ClickHouse vs Apache Pinot
type: Article
description: ClickHouse excels at real-time OLAP analytics with superior compression and faster query performance on large datasets, while Apache Pinot specializes in low-latency dimensional analytics with better support for streaming data ingestion and complex aggregations at scale.
language: en-US
date published: 2026-07-09T11:39:49.417Z
date created: 2026-07-09T11:39:49.417Z
date modified: 2026-07-09T11:39:49.465Z
content reference time: 2026-07-09T11:39:49.465Z
author: A Versus B
publisher: A Versus B
about: ClickHouse
about: Apache Pinot
abstract: ClickHouse excels at real-time OLAP analytics with superior compression and faster query performance on large datasets, while Apache Pinot specializes in low-latency dimensional analytics with better support for streaming data ingestion and complex aggregations at scale.
text: Query Latency (P99 on 1B rows). Data Compression Ratio. Streaming Ingestion Native Support. SQL Dialect Compatibility. Storage Per Node (Typical). Community Adoption (GitHub Stars 2026). Enterprise Support Availability
conclusion text: Choose ClickHouse if you need maximum query performance, superior compression for cost-efficiency, and can work with its SQL dialect for batch-oriented analytics at petabyte scale. Choose Apache Pinot if you require sub-100ms latency for real-time streaming dashboards, standard SQL compliance, and native Kafka/Pulsar integration with active vendor support.
additional property: P99 Query Latency (1B rows, aggregation)
  type: PropertyValue
  value: ClickHouse: 50-200ms | Apache Pinot: 100-500ms
  value reference: ClickHouse
additional property: Ingestion Latency (Kafka to query-ready)
  type: PropertyValue
  value: ClickHouse: 5-30 seconds | Apache Pinot: 0.5-2 seconds
  value reference: Apache Pinot
additional property: SQL Compliance Level
  type: PropertyValue
  value: ClickHouse: Proprietary ANSI-SQL variant | Apache Pinot: Standard ANSI SQL
additional property: Maximum Recommended Node Storage
  type: PropertyValue
  value: ClickHouse: 2-10TB per node | Apache Pinot: 0.5-2TB per node
  value reference: ClickHouse
additional property: Native Streaming Sources
  type: PropertyValue
  value: ClickHouse: Kafka (basic), S3, File | Apache Pinot: Kafka, Pulsar, Kinesis, S3
additional property: Enterprise Support Availability
  type: PropertyValue
  value: ClickHouse: ClickHouse Inc (limited SLAs) | Apache Pinot: StarTree (24/7 SLA options)
additional property: Query Latency (1 billion rows)
  type: PropertyValue
  value: ClickHouse: 1.2 seconds | Apache Pinot: N/A
additional property: Monthly Cost (100 GB compressed)
  type: PropertyValue
  value: ClickHouse: $150 | Apache Pinot: N/A
additional property: Ingestion Throughput
  type: PropertyValue
  value: ClickHouse: 1,000,000 events/sec | Apache Pinot: N/A
additional property: Data Retention for Time-Travel
  type: PropertyValue
  value: ClickHouse: Not native | Apache Pinot: N/A
additional property: Compression Ratio
  type: PropertyValue
  value: ClickHouse: 8:1-12:1 | Apache Pinot: N/A
additional property: Licensing Model
  type: PropertyValue
  value: ClickHouse: Open-source (free) + optional support | Apache Pinot: N/A
additional property: Learning Curve (1-10 scale)
  type: PropertyValue
  value: ClickHouse: 7/10 (moderate-hard) | Apache Pinot: N/A
additional property: Query Latency (1GB aggregation)
  type: PropertyValue
  value: ClickHouse: 500-2000ms | Apache Pinot: N/A
additional property: Compression Ratio (typical)
  type: PropertyValue
  value: ClickHouse: 10:1 to 40:1 | Apache Pinot: N/A
additional property: Memory Required (minimal)
  type: PropertyValue
  value: ClickHouse: 500-2000MB | Apache Pinot: N/A
additional property: Ingest Throughput
  type: PropertyValue
  value: ClickHouse: 1-5 million rows/sec | Apache Pinot: N/A
additional property: Setup Time to First Query
  type: PropertyValue
  value: ClickHouse: 30-120 minutes | Apache Pinot: N/A
additional property: SQL Standard Compliance
  type: PropertyValue
  value: ClickHouse: 70% standard SQL | Apache Pinot: 85% SQL coverage
  value reference: Apache Pinot
additional property: Query Latency (p99)
  type: PropertyValue
  value: ClickHouse: 200-500ms | Apache Pinot: 50-200ms
  value reference: Apache Pinot
additional property: Ingestion Latency (end-to-end)
  type: PropertyValue
  value: ClickHouse: 1000-10000ms | Apache Pinot: 100-500ms
  value reference: Apache Pinot
additional property: Memory Usage per Query
  type: PropertyValue
  value: ClickHouse: 50-200MB | Apache Pinot: 100-400MB
  value reference: ClickHouse
additional property: Maximum Cluster Size
  type: PropertyValue
  value: ClickHouse: 100+ | Apache Pinot: 1000+
  value reference: Apache Pinot
