11 ClickHouse alternatives to consider in 2026

What is ClickHouse?

TL;DR: ClickHouse alternatives are OLAP and real-time analytics databases for fast queries on large datasets. Best for: Instaclustr Managed ClickHouse for managed open source, BigQuery for serverless scale, Apache Pinot for user-facing analytics, and DuckDB for local analytics.

ClickHouse is an open source columnar database developed by Yandex. It is used for online analytical processing (OLAP) and known for managing large volumes of data with high performance. ClickHouse achieves this by storing data in a compressed format and executing queries directly on the compressed data.

This allows for fast query execution and efficient storage usage, making it suitable for environments where rapid data analysis and minimal latency are critical. ClickHouse is known for its high throughput and low response times, especially in real-time analytics scenarios.

ClickHouse supports SQL queries and offers compatibility with various data formats, contributing to its flexibility and ease of integration. The database provides horizontal scalability, allowing it to handle increasing data volumes by distributing load across multiple nodes. Its ability to process billions of rows per second helps optimize analytical workloads.

ClickHouse Alternatives At a Glance

The table below summarizes the key differences between the tools covered in this article. We explore each of them in more detail below.

Managed Data Platforms and Data Warehouses1. Instaclustr Managed ClickHouseTeams that want production-ready ClickHouse without operating itFully managed OLAP clusters with 24/7 support and SLAsDepends on the Instaclustr platform and managed-service model

Key features of ClickHouse

Column-Oriented Storage

ClickHouse stores data by columns rather than by rows. In a column-oriented layout, values from the same column are stored together on disk. This design is highly efficient for analytical queries because most OLAP workloads read only a small number of columns from very large tables. Instead of scanning entire rows, ClickHouse reads only the required columns, reducing disk I/O and improving query speed.

Columnar storage also improves data compression. Since values in the same column often share similar patterns and data types, compression algorithms work more effectively. Better compression reduces storage costs and allows more data to fit into memory and cache layers. This combination of reduced storage usage and faster reads is one of the main reasons ClickHouse performs well on large analytical datasets.

High-Performance Analytics

ClickHouse is optimized for high-speed analytical processing on large volumes of data. The database uses vectorized execution, which processes batches of rows together instead of one row at a time. It also executes queries in parallel across CPU cores, allowing complex aggregations and filtering operations to complete quickly even on tables containing billions of records.

The system is designed for workloads such as reporting, observability, business intelligence, and event analytics. Functions for aggregation, grouping, and time-series analysis are heavily optimized. ClickHouse can process large scans and calculations with low latency, making it suitable for dashboards and applications that require near-instant analytical responses.

Real-Time Data Processing

ClickHouse supports high-speed data ingestion and near real-time querying. Data inserted into tables becomes available for analysis almost immediately, allowing organizations to analyze logs, metrics, transactions, and user events with minimal delay. This makes the database useful for monitoring systems, fraud detection, and operational analytics platforms.

The database integrates well with streaming systems such as Apache Kafka and Redpanda. These integrations allow ClickHouse to continuously consume and process event streams without requiring complex batch pipelines. Combined with fast query execution, this enables real-time dashboards and alerting systems that rely on continuously updated data.

SQL Support

ClickHouse supports SQL as its primary query language, making it accessible to developers, analysts, and data engineers. Users can perform filtering, sorting, joins, aggregations, subqueries, and window functions using familiar SQL syntax. This reduces the learning curve for teams migrating from traditional relational databases or data warehouses.

The database also includes SQL extensions designed for analytical workloads. These extensions provide functions for arrays, time-series analysis, approximate calculations, and statistical operations. Because of its SQL compatibility, ClickHouse integrates easily with business intelligence tools, reporting platforms, and data visualization systems.

Distributed Query Execution

ClickHouse supports distributed deployments that span multiple servers or clusters. Data can be partitioned and replicated across nodes, allowing the system to scale horizontally as storage and processing requirements grow. Distributed tables enable queries to run across all nodes in parallel, significantly improving performance for large datasets.

