ScyllaDB Alternatives: 9 Best Options Compared for {{ year }}

These are the best alternatives to ScyllaDB depending on your use case:

For Real-Time Analytics (when ScyllaDB struggles):

1. Tinybird
2. ClickHouse®
3. Apache Druid
4. Apache Pinot

For OLTP NoSQL Workloads (ScyllaDB's territory):

5. Apache Cassandra
6. Amazon DynamoDB
7. Google Cloud Bigtable
8. Azure Cosmos DB
9. Apache HBase

ScyllaDB is a high-performance NoSQL database designed for low-latency keyed reads and writes at massive scale. Its shard-per-core architecture delivers predictable p99 latencies even under heavy load, making it excellent for operational workloads: sessions, profiles, shopping carts, feature stores, counters, and recent events.

But searching for "ScyllaDB alternatives" usually happens for one of two reasons:

1. You need a different OLTP NoSQL database (cost, operational complexity, cloud preference)
2. You're using ScyllaDB for analytics and it's painful (aggregations, dashboards, ad-hoc queries)

The second case is far more common—and it's not ScyllaDB's fault. Wide-column databases aren't designed for OLAP workloads. When teams force ScyllaDB to do analytics, they fight against the data model, struggle with compaction, and pay the cost of "doing OLAP with a hammer."

This post separates these two problems and recommends the right tool for each.

Using ScyllaDB for analytics and hitting performance walls? Tinybird is a real-time data platform built on ClickHouse® that handles analytical queries ScyllaDB wasn't designed for—aggregations, dashboards, cohorts, and ad-hoc exploration. Ingest from ScyllaDB via CDC and serve sub-100ms analytical APIs.

1. Tinybird: Real-Time Analytics Platform for ScyllaDB Workloads

Let's start with the most common reason people search for ScyllaDB alternatives: analytics performance.

ScyllaDB excels at keyed access patterns—fetch a user profile by ID, read the last 10 events for a session, increment a counter. But when teams try to use it for:

• Aggregations across millions of rows (GROUP BY, COUNT, SUM)
• Time-series analytics over long historical windows
• Cohort analysis and funnel calculations
• Ad-hoc exploration by dimensions that aren't partition keys
• Real-time dashboards with complex filters

...they hit fundamental architectural limits. Wide-column databases are optimized for partition-based access, not analytical scans. The pain isn't a bug—it's a mismatch.

Why ScyllaDB Struggles with Analytics

ScyllaDB's architecture is designed around the partition key:

• Data distribution depends on hashing the partition key
• Efficient queries must filter by partition key first
• Scanning across partitions is expensive and unpredictable
• Secondary indexes exist but add significant overhead
• Aggregations require touching many SSTables with read amplification

When your workload is "give me this user's data," ScyllaDB is excellent. When your workload is "aggregate all users who did X in the last 30 days," you're fighting the architecture.

How Tinybird Solves This

Tinybird is a real-time data platform built on ClickHouse®—a columnar OLAP engine designed for exactly the workloads ScyllaDB struggles with.

The proven architecture pattern:

• Keep ScyllaDB for operational workloads (keyed reads/writes, low-latency OLTP)
• Stream changes to Tinybird via CDC (ScyllaDB CDC → Kafka → Tinybird) leveraging robust real-time data ingestion pipelines for continuous synchronization.
• Run analytics in Tinybird with sub-100ms query latency on billions of rows

What Tinybird Provides

Columnar Storage for Analytics

• Efficient scans across billions of rows
• Compression optimized for analytical queries (10-100x reduction)
• Vectorized execution for fast aggregations that deliver consistently low latency for analytical workloads.