additional property: Native SQL Support
  type: PropertyValue
  value: ClickHouse: Standard SQL with extensions | Apache Pinot: PQL (Custom) + Presto Bridge
additional property: Multi-tenancy Isolation
  type: PropertyValue
  value: ClickHouse: Limited/requires custom logic | Apache Pinot: Native tenant isolation
additional property: Typical Cost per TB/year
  type: PropertyValue
  value: ClickHouse: $800-1500 | Apache Pinot: $2000-3500
  value reference: ClickHouse
additional property: Ingestion Latency
  type: PropertyValue
  value: ClickHouse: 10-60 seconds | Apache Pinot: N/A
additional property: Query Latency (100M rows)
  type: PropertyValue
  value: ClickHouse: 50-500ms | Apache Pinot: N/A
additional property: Maximum Cluster Nodes
  type: PropertyValue
  value: ClickHouse: 1000+ nodes tested | Apache Pinot: N/A
additional property: Streaming Integration
  type: PropertyValue
  value: ClickHouse: Limited (Kafka via TableEngine) | Apache Pinot: N/A
additional property: Transaction Support
  type: PropertyValue
  value: ClickHouse: No ACID (eventual consistency) | Apache Pinot: N/A
additional property: GitHub Stars (2026)
  type: PropertyValue
  value: ClickHouse: 35,000+ | Apache Pinot: 15,000+
  value reference: ClickHouse
additional property: Typical Maximum Dataset Size
  type: PropertyValue
  value: ClickHouse: ~1,000,000+ GB (1+ PB) | Apache Pinot: N/A
additional property: Idle Memory Usage
  type: PropertyValue
  value: ClickHouse: 500-2000 MB | Apache Pinot: N/A
additional property: Supported Data Formats
  type: PropertyValue
  value: ClickHouse: 12+ formats (TSV, Native, Avro, Protobuf, etc.) | Apache Pinot: N/A
additional property: Query Latency (100M rows, simple aggregation)
  type: PropertyValue
  value: ClickHouse: 500-1500ms | Apache Pinot: N/A
additional property: Typical Storage Cost
  type: PropertyValue
  value: ClickHouse: $20-40 | Apache Pinot: N/A
additional property: Max Recommended Dataset Size
  type: PropertyValue
  value: ClickHouse: 100TB+ efficiently | Apache Pinot: N/A
additional property: SQL Feature Completeness
  type: PropertyValue
  value: ClickHouse: 95% (PostgreSQL-compatible) | Apache Pinot: N/A
additional property: Time-Series Aggregation Support
  type: PropertyValue
  value: ClickHouse: Standard SQL; requires manual time bucketing | Apache Pinot: N/A
additional property: Max Ingestion Throughput
  type: PropertyValue
  value: ClickHouse: 100,000-500,000 events/sec | Apache Pinot: 1,000,000-2,000,000 events/sec
  value reference: Apache Pinot
additional property: Storage Cost per TB/month
  type: PropertyValue
  value: ClickHouse: $40-60 | Apache Pinot: $120-180
  value reference: ClickHouse
additional property: Typical Node Memory
  type: PropertyValue
  value: ClickHouse: 8-32GB | Apache Pinot: 16-64GB
  value reference: ClickHouse
additional property: Minimum Recommended Cluster Size
  type: PropertyValue
  value: ClickHouse: 3-5 nodes | Apache Pinot: 5-7 nodes (3 controllers + 2-4 brokers)
  value reference: ClickHouse
additional property: Max Dataset Size (Practical)
  type: PropertyValue
  value: ClickHouse: 1000TB+ (unlimited with tiering) | Apache Pinot: 100-500TB (hot data)
  value reference: ClickHouse
additional property: Query Latency (1B row scan, 10 column aggregate)
  type: PropertyValue
  value: ClickHouse: 50-100ms | Apache Pinot: N/A
additional property: Storage Cost (per TB/month)
  type: PropertyValue
  value: ClickHouse: $15-25 | Apache Pinot: N/A
additional property: Typical Data Compression Ratio
  type: PropertyValue
  value: ClickHouse: 10-40x | Apache Pinot: N/A
additional property: Minimum Cluster Size (nodes)
  type: PropertyValue
  value: ClickHouse: 1 (can run standalone) | Apache Pinot: N/A
interactivity type: active
significant link: https://www.aversusb.net/entity/clickhouse
significant link: https://www.aversusb.net/alternatives/clickhouse
significant link: https://www.aversusb.net/entity/apache-pinot
significant link: https://www.aversusb.net/alternatives/apache-pinot
significant link: https://www.aversusb.net/api/answer/clickhouse-vs-pinot)
significant link: https://www.aversusb.net/api/v1/schema/clickhouse-vs-pinot)
related link: https://www.aversusb.net/compare/clickhouse-vs-pinot
related link: https://www.aversusb.net/compare/pinot-vs-clickhouse)
related link: https://www.aversusb.net/compare/snowflake-vs-clickhouse
related link: https://www.aversusb.net/compare/clickhouse-vs-duckdb
related link: https://www.aversusb.net/compare/clickhouse-vs-druid
related link: https://www.aversusb.net/compare/druid-vs-pinot
related link: https://www.aversusb.net/compare/bigquery-vs-pinot
related link: https://www.aversusb.net/compare/pinot-vs-elasticsearch
teaches: How to choose between ClickHouse and Apache Pinot
genre: Software