The distributed architecture also improves availability and fault tolerance. If one node becomes unavailable, replicated data can still be accessed from other nodes in the cluster. This design allows organizations to build highly scalable analytical systems capable of handling increasing workloads without relying on a single machine.

Materialized Views

Materialized views in ClickHouse automatically process and store transformed or aggregated data during insertion. Instead of calculating expensive aggregations every time a query runs, the database precomputes results and stores them in a separate table. This improves query performance for frequently accessed reports and dashboards.

Materialized views are commonly used for rollups, metrics aggregation, and data transformation pipelines. For example, raw event data can be automatically aggregated into hourly or daily summaries as it enters the database. This reduces query complexity and lowers computational overhead during analysis, especially in real-time analytics environments.

Limitations of ClickHouse

While ClickHouse offers some advantages, it also comes with limitations that users should be aware of. These limitations were reported by users on the G2 platform:

• Steep learning curve for advanced usage: Multiple users reported that ClickHouse can be difficult for beginners, especially when working with data modeling, partitioning, sharding, replication, and query optimization. Advanced features often require deeper expertise to use effectively.
• Complex query optimization: Some reviewers noted that optimizing complex analytical queries is not always straightforward. Achieving the best performance may require careful schema design and tuning strategies.
• Resource-intensive infrastructure requirements: Users mentioned that ClickHouse may require larger servers to maintain high performance at scale. Some organizations may need to rely on cloud deployments, which can increase operational costs.
• Limited support for data updates and modifications: Editing or updating existing records can be cumbersome. ClickHouse is optimized for analytical workloads rather than transactional update-heavy operations.
• Deduplication management challenges: One reviewer highlighted issues with deduplication strategies and limited visibility into how deduplication processes are handled internally.
• Materialized view limitations: Although materialized views improve performance, users reported that they can be difficult to configure for complex use cases. Some users also noted limitations around joins in materialized views.
• Operational complexity in distributed environments: Users experienced difficulties managing distributed features such as ZooKeeper integration, replication, and sharding configuration.
• Documentation gaps for advanced scenarios: While the documentation is generally considered strong, some reviewers felt that advanced use cases could be explained in more detail to simplify onboarding and troubleshooting.
• Limited extensibility for custom functions: Some users reported that the ability to create custom functions is limited compared to other database systems.

ClickHouse vs open source alternatives

ClickHouse operates on an open source license, which means users and organizations like NetApp Instaclustr are free to use, contribute updates and patches, and offer managed services for ClickHouse under a different logo.

Although ClickHouse is currently open source, there is always a risk that the founding company will revoke its open source license and change it to a commercial license. Most recent examples of this include Redis revoking its open source license in 2024 and Elasticsearch switching to a commercial license in 2021, leading to the forking and creation of open source Valkey and OpenSearch, respectively.

Related content: Read our guide to ClickHouse vs Elasticsearch

Notable ClickHouse alternatives

How we selected these tools: We shortlisted ClickHouse alternatives based on their ability to run OLAP and real-time analytical queries at scale, including columnar storage, fast aggregation, streaming and batch ingestion, distributed query execution, and managed or self-hosted deployment options.

Managed Data Platforms and Data Warehouses

1. Instaclustr Managed ClickHouse

Best for: Teams that want production-ready ClickHouse without operating it

Strengths: Fully managed OLAP clusters with 24/7 support and SLAs

Things to consider: Depends on the Instaclustr platform and managed-service model

Instaclustr for ClickHouse is a fully managed version of the Apache 2.0-licensed open source ClickHouse. It handles deployment, scaling, security, monitoring, and updates so teams can run analytical workloads without managing the underlying infrastructure. The service is 100% open source and can run in the customer’s own cloud provider account or in Instaclustr’s account, as well as on-premises and in hybrid environments.

Clusters can be provisioned through a console, an API, or a Terraform provider. The platform adds operational features around ClickHouse such as built-in monitoring, automated backups, tiered storage, and expert support, and offers availability SLAs of up to 99.99% for enterprise deployments.