Integrated Ingestion

• Kafka connectors for streaming CDC data from ScyllaDB, ensuring resilient streaming data ingestion for continuous analytics updates.
• HTTP streaming endpoint for direct event ingestion
• Batch ingestion from S3, BigQuery, Snowflake

SQL Transformations

• Pipes for declarative data transformations
• Materialized views for pre-aggregation
• No custom query languages—standard SQL

Instant API Publication

• Any SQL query becomes a production-ready HTTP endpoint
• Built-in authentication, rate limiting, documentation
• Sub-100ms latency at thousands of concurrent requests

Developer-First Workflow

• Git integration for version control
• CLI for local development
• CI/CD deployment patterns

When Tinybird Is the Right Choice

• Your ScyllaDB aggregations are too slow or impossible
• You need dashboards and analytics over ScyllaDB data
• You want to serve analytical queries as APIs with millisecond latency
• You're building user-facing analytics embedded in your product
• You need ad-hoc exploration across dimensions

Teams building interactive real-time dashboards or embedded analytics experiences benefit from platforms that convert SQL directly into production APIs—eliminating the need to manage caching, rate limiting, and query optimization manually.

The Architecture That Works

ScyllaDB (OLTP) → CDC/Kafka → Tinybird (Analytics)

├── Sessions ├── Dashboards

├── Profiles ├── Cohort analysis

├── Counters ├── Aggregations

└── Keyed lookups └── API endpoints

ScyllaDB does what it's good at. Tinybird handles analytics. No forcing square pegs into round holes.

2. Apache Cassandra: The Original Wide-Column Database

If you're looking for a ScyllaDB alternative for OLTP workloads, Apache Cassandra is the most direct comparison—ScyllaDB was designed as a Cassandra-compatible, higher-performance implementation.

What Cassandra Offers

• Wide-column distributed database with eventual consistency
• CQL compatibility—same query language as ScyllaDB
• Proven at massive scale (Netflix, Apple, Discord)
• Large ecosystem of tools, drivers, and expertise
• Open source with commercial support options (DataStax)

Architecture Comparison

Cassandra uses a similar data model to ScyllaDB:

• Partition key determines data distribution
• Clustering keys order data within partitions
• Replication factor controls durability
• Consistency levels balance latency vs. consistency

The main difference: ScyllaDB's shard-per-core architecture typically delivers lower tail latencies than Cassandra's JVM-based implementation.

When Cassandra Fits

• You want CQL compatibility with a larger community
• JVM expertise is stronger on your team than C++
• You prefer DataStax's ecosystem and tooling
• Operational familiarity matters more than raw performance

Considerations

• Higher tail latencies (p99) than ScyllaDB under load
• JVM tuning adds operational complexity
• Same analytical limitations as ScyllaDB (it's still wide-column)

3. Amazon DynamoDB: Serverless Key-Value at Scale

DynamoDB is AWS's fully managed key-value and document database, designed for single-digit millisecond latency with zero operational overhead.

What DynamoDB Offers

• Fully serverless—no clusters to manage
• Predictable performance at any scale
• On-demand or provisioned capacity models
• Global tables for multi-region replication
• DynamoDB Streams for CDC and event processing

Comparison with ScyllaDB

DynamoDB shares ScyllaDB's key-based access pattern philosophy:

• Partition key (and optional sort key) drive query efficiency
• Designed for keyed lookups, not analytical scans
• Secondary indexes available but with cost implications

Key differences:

• Fully managed vs. ScyllaDB's operational requirements
• AWS-native vs. multi-cloud flexibility
• Per-request pricing vs. cluster-based costs

When DynamoDB Fits

• You're committed to AWS and want minimal operations
• Serverless pricing matches your access patterns
• You need global tables with managed replication
• Operational simplicity is the top priority

Considerations

• AWS lock-in—no on-premise or multi-cloud option
• Cost can spike with unpredictable workloads
• Same analytical limitations—not designed for OLAP
• Query flexibility constrained by partition key design

4. Google Cloud Bigtable: Wide-Column for Massive Scale

Cloud Bigtable is Google's managed wide-column database, powering services like Search, Maps, and Gmail at petabyte scale.

