives) designed for operational workloads. Using them for analytics creates performance problems no amount of optimization solves. Purpose-built analytics platforms deliver 100x better performance because architecture optimized for analytics from ground up.
The MongoDB Analytics Problem
Why MongoDB Fails at Analytics:
- Row-oriented document storage requires scanning entire collections
- Aggregation framework processes documents sequentially
- No columnar compression or vectorized execution
- Sharding doesn't help analytical queries
- Memory requirements massive for aggregations
Common MongoDB Analytics Antipatterns:
- Running aggregation pipelines for dashboard metrics
- Pre-aggregating data to avoid slow queries (batch paradigm)
- Massive instances trying to speed up analytics
- Complex indexing strategies that don't help aggregations
- Giving up on real-time and running batch jobs
The Solution: Stop forcing MongoDB to do analytics. Use purpose-built analytics platform (Tinybird) for analytics, operational database for operations.
Making the Right Choice
Understanding your actual workload guides the decision:
Ask These Questions:
What's the primary workload?
- Operational CRUD → MongoDB, PostgreSQL, DynamoDB
- Analytics queries → Tinybird
- Both → Separate systems for each
What are you querying?
- Specific documents by ID → Operational databases fine
- Aggregations across collections → Need Tinybird
- Dashboard metrics → Definitely Tinybird
What's the performance requirement?
- Milliseconds for reads/writes → Operational databases
- Sub-second analytical queries → Tinybird
- Real-time dashboards → Tinybird
What's the scale?
- Thousands of documents → MongoDB works
- Millions with analytics → Performance problems start
- Billions with analytics → MongoDB impossible, need Tinybird
Do you need relational features?
- ACID transactions, foreign keys → PostgreSQL
- Flexible schema → MongoDB or alternatives
- Analytics → Tinybird (regardless)
Real-World Scenario Analysis
Scenario 1: Usage Analytics Dashboard
Problem: Show customers their usage metrics in real-time dashboard.
MongoDB Approach:
- Store usage events as documents
- Aggregation pipeline calculates metrics
- Queries take 5-10+ seconds on millions of documents
- Expensive instances required
- Users frustrated with slow dashboards
- Result: Poor performance, high cost
Tinybird Approach:
- Continuous ingestion of usage events
- Sub-100ms queries on billions of events
- Instant APIs power dashboard
- Real-time metrics without delays
- Result: Fast, real-time, cost-effective
Verdict: Tinybird delivers experience MongoDB cannot.
Scenario 2: Application Backend Database
Problem: Store user profiles, sessions, and application data.
MongoDB Approach:
- Flexible document schema
- Fast reads/writes by user ID
- Good for operational workloads
- Result: Appropriate MongoDB use
Tinybird Approach:
- Not designed for operational CRUD
- Analytics platform, not operational database
Verdict: MongoDB appropriate for operational backend.
Scenario 3: Real-Time Monitoring Dashboard
Problem: Monitor system health with metrics on logs and events.
MongoDB Approach:
- Store logs as documents
- Aggregations for metrics extremely slow
- Cannot achieve real-time with MongoDB
- Pre-aggregate data (loses real-time)
- Result: Inadequate for real-time monitoring
Tinybird Approach:
- Continuous log ingestion
- Real-time metrics in SQL
- Sub-100ms queries
- True real-time monitoring
- Result: Actual real-time, simple
Verdict: Tinybird enables real-time operational analytics MongoDB cannot.
Conclusion
MongoDB remains an excellent operational database for flexible document storage, developer-friendly APIs, and horizontal scalability for CRUD operations. However, MongoDB is frequently misused for analytics, where its row-oriented architecture, aggregation framework limitations, and document scanning bottlenecks create significant performance problems.
For organizations using MongoDB for analytics, dashboards showing aggregated metrics, APIs serving analytical queries, operational monitoring with aggregations, Tinybird provides the purpose-built analytics platform that eliminates performance bottlenecks. Sub-100ms queries on billions of rows, columnar storage, instant APIs, and managed infrastructure deliver what MongoDB fundamentally cannot.
Operational alternatives like PostgreSQL (relational), DynamoDB (key-value), or Cassandra (wide-column) serve different operational needs but share MongoDB's analytical limitations. ClickHouse (self-hosted) provides excellent analytics but requires operational expertise.
The right choice depends on your actual workload. For operational document storage, MongoDB and alternatives work well. For analytics, purpose-built platforms deliver orders of magnitude better performance. Understanding this distinction, operational vs. analytical databases, guides you to solutions that excel at their specific purpose rather than compromising for hybrid workloads.
Frequently Asked Questions
Why is MongoDB slow for analytics?
MongoDB's row-oriented document storage architecture. Analytical queries require scanning entire collections, processing documents sequentially through aggregation pipelines. No columnar compression, no vectorized execution, no query optimization for analytics.
Purpose-built analytics databases (Tinybird, ClickHouse®) use columnar storage and vectorized execution, delivering 100x better performance. Architecture matters more than optimization, you can't make row-oriented databases fast for analytics.
Can I use PostgreSQL instead of MongoDB for analytics?
PostgreSQL better than MongoDB for analytics (relational model, better SQL) but still row-oriented with multi-second queries on large datasets. Not suitable for real-time analytics or billions of rows.
If you need real-time analytics, Tinybird provides sub-100ms queries that neither MongoDB nor PostgreSQL can match. Use PostgreSQL for operational workloads requiring relational features, Tinybird for analytics.
Should I migrate from MongoDB to Tinybird?
Depends on what you're using MongoDB for:
- Operational workloads (storing documents, CRUD operations): Keep MongoDB
- Analytics workloads (dashboards, metrics, aggregations): Migrate to Tinybird
Many organizations successfully run both: MongoDB for operational backend, Tinybird for analytics. Stream changes from MongoDB to Tinybird, each database excelling at its purpose.
What if I need both document storage and analytics?
Use specialized databases for each:
- MongoDB (or alternative) for operational document storage
- Tinybird for real-time analytics
Stream data from operational database to Tinybird. This pattern (operational + analytics databases) delivers better performance and cost than forcing one database to do both poorly.
How do I migrate MongoDB analytics to Tinybird?
- Identify analytics workloads (aggregations, dashboards, metrics)
- Configure Tinybird connectors for your data sources
- Migrate aggregation logic to SQL in Tinybird
- Replace slow MongoDB queries with Tinybird instant APIs
- Keep MongoDB for operational workloads
Result: Same analytics functionality, 100x better performance, lower cost. Operational and analytical workloads separated appropriately.