Key features include:

• Fully managed operations: Instaclustr manages deployment, scaling, security, monitoring, patching, and version upgrades for ClickHouse clusters. Provisioning is available through a console, an API, and a Terraform provider, and clusters can be created in minutes without advanced database expertise.
• Deployment flexibility: Clusters can run in the customer’s own cloud provider account or in Instaclustr’s account, and can also be deployed on-premises or in hybrid setups. This lets teams keep data within their own environment while still using a managed service.
• Tiered storage integration: Through the AWS FSx for NetApp ONTAP integration, the service can automatically tier data, moving colder data to lower-cost storage while keeping active data on high-performance storage. This controls storage spending for large datasets.
• Backups and high availability: The platform provides built-in redundancy, automated and manual backups, and high availability. Data can be restored from previously taken backups, and enterprise deployments are covered by availability SLAs of up to 99.99%.
• Zero downtime scaling: Clusters can be scaled horizontally by adding or removing nodes to match workload changes without interrupting service. Proactive monitoring and health checks run continuously, with alerts routed to the support team.
• Security and compliance: The service includes enterprise-grade encryption and is certified for SOC 2, ISO 27001, and ISO 27018, with PCI-DSS and HIPAA compliance. Security controls are built in rather than requiring custom implementation.

Limitations (as reported by users on G2):

• Some technologies offered as consulting only: A portion of the supported open source technologies are available through consulting engagements rather than as part of the automated managed platform, which can affect teams expecting every option to be self-service.
• Initial schema and architecture guidance needed: Getting started can require support involvement to set up schema and architecture correctly, so early deployments may depend on working closely with the support team.
• Learning curve for platform newcomers: Teams new to managed open source data infrastructure may need time to become familiar with the console, provisioning options, and operational model before working independently.

2. Google Cloud BigQuery

Best for: Serverless SQL analytics on large datasets across clouds

Strengths: No infrastructure management with built-in ML and streaming

Things to consider: Usage-based query pricing can be hard to predict

Google Cloud BigQuery is a fully managed, serverless enterprise data warehouse for large-scale analytics. It separates storage and compute so each can scale independently, and it removes the need to provision or manage infrastructure. BigQuery stores data in a columnar format optimized for analytical queries and supports structured and unstructured data.

It works across clouds and supports open table formats such as Apache Iceberg, Delta, and Apache Hudi. BigQuery includes built-in machine learning, geospatial analysis, search, and business intelligence, and integrates with the wider Google Cloud ecosystem for data movement, streaming, and AI workflows.

Key features include:

• Serverless architecture: BigQuery removes the need to provision or manage servers. Its infrastructure scales automatically based on workload, so teams focus on queries and analysis rather than capacity planning or maintenance.
• Separation of storage and compute: Storage and analytics layers operate independently, which lets each scale on its own and reduces resource contention. Pricing reflects this split, with separate charges for storage and for query compute.
• Columnar storage engine: Data is stored in a column-oriented format designed for analytical queries, letting the distributed engine query terabytes in seconds and petabytes in minutes across large datasets.
• Streaming and real-time analytics: Built-in streaming capabilities such as SQL-based continuous queries ingest streaming data and make it queryable immediately. Integrations with Pub/Sub, Dataflow, and Managed Service for Apache Kafka support real-time pipelines.
• Built-in machine learning and AI: Models can be trained, evaluated, and deployed directly in BigQuery using SQL. It also supports vector and text search, embeddings, and integration with Google’s AI services for tasks like summarization and sentiment analysis.
• Data integration and open formats: BigQuery supports ELT ingestion through batch loading, streaming inserts, and change data capture, and can query open table formats including Apache Iceberg, Delta, and Hudi, as well as federated external sources without moving data.