What Bigtable Offers

• Massive scale with consistent low latency
• Wide-column model similar to HBase (Bigtable inspired HBase)
• Managed service on Google Cloud
• Integration with BigQuery, Dataflow, and GCP ecosystem

When Bigtable Fits

• You're committed to Google Cloud
• You need petabyte-scale wide-column storage
• Time-series and IoT workloads at extreme volume
• Integration with BigQuery for analytics (separate system)

Considerations

• GCP lock-in—no multi-cloud option
• Different API than Cassandra/ScyllaDB (HBase-compatible)
• Analytics still requires BigQuery—Bigtable is OLTP-focused
• Operational model differs from ScyllaDB clusters

Bigtable also underpins many Internet of Things (IoT) applications where sensor and telemetry data flow continuously at high velocity. However, turning that raw stream into analytical insight often still requires an OLAP layer like Tinybird or ClickHouse®.

5. Azure Cosmos DB: Multi-Model with Global Distribution

Azure Cosmos DB is Microsoft's globally distributed, multi-model database offering multiple APIs including Cassandra API for CQL compatibility.

What Cosmos DB Offers

• Global distribution with configurable consistency levels
• Multiple APIs: Document, Key-Value, Graph, Cassandra, MongoDB
• Elastic scaling with automatic partition management
• SLA-backed latency, throughput, and availability

Cassandra API Compatibility

Cosmos DB's Cassandra API lets you use CQL and existing drivers:

• Wire protocol compatibility for easier migration
• Managed service without cluster operations
• Global distribution built-in

When Cosmos DB Fits

• You're committed to Azure and need global distribution
• Multi-model flexibility matters for your architecture
• You want Cassandra compatibility with managed operations
• SLA guarantees are critical for your business

Considerations

• Request Unit (RU) pricing can be complex to estimate
• Cassandra API isn't 100% compatible—check feature matrix
• Vendor lock-in to Azure's implementation
• Analytics workloads still need separate systems

6. Apache HBase: Wide-Column on Hadoop

Apache HBase is a wide-column database built on the Hadoop ecosystem, offering strong consistency and random read/write access on HDFS.

What HBase Offers

• Hadoop ecosystem integration (HDFS, MapReduce, Spark)
• Strong consistency (unlike Cassandra's eventual consistency)
• Proven at scale in on-premise big data deployments
• Open source with mature tooling

When HBase Fits

• You have existing Hadoop infrastructure
• Strong consistency is required for your use case
• On-premise deployment is preferred
• Your team has Hadoop ecosystem expertise

Considerations

• Operational complexity is significant
• Hadoop dependency adds infrastructure weight
• Not cloud-native—managed options exist but less mature
• Same analytical limitations as other wide-column databases

7. ClickHouse®: Self-Managed OLAP Engine

If your ScyllaDB pain is analytics, and you want to self-manage the OLAP layer, ClickHouse® is the columnar database that powers Tinybird.

What ClickHouse® Offers

• Columnar storage optimized for analytical queries
• Vectorized execution for fast aggregations
• Extreme compression (10-100x typical)
• SQL interface—no proprietary query language
• Open source with active development

When Self-Managed ClickHouse® Fits

• You have dedicated database engineering resources
• You want maximum control over configuration and tuning
• Cost optimization through self-hosting matters
• You're comfortable with operational complexity

Considerations

• Significant operational burden—cluster management, upgrades, monitoring
• Expertise required for MergeTree tuning, sharding, replication
• No built-in API layer—you build and maintain the API yourself
• Integration work—you wire up ingestion pipelines manually

Why Teams Choose Tinybird Over Self-Managed ClickHouse®

Tinybird provides ClickHouse®'s analytical power without the operational burden:

• Managed infrastructure—no cluster operations
• Integrated ingestion—Kafka, S3, HTTP streaming built-in
• Instant APIs—SQL query to HTTP endpoint in one click
• Developer workflow—Git, CLI, CI/CD integration

8. Apache Druid: Real-Time Analytics Database

Apache Druid is an OLAP database designed for low-latency analytics on streaming and batch data.