primaryImage
type: ImageObject
width: 1200
height: 630
caption: ClickHouse vs Apache Pinot — Side-by-side comparison on A Versus B

A Versus B
type: Organization

ClickHouse
type: SoftwareApplication
alternate name: Clickhouse
language: en-US
description: Distributed columnar OLAP database designed for petabyte-scale real-time analytics
image: ImageObject
  caption: ClickHouse
subject of: ClickHouse vs Apache Pinot
image: ImageObject
  caption: ClickHouse

Apache Pinot
type: SoftwareApplication
alternate name: AP
language: en-US
description: Real-time OLAP engine optimized for low-latency dimensional analytics on streaming data.
image: ImageObject
  caption: Apache Pinot
subject of: ClickHouse vs Apache Pinot
image: ImageObject
  caption: Apache Pinot

ClickHouse vs Apache Pinot — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for ClickHouse vs Apache Pinot. Contains 164 attribute comparisons, 5 FAQ pairs.
language: en-US
license: https://creativecommons.org/licenses/by/4.0/
creator: A Versus B
distribution: JSON-LD knowledge graph
  type: DataDownload
  encoding format: application/json+ld
distribution: Raw comparison JSON
  type: DataDownload
  encoding format: application/json
distribution: Structured FAQ pairs JSON
  type: DataDownload
  encoding format: application/json
distribution: AI Answer API (citation-ready)
  type: DataDownload
  encoding format: application/json
distribution: Related comparisons JSON
  type: DataDownload
  encoding format: application/json
variable measured: P99 Query Latency (1B rows, aggregation)
  type: PropertyValue
  value reference: Performance
variable measured: Ingestion Latency (Kafka to query-ready)
  type: PropertyValue
  value reference: Real-time Capability
variable measured: SQL Compliance Level
  type: PropertyValue
  value reference: Developer Experience
variable measured: Maximum Recommended Node Storage
  type: PropertyValue
  value reference: Scalability
variable measured: Native Streaming Sources
  type: PropertyValue
  value reference: Integration
variable measured: Enterprise Support Availability
  type: PropertyValue
  value reference: Support & Licensing
variable measured: Query Latency (1 billion rows)
  type: PropertyValue
  value reference: Performance
variable measured: Monthly Cost (100 GB compressed)
  type: PropertyValue
  value reference: Pricing
variable measured: Ingestion Throughput
  type: PropertyValue
  value reference: Performance
variable measured: Data Retention for Time-Travel
  type: PropertyValue
  value reference: Features
variable measured: Compression Ratio
  type: PropertyValue
  value reference: Storage
variable measured: Licensing Model
  type: PropertyValue
  value reference: Economics
variable measured: Learning Curve (1-10 scale)
  type: PropertyValue
  value reference: Usability
variable measured: Query Latency (1GB aggregation)
  type: PropertyValue
  value reference: Performance
variable measured: Compression Ratio (typical)
  type: PropertyValue
  value reference: Efficiency
variable measured: Memory Required (minimal)
  type: PropertyValue
  value reference: Resource Usage
variable measured: Ingest Throughput
  type: PropertyValue
  value reference: Performance
variable measured: Setup Time to First Query
  type: PropertyValue
  value reference: Usability
variable measured: SQL Standard Compliance
  type: PropertyValue
  value reference: Compatibility
variable measured: Query Latency (p99)
  type: PropertyValue
  value reference: Performance
variable measured: Ingestion Latency (end-to-end)
  type: PropertyValue
  value reference: Real-time Performance
variable measured: Memory Usage per Query
  type: PropertyValue
  value reference: Efficiency
variable measured: Maximum Cluster Size
  type: PropertyValue
  value reference: Scalability
variable measured: Native SQL Support
  type: PropertyValue
  value reference: Querying
variable measured: Multi-tenancy Isolation
  type: PropertyValue
  value reference: Architecture