Limitations (as reported by users on G2):

• Cost can escalate on large queries: The usage-based pricing model can become expensive for large-scale queries without careful optimization and cost monitoring, and some users move workloads elsewhere because of this.
• Limited cost visibility before running queries: Estimating the cost or memory a query will consume before execution is not always clear, which makes budgeting for ad-hoc analysis harder.
• Learning curve for advanced features: Query optimization techniques and advanced features require time to learn, and debugging nested queries can be complex for newer users.

Source: Google Cloud

3. Snowflake

Best for: Cross-cloud data warehousing and governed data sharing

Strengths: Managed platform with independent compute and storage scaling

Things to consider: Consumption-based costs need active monitoring

Snowflake is a cloud data platform for analytics, data engineering, AI workloads, and data sharing. It provides managed storage and compute and supports analytical workloads on the same system. The platform handles infrastructure management, scaling, maintenance, and performance updates automatically.

Snowflake runs across multiple cloud providers and regions and includes capabilities for data collaboration, governance, and integrated AI. Compute resources scale independently from storage, and the platform operates on a usage-based consumption model where customers pay for the compute and storage they use.

Key features include:

• Fully managed platform: Snowflake handles infrastructure, scaling, maintenance, and updates automatically. Most generally available features are accessible within an existing account without additional installation.
• Separation of storage and compute: Compute resources scale independently from storage, which allows flexible workload management and lets multiple workloads run without competing for the same resources.
• Cross-cloud deployment: The platform supports deployments and collaboration across multiple cloud providers and regions, so data and workloads are not tied to a single cloud.
• Governed data sharing: Secure data sharing and data clean rooms let teams publish live data to partners or customers without copying or exporting files, with governance applied consistently through Snowflake Horizon.
• Multi-language processing: Through Snowpark, data engineers and scientists can process data using Python, Java, and Scala directly inside the platform, building pipelines and models without moving data to a separate engine.
• Integrated AI and applications: Cortex provides managed access to large language models and vector search, and Native Apps and the Marketplace let teams build, distribute, and connect data applications and third-party data sources.

Limitations (as reported by users on G2):

• Cost visibility can be unclear: Spending visibility can feel unclear at first, and usage can rise quickly if queries are not monitored closely, so teams need to track consumption actively.
• Performance sensitive to data layout: Query performance can suffer with many small files, requiring attention to how data is organized and loaded to keep pipelines efficient.
• Administrative features and support gaps: Some administrative capabilities are not readily available, and a portion of users report inconsistent support experiences.

Source: Snowflake

4. Amazon Redshift

Best for: SQL analytics for teams standardized on AWS

Strengths: Serverless option with deep AWS and lakehouse integration

Things to consider: Cluster management and tuning add operational effort

Amazon Redshift is a cloud data warehouse service for SQL analytics on large-scale datasets. It supports analytics across data warehouses, data lakes, and federated data sources, and offers both provisioned and serverless deployment options. Redshift is optimized for distributed query execution on structured data.

The service integrates with the wider AWS ecosystem, including the lakehouse in Amazon SageMaker, and can query open formats such as Apache Iceberg and Parquet stored in Amazon S3. Zero-ETL integrations move data from AWS databases and streaming services into Redshift for near real-time analytics without building custom pipelines.

Key features include:

• Managed cloud data warehouse: Redshift provides managed infrastructure for analytical workloads with automatic scaling and maintenance. It delivers SQL analytics on large datasets with columnar storage and compression.
• Serverless deployment option: Redshift Serverless runs analytics workloads without provisioning or managing clusters, automatically scaling compute to match demand so teams can start querying without infrastructure setup.
• Lakehouse and open format support: Redshift queries data in open formats such as Apache Iceberg and Parquet stored in Amazon S3 through its integration with the lakehouse in Amazon SageMaker, without moving or duplicating data.
• Zero-ETL and streaming ingestion: Zero-ETL integrations bring transactional data from Amazon Aurora, RDS, and DynamoDB into Redshift, and native streaming integrations ingest data from Amazon Kinesis and Amazon MSK for near real-time analytics.
• In-database machine learning: Redshift ML lets users build, train, and deploy machine learning models using SQL for tasks such as prediction, classification, and regression on warehouse data.
• Security and access controls: The service includes network isolation and fine-grained access controls such as row-level and column-level permissions, with data encryption available at no additional cost.