What Druid Offers

• Real-time ingestion from Kafka and other streams
• Sub-second queries on large datasets
• High concurrency for user-facing analytics
• Time-series optimized with automatic roll-ups

When Druid Fits

• You need real-time analytics with streaming ingestion
• High concurrency (many users querying simultaneously)
• Time-series and event data are primary workloads
• You have engineering capacity to operate it

Considerations

• Operational complexity is significant (multiple node types)
• Learning curve for optimal data modeling
• No built-in API layer—requires additional development
• Resource-intensive compared to simpler solutions

9. Apache Pinot: User-Facing Analytics at Scale

Apache Pinot is a distributed OLAP datastore built for low-latency, high-throughput analytical queries, originally developed at LinkedIn.

What Pinot Offers

• Sub-second queries at high concurrency
• Real-time and batch ingestion support
• Upsert support for mutable data
• Star-tree indexes for pre-aggregation

When Pinot Fits

• You're building user-facing analytics at LinkedIn scale
• High concurrency is a primary requirement
• You need upserts for mutable analytical data
• You have engineering resources for operation

Considerations

• Operational complexity requires dedicated expertise
• Smaller community than Druid or ClickHouse®
• No built-in API layer—you build the API yourself
• Learning curve for optimal configuration

Decision Framework: Choosing Your ScyllaDB Alternative

First: Identify Your Actual Problem

ScyllaDB doesn't have a "replacement"—it has alternatives for different problems:

If your problem is OLTP/operational workloads:

• You need a different wide-column or key-value database
• Consider: Cassandra, DynamoDB, Bigtable, Cosmos DB, HBase
• These compete with ScyllaDB in its core use case

If your problem is analytics:

• You need an OLAP engine, not another OLTP database
• Consider: Tinybird, ClickHouse®, Druid, Pinot
• This is the more common pain point

By Use Case

• Keyed lookups at massive scale, minimal ops → DynamoDB
• Cassandra compatibility with better latency → Keep ScyllaDB
• Strong consistency on Hadoop → HBase
• Global distribution on Azure → Cosmos DB
• Aggregations, dashboards, analytical APIs → Tinybird
• Self-managed OLAP with full control → ClickHouse®

By Operational Preference

• Fully managed, zero ops → DynamoDB, Cosmos DB, Tinybird
• Self-managed with cloud flexibility → Cassandra, ClickHouse®
• On-premise big data → HBase, Cassandra

By Cloud Platform

• AWS-native → DynamoDB
• GCP-native → Bigtable
• Azure-native → Cosmos DB
• Multi-cloud or cloud-agnostic → Cassandra, Tinybird

If you’re still assessing the best database for real-time analytics, focus on the architecture that minimizes time from ingestion to insight. Systems purpose-built for analytics—like Tinybird—deliver end-to-end efficiency that wide-column OLTP databases can’t match.

Why Tinybird Is the Best ScyllaDB Alternative for Analytics

After reviewing all options, one pattern emerges: most teams searching for "ScyllaDB alternatives" are struggling with analytics, not with ScyllaDB's core OLTP capabilities.

ScyllaDB is excellent at what it's designed for—keyed reads and writes with predictable low latency. The problem is teams using it for analytics workloads it was never meant to handle.

The Real Problem: OLTP vs. OLAP

ScyllaDB is an OLTP database optimized for:

• Single-key lookups in single-digit milliseconds
• Write-heavy workloads with LSM-tree storage
• Horizontal scaling for operational data

Analytical queries require OLAP architecture:

• Full-table scans across billions of rows
• Aggregations (GROUP BY, COUNT, SUM, AVG)
• Multi-dimensional filtering without partition key constraints
• Historical analysis over long time windows

These are fundamentally different workloads. No amount of ScyllaDB tuning makes it good at analytics—the architecture doesn't support it.

Why Tinybird Solves This Better Than Other Options

Tinybird is purpose-built for the workload ScyllaDB struggles with.

Unlike switching to another OLTP database (which has the same analytical limitations), Tinybird uses ClickHouse®—a columnar OLAP engine designed specifically for analytical queries at scale.