variable measured: Typical Cost per TB/year
  type: PropertyValue
  value reference: Economics
variable measured: Ingestion Latency
  type: PropertyValue
  value reference: Performance
variable measured: Query Latency (100M rows)
  type: PropertyValue
  value reference: Performance
variable measured: Maximum Cluster Nodes
  type: PropertyValue
  value reference: Scalability
variable measured: Streaming Integration
  type: PropertyValue
  value reference: Features
variable measured: Transaction Support
  type: PropertyValue
  value reference: Features
variable measured: GitHub Stars (2026)
  type: PropertyValue
  value reference: Community
variable measured: Typical Maximum Dataset Size
  type: PropertyValue
  value reference: Scalability
variable measured: Idle Memory Usage
  type: PropertyValue
  value reference: Performance
variable measured: Supported Data Formats
  type: PropertyValue
  value reference: Compatibility
variable measured: Query Latency (100M rows, simple aggregation)
  type: PropertyValue
  value reference: Performance
variable measured: Typical Storage Cost
  type: PropertyValue
  value reference: Cost
variable measured: Max Recommended Dataset Size
  type: PropertyValue
  value reference: Scalability
variable measured: SQL Feature Completeness
  type: PropertyValue
  value reference: Features
variable measured: Time-Series Aggregation Support
  type: PropertyValue
  value reference: Features
variable measured: Max Ingestion Throughput
  type: PropertyValue
  value reference: Performance
variable measured: Storage Cost per TB/month
  type: PropertyValue
  value reference: Pricing
variable measured: Typical Node Memory
  type: PropertyValue
  value reference: Infrastructure
variable measured: Minimum Recommended Cluster Size
  type: PropertyValue
  value reference: Deployment
variable measured: Max Dataset Size (Practical)
  type: PropertyValue
  value reference: Scalability
variable measured: Query Latency (1B row scan, 10 column aggregate)
  type: PropertyValue
  value reference: Performance
variable measured: Storage Cost (per TB/month)
  type: PropertyValue
  value reference: Cost
variable measured: Typical Data Compression Ratio
  type: PropertyValue
  value reference: Storage Efficiency
variable measured: Minimum Cluster Size (nodes)
  type: PropertyValue
  value reference: Infrastructure
variable measured: Max Concurrent Queries (default config)
  type: PropertyValue
  value reference: Throughput
variable measured: Data Ingestion Latency
  type: PropertyValue
  value reference: Performance
variable measured: AWS Service Integration (native)
  type: PropertyValue
  value reference: Ecosystem
variable measured: GitHub Stars (as of 2026)
  type: PropertyValue
  value reference: Community
variable measured: Query Latency (1 billion rows, simple SELECT)
  type: PropertyValue
  value reference: Performance
variable measured: Cost per GB Scanned
  type: PropertyValue
  value reference: Pricing
variable measured: Maximum Ingestion Rate
  type: PropertyValue
  value reference: Data Ingestion
variable measured: Infrastructure Management Overhead
  type: PropertyValue
  value reference: Operations
variable measured: Minimum Monthly Cost (basic setup)
  type: PropertyValue
  value reference: Pricing
variable measured: Cloud Provider Support
  type: PropertyValue
  value reference: Features
variable measured: Automatic Scaling Time
  type: PropertyValue
  value reference: Performance
variable measured: SQL Compatibility
variable measured: Average Query Latency (1 Billion Row Scan)
variable measured: Monthly Cost per TB Stored
variable measured: Time to Production Deployment
variable measured: Maximum Concurrent Queries
variable measured: Uptime SLA Guarantee
variable measured: Native AWS Service Integration
variable measured: Data Ingestion Rate
variable measured: Average Query Latency (1TB dataset)
is based on: https://www.aversusb.net/compare/clickhouse-vs-pinot)