Limitations (as reported by users on G2):

• Cluster management overhead: The provisioned deployment is not serverless, so teams must manage clusters and nodes themselves, adding operational work compared to fully serverless options.
• Concurrency handling under heavy use: Concurrency issues can appear when many users query at once, requiring separate limits and controls to keep performance stable.
• Query optimization complexity: Getting the best performance depends on understanding columnar storage and tuning table design, which can be challenging for new users.

Source: Amazon

5. Databricks

Best for: Unified data engineering, analytics, and AI on a lakehouse

Strengths: One platform for ETL, warehousing, ML, and governance

Things to consider: Cost and complexity can be high for smaller teams

Databricks is a data and AI platform built around lakehouse architecture, combining data engineering, analytics, machine learning, and AI workflows in one environment. It provides tools for ETL, streaming, data warehousing, governance, business intelligence, and AI development while supporting open data formats and distributed processing.

The platform uses generative AI to help understand the structure of an organization’s data, and offers natural language assistance for search, code writing, and error remediation. Governance and security are applied across data and AI assets through a unified approach.

Key features include:

• Lakehouse architecture: Databricks combines data lake and data warehouse capabilities in one platform, giving an open, unified foundation for data storage, analytics, and AI.
• Unified data and AI workflows: The platform supports data engineering, analytics, machine learning, and AI application development so teams can build pipelines, train models, and serve applications without switching tools or moving data.
• Natural language assistance: AI-powered interfaces simplify querying, data discovery, code generation, and error remediation, letting users search and build using plain language.
• Serverless data warehousing: A serverless data warehouse provides SQL analytics on lakehouse data, and the platform integrates with common BI and visualization tools for reporting.
• Governance and security: Unified governance covers all data, analytics, and AI assets, providing end-to-end controls for security and privacy across workloads, including generative AI development.
• Real-time streaming and sharing: The platform supports streaming data pipelines and open data sharing, along with a marketplace for data, analytics, and AI assets.

Limitations (as reported by users on G2):

• Unpredictable and high cost: Costs can be hard to predict and rise quickly when workloads run longer than expected, which is a particular concern for smaller teams.
• Steep learning curve: The platform can feel complex and overwhelming for new users, especially those without a strong technical background, and takes time to learn fully.
• Requires dedicated expertise: Setting up, managing, and optimizing performance takes technical skill, and some tasks depend on having a team of specialists rather than a single user.

Source: Databricks

Real-Time OLAP Databases

6. Apache Druid

Best for: High-concurrency real-time analytics on event data

Strengths: Sub-second OLAP queries with native streaming ingestion

Things to consider: Multi-component architecture adds operational effort

Apache Druid is a real-time analytics database for fast slice-and-dice OLAP queries on large datasets. It is designed for event-oriented data and powers use cases where real-time ingestion, fast query performance, and high uptime matter, such as analytical application backends and high-concurrency APIs.

Druid combines ideas from data warehouses, time-series databases, and log-search systems. It stores data in a columnar format, ingests from streaming and batch sources, and distributes queries across a cluster of independently scalable services for ingestion, querying, and orchestration.

Key features include:

• Interactive query engine: Druid uses a scatter/gather approach with data preloaded into memory or local storage to avoid data movement and network latency, executing OLAP queries in milliseconds on high-cardinality datasets.
• Native streaming ingestion: Druid integrates directly with Apache Kafka and Amazon Kinesis for query-on-arrival at millions of events per second, with low-latency ingestion and guaranteed consistency, alongside batch ingestion.
• Optimized columnar storage: Ingested data is automatically columnarized, time-indexed, dictionary-encoded, bitmap-indexed, and type-aware compressed, which supports fast filtering and aggregation.
• Elastic distributed architecture: Loosely coupled components for ingestion, queries, and orchestration combine with a deep storage layer to allow scale-up and scale-out, and tiering with quality of service tunes price and performance for mixed workloads.
• Schema auto-discovery and joins: Druid can automatically detect and update column names and data types on ingestion, and supports join operations both during ingestion and at query time.
• SQL support and reliability: A SQL API covers ingestion, transformation, and querying, and automatic data services including continuous backup, automated recovery, and multi-node replication support high availability.