What that means in practice:

• Queries that timeout in ScyllaDB return in under 100 milliseconds in Tinybird
• Aggregations across billions of rows execute in real-time
• Ad-hoc exploration works without partition key constraints
• Dashboards and APIs serve thousands of concurrent users

The Integration Is Clean

You don't have to rip out ScyllaDB. The proven architecture:

1. Keep ScyllaDB for operational workloads (sessions, profiles, counters)
2. Stream changes via CDC (ScyllaDB CDC → Kafka → Tinybird)
3. Run all analytics in Tinybird (dashboards, APIs, exploration)

Each system does what it's designed for. ScyllaDB handles OLTP. Tinybird handles OLAP. Data stays synchronized in real-time.

From Query to API in Seconds

This is Tinybird's key differentiator. Every SQL query instantly becomes a production HTTP endpoint:

• Built-in authentication and rate limiting
• Auto-generated documentation
• Horizontal scaling handled automatically
• Sub-100ms latency guaranteed

For teams building user-facing analytics, embedded dashboards, or real-time product metrics, this eliminates months of backend development.

Predictable Costs

ScyllaDB analytics workloads often spike cluster costs—more nodes, more compaction, more operational headaches.

Tinybird offers fixed monthly pricing:

• Free tier to start without cost
• Developer plan at $25/month
• Scalable Enterprise plans

No per-query billing surprises. No cluster scaling emergencies.

Start in Minutes

1. Sign up at tinybird.co
2. Connect your data—Kafka for CDC, or direct ingestion
3. Write SQL queries against your data
4. Publish as APIs with one click

Most teams have their first analytical API running in under an hour.

If your ScyllaDB alternative search is really about analytics performance, Tinybird is the answer.

Frequently Asked Questions (FAQs)

Is there a drop-in replacement for ScyllaDB?

For OLTP workloads, Apache Cassandra is the most compatible alternative (same CQL, same data model). DynamoDB, Bigtable, and Cosmos DB offer similar capabilities with different tradeoffs.

For analytics workloads, there's no drop-in replacement because ScyllaDB isn't designed for analytics. You need an OLAP engine like Tinybird, ClickHouse®, Druid, or Pinot.

Can I use ClickHouse® instead of ScyllaDB?

Not as a direct replacement. ClickHouse® is an OLAP database for analytics. It's not designed for low-latency keyed lookups or write-heavy operational workloads.

The right pattern: Keep ScyllaDB for OLTP, add ClickHouse® (or Tinybird) for analytics, connect via CDC.

Why do my ScyllaDB aggregations perform poorly?

ScyllaDB's wide-column architecture is optimized for partition-based access:

• Efficient: Queries that filter by partition key
• Inefficient: Aggregations across partitions, ad-hoc filters, full scans

This is by design, not a bug. For aggregations, you need a columnar OLAP engine like Tinybird.

How do I get data from ScyllaDB to Tinybird?

Use ScyllaDB's CDC (Change Data Capture) feature:

1. Enable CDC on your ScyllaDB tables
2. Stream changes to Kafka (or consume directly)
3. Ingest into Tinybird via Kafka connector
4. Query and publish APIs in Tinybird

This keeps both systems synchronized in real-time without batch ETL delays.

Should I replace ScyllaDB or add an analytics layer?

For most teams, adding an analytics layer is the right approach:

• Lower risk—ScyllaDB keeps doing what it's good at
• Cleaner architecture—OLTP and OLAP separated by design
• Faster implementation—no migration of operational data

Tinybird is designed for exactly this pattern: complement ScyllaDB, don't replace it.

Is self-managed ClickHouse® cheaper than Tinybird?

Infrastructure costs can be lower self-hosted. But total cost includes:

• Engineering time for cluster setup and maintenance
• Operational burden for upgrades, monitoring, troubleshooting
• API development and maintenance
• Integration work for ingestion pipelines

For most teams, Tinybird's managed approach costs less when you include engineering time. Plus you get instant APIs that would take months to build yourself.