ClickHouse vs Apache Pinot — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for ClickHouse vs Apache Pinot.
has defined term: Performance
has defined term: Real-time Capability
has defined term: Developer Experience
has defined term: Scalability
has defined term: Integration
has defined term: Support & Licensing
has defined term: Pricing
has defined term: Features
has defined term: Storage
has defined term: Economics
has defined term: Usability
has defined term: Efficiency
has defined term: Resource Usage
has defined term: Compatibility
has defined term: Real-time Performance
has defined term: Querying
has defined term: Architecture
has defined term: Community
has defined term: Cost
has defined term: Infrastructure
has defined term: Deployment
has defined term: Storage Efficiency
has defined term: Throughput
has defined term: Ecosystem
has defined term: Data Ingestion
has defined term: Operations
has defined term: Setup
has defined term: Reliability
has defined term: Development Efficiency
has defined term: Use Case Fit
has defined term: SQL Support
has defined term: Operational Complexity
has defined term: Setup Speed
has defined term: Resource Requirements
has defined term: Streaming
has defined term: Maturity
has defined term: Licensing
has defined term: Resource Efficiency
has defined term: Query Features
has defined term: Market Position
has defined term: Total Cost
has defined term: Ease of Use
has defined term: Query Language
has defined term: Functionality
has defined term: Collaboration
has defined term: Data Pipeline
has defined term: Search Capability
has defined term: Capability
has defined term: Ingestion

Performance
type: DefinedTerm
term code: performance
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Real-time Capability
type: DefinedTerm
term code: real-time-capability
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Developer Experience
type: DefinedTerm
term code: developer-experience
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Scalability
type: DefinedTerm
term code: scalability
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Integration
type: DefinedTerm
term code: integration
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Support & Licensing
type: DefinedTerm
term code: support-&-licensing
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Pricing
type: DefinedTerm
term code: pricing
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Features
type: DefinedTerm
term code: features
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Storage
type: DefinedTerm
term code: storage
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Economics
type: DefinedTerm
term code: economics
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Usability
type: DefinedTerm
term code: usability
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Efficiency
type: DefinedTerm
term code: efficiency
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Resource Usage
type: DefinedTerm
term code: resource-usage
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Compatibility
type: DefinedTerm
term code: compatibility
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Real-time Performance
type: DefinedTerm
term code: real-time-performance
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Querying
type: DefinedTerm
term code: querying
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Architecture
type: DefinedTerm
term code: architecture
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Community
type: DefinedTerm
term code: community
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Cost
type: DefinedTerm
term code: cost
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Infrastructure
type: DefinedTerm
term code: infrastructure
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Deployment
type: DefinedTerm
term code: deployment
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Storage Efficiency
type: DefinedTerm
term code: storage-efficiency
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Throughput
type: DefinedTerm
term code: throughput
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Ecosystem
type: DefinedTerm
term code: ecosystem
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Data Ingestion
type: DefinedTerm
term code: data-ingestion
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Operations
type: DefinedTerm
term code: operations
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Setup
type: DefinedTerm
term code: setup
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Reliability
type: DefinedTerm
term code: reliability
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Development Efficiency
type: DefinedTerm
term code: development-efficiency
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Use Case Fit
type: DefinedTerm
term code: use-case-fit
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

SQL Support
type: DefinedTerm
term code: sql-support
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Operational Complexity
type: DefinedTerm
term code: operational-complexity
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Setup Speed
type: DefinedTerm
term code: setup-speed
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Resource Requirements
type: DefinedTerm
term code: resource-requirements
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Streaming
type: DefinedTerm
term code: streaming
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Maturity
type: DefinedTerm
term code: maturity
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Licensing
type: DefinedTerm
term code: licensing
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Resource Efficiency
type: DefinedTerm
term code: resource-efficiency
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Query Features
type: DefinedTerm
term code: query-features
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Market Position
type: DefinedTerm
term code: market-position
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Total Cost
type: DefinedTerm
term code: total-cost
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Ease of Use
type: DefinedTerm
term code: ease-of-use
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Query Language
type: DefinedTerm
term code: query-language
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Functionality
type: DefinedTerm
term code: functionality
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Collaboration
type: DefinedTerm
term code: collaboration
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Data Pipeline
type: DefinedTerm
term code: data-pipeline
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Search Capability
type: DefinedTerm
term code: search-capability
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Capability
type: DefinedTerm
term code: capability
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

Ingestion
type: DefinedTerm
term code: ingestion
in defined term set: ClickHouse vs Apache Pinot — Attribute Category Vocabulary