Limitations (based on publicly available sources):

• Operational complexity: Druid’s multi-component architecture with separate ingestion, query, and coordination services requires dedicated engineering time to set up and manage at scale.
• Limited SQL flexibility: It is optimized for slice-and-dice serving rather than broad warehouse-style exploration, and reviewers note constraints in data types and open-ended analytical work.
• BI tool integration gaps: Reported ratings for business intelligence tool integration are modest, indicating some limitations when connecting standard BI tools compared with warehouse engines.

7. Apache Pinot

Best for: User-facing analytics at very high query concurrency

Strengths: Sub-second queries on fresh data with rich indexing

Things to consider: Complex architecture and limited open-format querying

Apache Pinot is an open source distributed OLAP datastore for user-facing and real-time analytics. Originally built at LinkedIn, it delivers sub-second queries on fresh data at high concurrency, and is used for interactive dashboards, customer-facing analytics, and metrics APIs.

Pinot ingests data from streaming sources such as Apache Kafka, Apache Pulsar, and Amazon Kinesis in real time, and from batch sources including Hadoop, Spark, and Amazon S3. It uses columnar storage with several indexing techniques and can combine batch and streaming data into a single table for querying.

Key features include:

• Sub-second queries at high concurrency: Pinot filters and aggregates large datasets with low latencies and can serve hundreds of thousands of concurrent queries per second, which suits analytics embedded directly in products.
• Real-time and batch ingestion: It ingests from Apache Kafka, Apache Pulsar, and AWS Kinesis in real time and from Hadoop, Spark, and S3 in batch, and combines streaming and batch sources into a single table.
• Upserts: Built-in upsert support lets the same record be ingested multiple times while queries return only the latest value, which handles updates and corrections to streaming data.
• Rich indexing options: Pinot offers pluggable indexes including timestamp, inverted, StarTree, Bloom filter, range, text, JSON, and geospatial types that can be tuned for specific query patterns.
• Versatile joins: The engine performs fact/dimension and fact/fact joins on large datasets, with a query engine that handles distributed joins.
• SQL interface and multitenancy: A standard SQL interface is available through a built-in query editor and a REST API, and built-in multitenancy isolates data into logical namespaces for resource management.

Limitations (based on publicly available sources):

• Operational complexity: Pinot’s architecture uses multiple specialized components (controllers, brokers, servers) plus ZooKeeper, and managing segment indexing, retention, and high availability requires significant operational effort.
• Limited open table format support: Pinot has historically been unable to query open table formats such as Apache Iceberg and Apache Hudi directly, which limits lakehouse-style workflows.
• Narrow query profile: It is optimized for well-understood, high-QPS serving queries rather than open-ended ad-hoc SQL, so it often runs alongside another system for broader analysis.

8. StarRocks

Best for: Real-time analytics with complex multi-table SQL joins

Strengths: MPP engine with MySQL compatibility and materialized views

Things to consider: Self-managed operations add complexity at scale

StarRocks is an open source, high-performance analytical database that uses a massively parallel processing (MPP) architecture for real-time, multi-dimensional analysis. It has a fully vectorized execution engine, a cost-based optimizer, and a columnar storage engine that supports real-time updates.

StarRocks ingests real-time and batch data from a range of sources and can analyze data in data lakes directly without migration. It is compatible with the MySQL protocol and standard SQL, so it connects to common BI tools, and it can serve both real-time queries and lakehouse queries with one engine.