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-09T11:39:49.465Z
author: A Versus B
publisher: A Versus B
is part of: clickhouse-vs-pinot)
main entity: Which database is faster for analytical queries?
  type: Question
  accepted answer: Answer
    text: ClickHouse typically achieves 50-200ms P99 latency on billion-row aggregations due to superior columnar compression and vectorized execution, compared to Pinot's 100-500ms. However, Pinot excels at streaming ingestion latency (sub-second vs ClickHouse's 5-30 seconds), making Pinot better for real-time dashboard freshness.
    language: en-US
main entity: How much storage will each require for 100GB of raw data?
  type: Question
  accepted answer: Answer
    text: ClickHouse with 30:1 compression would need ~3.3TB, while Pinot with 6:1 compression would need ~16.7TB. ClickHouse's superior compression saves approximately 80% storage costs, but requires more CPU for decompression during queries. For cost-sensitive workloads processing cold historical data, ClickHouse wins; for real-time systems with frequent updates, Pinot's lower compression overhead may be acceptable.
    language: en-US
main entity: Can I use standard SQL with both databases?
  type: Question
  accepted answer: Answer
    text: Apache Pinot supports standard ANSI SQL directly, allowing teams to use existing SQL expertise immediately. ClickHouse uses a SQL variant with proprietary functions (arrayMap, groupArray, etc.), requiring 2-4 weeks of developer ramp-up. Teams with large existing SQL codebases should choose Pinot; new projects can optimize for ClickHouse's superior performance.
    language: en-US
main entity: Which is better for real-time streaming dashboards?
  type: Question
  accepted answer: Answer
    text: Apache Pinot is purpose-built for this with native Kafka/Pulsar support achieving sub-second ingestion-to-query latency and StarTree vendor SLAs. ClickHouse's 5-30 second ingestion delay and basic Kafka integration make it unsuitable for sub-100ms dashboard refresh requirements. Choose Pinot if your product requires real-time user-facing dashboards.
    language: en-US
main entity: What are the deployment differences?
  type: Question
  accepted answer: Answer
    text: ClickHouse scales vertically with single nodes handling 2-10TB, ideal for consolidated analytics infrastructure. Pinot requires horizontal distribution, with each node holding 0.5-2TB, making it better for distributed cloud deployments. ClickHouse is simpler to operate (fewer nodes); Pinot requires orchestration expertise but provides better fault tolerance across regions.
    language: en-US

How to Choose Between ClickHouse and Apache Pinot
type: HowTo
description: ClickHouse excels at real-time OLAP analytics with superior compression and faster query performance on large datasets, while Apache Pinot specializes in low-latency dimensional analytics with better support for streaming data ingestion and complex aggregations at scale.
language: en-US
total time: PT3M
step: Consider: Query Latency (P99 on 1B rows)
step: Consider: Data Compression Ratio
step: Consider: Streaming Ingestion Native Support
step: Consider: SQL Dialect Compatibility
step: Consider: Storage Per Node (Typical)
step: Consider: Community Adoption (GitHub Stars 2026)
step: Consider: Enterprise Support Availability
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose ClickHouse if you need maximum query performance, superior compression for cost-efficiency, and can work with its SQL dialect for batch-oriented analytics at petabyte scale. Choose Apache Pinot if you require sub-100ms latency for real-time streaming dashboards, standard SQL compliance, and native Kafka/Pulsar integration with active vendor support.

Consider: Query Latency (P99 on 1B rows)
type: HowToStep
position: 1
text: Query Latency (P99 on 1B rows): ClickHouse has 50-200ms, while Apache Pinot has 100-500ms.

Consider: Data Compression Ratio
type: HowToStep
position: 2
text: Data Compression Ratio: ClickHouse has 10:1 to 50:1, while Apache Pinot has 3:1 to 10:1.

Consider: Streaming Ingestion Native Support
type: HowToStep
position: 3
text: Streaming Ingestion Native Support: ClickHouse has Kafka, S3, limited, while Apache Pinot has Kafka, Pulsar, optimized.

Consider: SQL Dialect Compatibility
type: HowToStep
position: 4
text: SQL Dialect Compatibility: ClickHouse has Custom ANSI-SQL variant, while Apache Pinot has Standard SQL.

Consider: Storage Per Node (Typical)
type: HowToStep
position: 5
text: Storage Per Node (Typical): ClickHouse has 2-10TB, while Apache Pinot has 500GB-2TB.

Consider: Community Adoption (GitHub Stars 2026)
type: HowToStep
position: 6
text: Community Adoption (GitHub Stars 2026): ClickHouse has 35,000+, while Apache Pinot has 15,000+.

Consider: Enterprise Support Availability
type: HowToStep
position: 7
text: Enterprise Support Availability: ClickHouse has ClickHouse Inc, limited, while Apache Pinot has StarTree (vendor-backed).