Key features include:

• Vectorized MPP engine: StarRocks uses a fully vectorized execution engine with massively parallel processing to deliver sub-second latency on multi-table queries and multi-dimensional analysis at scale.
• Real-time updates and ingestion: Data can be ingested at high speed and updated or deleted in real time without affecting query performance, supporting analytics on fresh data.
• Cost-based optimizer: A cost-based optimizer plans queries, which is particularly effective for complex multi-table joins compared with systems that require denormalization.
• Materialized views: Intelligent materialized views with automatic query rewriting precompute and store results transparently to speed up recurring queries.
• Lakehouse querying: StarRocks can query data directly in lakehouse formats such as Apache Iceberg and Hive with no data migration, building analytics on open formats and standards.
• MySQL compatibility: The database supports ANSI SQL and the MySQL wire protocol, so existing MySQL clients and BI tools such as Tableau and Power BI connect out of the box.

Limitations (based on publicly available sources):

• Self-managed operational complexity: Running StarRocks directly involves meaningful operational work compared with fully managed alternatives, including sizing frontend and backend nodes and maintaining clusters.
• Materialized view planning effort: Getting strong performance depends on deciding which aggregations to materialize and how to design materialized views, which shifts tuning effort into query planning.
• Ecosystem maturity: As a newer project relative to established warehouses, some tooling and integrations are less mature, which can require additional setup work.

Databases and Distributed Systems

9. DuckDB

Best for: Local and embedded analytics on single-node datasets

Strengths: In-process SQL engine that queries files directly

Things to consider: Single-writer model limits concurrent production use

DuckDB is an in-process analytical SQL database for local analytics and embedded data processing. It runs inside an application without a separate database server and queries files, cloud storage, and structured datasets using SQL. It is built for analytical workloads with a columnar execution engine and a lightweight deployment model.

DuckDB installs quickly as a single binary and ships client APIs for major programming languages. It can query formats such as Parquet and JSON directly, spill to disk to handle datasets larger than memory, and is released under the permissive MIT license.

Key features include:

• Embedded database architecture: DuckDB runs in-process inside an application without requiring a separate server, which simplifies setup and removes the need to manage a database service.
• Columnar execution engine: The database uses a column-oriented engine designed for analytical query performance, and can spill to disk to process workloads larger than available memory.
• Direct file querying: DuckDB queries formats such as Parquet, CSV, and JSON directly, including files in cloud storage and data lakes, without importing data into tables first.
• Broad client and format support: It ships idiomatic clients for languages including Python, Java, Go, Node.js, and R, and integrates with formats such as Iceberg, Delta Lake, and Arrow.
• Portable single-binary deployment: DuckDB distributes for all popular operating systems as a single binary that installs in seconds and runs on the same familiar technologies across environments.
• Extensible architecture: An extension mechanism adds features such as spatial queries and remote data access, and many core capabilities are themselves implemented as extensions.

Limitations (based on publicly available sources):

• Single-writer concurrency: DuckDB uses a single-writer model, so multiple processes cannot write to the same database file at once, which makes it unsuitable for multi-user transactional systems.
• Single-node scaling: It is single-node by design, and datasets that exceed a single machine or require distributed queries need external orchestration.
• No built-in access control: There is no built-in user management or row-level security, so any process with file access can read the entire database.

Source: DuckDB

10. ScyllaDB

Best for: Low-latency, high-throughput NoSQL workloads at scale

Strengths: Shard-per-core design with Cassandra and DynamoDB APIs

Things to consider: Source-available license and steep tuning learning curve

ScyllaDB is a distributed NoSQL database for low-latency, high-throughput workloads. It is compatible with the Apache Cassandra and Amazon DynamoDB APIs and uses a shard-per-core architecture to make efficient use of hardware. It is built for distributed deployments, fault tolerance, and write-heavy applications.

ScyllaDB is written in C++ and runs one shard per CPU core with isolated memory and asynchronous I/O, which avoids locking and provides predictable low latency. It can be deployed as a managed cloud service, in a customer VPC, or on-premises, and moved to a source-available license starting in 2025.