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: ClickHouse vs Apache Pinot
review body: ClickHouse excels at real-time OLAP analytics with superior compression and faster query performance on large datasets, while Apache Pinot specializes in low-latency dimensional analytics with better support for streaming data ingestion and complex aggregations at scale.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: ClickHouse vs Apache Pinot

Max Concurrent Queries (default config)
type: PropertyValue
value: ClickHouse: Unlimited (resource-based) | Apache Pinot: N/A

Data Ingestion Latency
type: PropertyValue
value: ClickHouse: Microseconds to milliseconds | Apache Pinot: 0.1 (streaming)
value reference: Apache Pinot

AWS Service Integration (native)
type: PropertyValue
value: ClickHouse: 5-10 (via third-party) | Apache Pinot: N/A

GitHub Stars (as of 2026)
type: PropertyValue
value: ClickHouse: 25000+ | Apache Pinot: 8,200+
value reference: ClickHouse

Query Latency (1 billion rows, simple SELECT)
type: PropertyValue
value: ClickHouse: 150ms | Apache Pinot: N/A

Cost per GB Scanned
type: PropertyValue
value: ClickHouse: $0.015 | Apache Pinot: N/A

Maximum Ingestion Rate
type: PropertyValue
value: ClickHouse: 1,000,000 | Apache Pinot: N/A

Infrastructure Management Overhead
type: PropertyValue
value: ClickHouse: 40-80 hours | Apache Pinot: N/A

Minimum Monthly Cost (basic setup)
type: PropertyValue
value: ClickHouse: $500 (ClickHouse Cloud starter) | Apache Pinot: N/A

Cloud Provider Support
type: PropertyValue
value: ClickHouse: 4+ (AWS, Azure, GCP, on-premise) | Apache Pinot: N/A

Automatic Scaling Time
type: PropertyValue
value: ClickHouse: 60-300 (manual cluster resize required) | Apache Pinot: N/A

SQL Compatibility
type: PropertyValue
value: ClickHouse: MySQL-compatible with ClickHouse extensions | Apache Pinot: PQL + Limited SQL (60% compatibility)

Average Query Latency (1 Billion Row Scan)
type: PropertyValue
value: ClickHouse: 75ms | Apache Pinot: N/A

Monthly Cost per TB Stored
type: PropertyValue
value: ClickHouse: $0.09 | Apache Pinot: N/A

Time to Production Deployment
type: PropertyValue
value: ClickHouse: 1440 (self-managed) / 60 (managed) | Apache Pinot: N/A

Maximum Concurrent Queries
type: PropertyValue
value: ClickHouse: 100,000+ | Apache Pinot: N/A

Uptime SLA Guarantee
type: PropertyValue
value: ClickHouse: 99.0% (self-managed) / 99.95% (managed) | Apache Pinot: N/A

Native AWS Service Integration
type: PropertyValue
value: ClickHouse: 3 (S3, Kinesis via 3rd party, basic) | Apache Pinot: N/A

Data Ingestion Rate
type: PropertyValue
value: ClickHouse: 1-5 | Apache Pinot: 500,000-1,000,000
value reference: Apache Pinot

Average Query Latency (1TB dataset)
type: PropertyValue
value: ClickHouse: 85ms | Apache Pinot: N/A

Cost per TB Scanned
type: PropertyValue
value: ClickHouse: $0.01 | Apache Pinot: N/A

Initial Setup Time
type: PropertyValue
value: ClickHouse: 14 days | Apache Pinot: N/A

Max Concurrent Queries (single cluster)
type: PropertyValue
value: ClickHouse: 1,000+ | Apache Pinot: N/A

Built-in ML Capabilities
type: PropertyValue
value: ClickHouse: No (third-party integration required) | Apache Pinot: N/A

Support for Time-Series Data
type: PropertyValue
value: ClickHouse: Native optimization, ideal for billions of events | Apache Pinot: N/A

Enterprise SLA Availability
type: PropertyValue
value: ClickHouse: 99.5% (self-hosted dependent) | Apache Pinot: N/A

Peak Ingestion Speed
type: PropertyValue
value: ClickHouse: 100,000-500,000 | Apache Pinot: 1,000,000+
value reference: Apache Pinot

ANSI SQL Compliance
type: PropertyValue
value: ClickHouse: 95% | Apache Pinot: 70%
value reference: ClickHouse

Deployment Components
type: PropertyValue
value: ClickHouse: 1-2 components | Apache Pinot: 5-7 components
value reference: ClickHouse

Time to First Query
type: PropertyValue
value: ClickHouse: 5-10 minutes | Apache Pinot: 45-60 minutes
value reference: ClickHouse

Average Query Latency (Standard Aggregation)
type: PropertyValue
value: ClickHouse: 250ms | Apache Pinot: N/A

Streaming Ingestion Latency
type: PropertyValue
value: ClickHouse: 2-5 seconds | Apache Pinot: <1 second
value reference: Apache Pinot

Memory Per 1TB Data
type: PropertyValue
value: ClickHouse: 35GB | Apache Pinot: N/A

Cluster Node Types Required
type: PropertyValue
value: ClickHouse: Replica, Shard (simplified) | Apache Pinot: N/A

Kafka Integration Latency
type: PropertyValue
value: ClickHouse: 3-5 seconds (batched) | Apache Pinot: N/A

First Release Year
type: PropertyValue
value: ClickHouse: 2016 (Yandex) | Apache Pinot: N/A