Key features include:

• Shard-per-core architecture: Each CPU core independently manages its own memory and storage with a shared-nothing design, which reduces contention and provides linear scalability as cores or nodes are added.
• Cassandra and DynamoDB compatibility: ScyllaDB supports Cassandra Query Language (CQL) and a DynamoDB-compatible API, so applications can switch without rewriting, and it can replace both a database and its cache layer.
• Predictable low latency: Userspace I/O schedulers, internal caching, and workload prioritization keep tail latency low and single-digit millisecond P99s stable under heavy load.
• Elastic scaling with tablets: Tablets partition data into small chunks that rebalance dynamically across the cluster, supporting elastic scaling to handle traffic peaks with lower infrastructure cost.
• High availability: Data is replicated across nodes and availability zones with active-active replication, allowing the system to survive node, rack, or region failures.
• Vector search support: ScyllaDB adds vector search for AI workloads, enabling storage and querying of large volumes of vector embeddings alongside operational data.

Limitations (as reported by users on G2):

• Steep tuning learning curve: The shard-per-core architecture and tuning are difficult for team members unfamiliar with it, and initial cluster setup can take time and effort.
• Monitoring and tooling setup: The native monitoring stack based on Prometheus and Grafana requires manual setup, and integration with popular observability tools lags behind managed database services.
• Limited relational functionality: As a NoSQL database it lacks arbitrary joins and ad-hoc querying, does not yet support full multi-partition ACID transactions, and requires careful data modeling around access patterns.

Source: ScyllaDB

11. CockroachDB

Best for: Distributed SQL for globally available transactional apps

Strengths: PostgreSQL-compatible with automatic horizontal scaling

Things to consider: Configuration and tuning have a learning curve

CockroachDB is a distributed SQL database for cloud-native applications that need high availability, geographic distribution, and horizontal scalability. It is PostgreSQL-compatible and runs across cloud, hybrid, and on-premises environments. The database targets resilience, consistency, and operational simplicity for transactional workloads.

CockroachDB distributes data and queries across multiple nodes while maintaining SQL compatibility and automatic replication. It is offered in serverless, provisioned, and self-hosted forms, with multi-region support, and includes native vector indexing for storing and querying embeddings alongside transactional data.

Key features include:

• Distributed SQL architecture: CockroachDB distributes data and queries across nodes while keeping SQL compatibility, and automatically replicates data three times for durability.
• PostgreSQL compatibility: Applications interact with CockroachDB using PostgreSQL interfaces and tooling, so existing PostgreSQL connections often work without code changes.
• Automatic horizontal scaling: The database scales across nodes without manual sharding, and cloud plans offer on-demand scaling that can scale to zero or provisioned compute up to large vCPU counts.
• High availability and multi-region: Multi-region deployments support availability SLAs up to 99.999%, with data domiciling and resilience to node, zone, and region failures.
• Security and compliance controls: It provides role-based access control, encrypted data in motion, private connectivity, customer-managed encryption keys, row-level security, and support for PCI-DSS and HIPAA requirements.
• Distributed vector indexing: A distributed vector index stores and queries high-dimensional embeddings across a globally distributed cluster with pgvector compatibility, integrating vector and transactional data.

Limitations (as reported by users on G2):

• Configuration complexity: The database can be complex to configure and optimize, particularly for beginners, and tuning for best performance takes time.
• Operational learning curve: Teams have to think differently about how data is distributed and about operational aspects, which is the most challenging part of adopting it.
• Cost at higher scale: Licensing and pricing can be high for larger-scale deployments, and for smaller workloads a standard PostgreSQL setup may be sufficient.

Source: CockroachDB

Conclusion

When selecting a database for analytics and data processing, it is important to consider the specific requirements of your use case, including performance, scalability, ease of integration, and operational complexity. It is critical to evaluate each tool in the context of workload demands, team expertise, and long-term business goals. A well-chosen database can optimize workflows, improve decision-making, and adapt to evolving data needs over time.