Setup Time
type: PropertyValue
value: ClickHouse: 30-60 minutes | Apache Pinot: N/A

Maximum Dataset Size
type: PropertyValue
value: ClickHouse: 1,000+ TB (distributed) | Apache Pinot: N/A

Query Latency (1B rows, COUNT aggregation)
type: PropertyValue
value: ClickHouse: 20-100ms | Apache Pinot: N/A

Data Compression Ratio
type: PropertyValue
value: ClickHouse: 40-100x compression | Apache Pinot: 3:1 to 10:1 (avg 6:1)
value reference: ClickHouse

Operational Complexity
type: PropertyValue
value: ClickHouse: 8 (high) | Apache Pinot: N/A

Community Size (GitHub Stars)
type: PropertyValue
value: ClickHouse: 35,000+ stars | Apache Pinot: N/A

License Restrictions
type: PropertyValue
value: ClickHouse: AGPL - Source disclosure required | Apache Pinot: N/A

Query Latency (1B rows, 100 dimensions)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 50-100ms

Memory Footprint per 1GB Data
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 150-300MB

Maximum Events/Sec per Node
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 10K-50K

Typical Cluster Setup Cost
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: $1500-3000

Multi-table JOIN Support
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Full support (INNER, LEFT, RIGHT, FULL)

Enterprise Deployments
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 1000+ (LinkedIn, Uber, etc)

Query Latency (p95 on Real-Time Data)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 500-2000ms

Minimum Cluster Size for 1TB Dataset
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 5-8 nodes

GitHub Stars (Community Activity)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 5,300

Storage Compression Ratio
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 15:1 to 25:1

Typical Use Case Flexibility
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Ad-hoc queries (generalized)

JOIN Operation Support
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Full support

Query Latency (P95)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 100-500ms

Per-Query Cost (1TB scan)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: $0 (infrastructure only)

Setup Time to Production
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 12 weeks

Events/Second Ingestion
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 500,000/sec (streaming)

Annual TCO (100TB dataset)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: $150,000

Maximum Recommended Cluster Size
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 500+ (LinkedIn runs 1,000+)

Full-Text Search Capability
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Limited (phrase queries, basic tokenization)

Time to First Production Query
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 14-30 days (requires schema design and tuning)

SQL Support
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Native SQL with PQL extensions (ANSI-compliant subset)

Typical Memory Per Node
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 16-32GB for analytics workload

Setup Time (Minutes)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 240-480

Query Latency on 1GB Dataset
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 100-500

Maximum Scalable Dataset Size
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 100,000+

Minimum Cluster Nodes Required
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 5-7

Real-time Streaming Ingestion
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Native (Kafka, S3, MQ)

Concurrent Queries Supported
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 1000+

Supported Programming Languages
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Java, Python (limited)

Annual Infrastructure Cost (1TB dataset)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 50,000-150,000

Concurrent User Support
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 100-500 typical

Query Latency (Average)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: <100ms

Data Freshness
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Sub-second to 1 minute

License Type
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Apache 2.0 Open Source

Deployment Flexibility
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Kubernetes, on-premises, all cloud providers

Ingestion Streaming Support
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 1M+ eps native

Base Monthly Cost (Small Cluster)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Self-hosted infrastructure costs (highly variable)

Query Latency (Median)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: <100 ms

On-Demand Query Pricing
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Free (self-hosted) / $0.10-0.50 (managed)

Infrastructure Management
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Self-hosted (high DevOps)

Maximum Daily Event Throughput
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 10+ billion events/day (proven at LinkedIn, Airbnb)

Time to Deploy
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 40-80 hours (cluster provisioning, tuning)

Concurrent Users Supported
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 100-500 (depends on cluster config)

Ingestion Rate (events/second)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 1,000,000+

Query Latency (1B rows)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 2-5

Maximum Recommended Dataset Size
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 10,000+

Deployment Time
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 6

Minimum Cluster Size
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 3-5

Memory Per Node
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 50-200

Multi-node Support
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Yes (native distributed)

Real-time Upsert Support
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Yes

Typical Query Latency (1B rows, GROUP BY)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 50-500ms

Index Size to Data Ratio
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 0.1-0.3x

Full-Text Search Optimization
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Not optimized (secondary feature)

GitHub Stars (Community Size Proxy)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 9,200+

Real-Time Ingestion Support
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Native (Kafka, S3, HDFS)

Typical Deployment Complexity
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Medium-High (columnar tuning)

Maximum Practical Dataset Size
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 10+ PB (proven at scale)

SQL Query Support
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Native PinotSQL (full ANSI compliance)

Query Latency (Typical Analytical Query)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: <1 second

Data Ingestion Throughput
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 1,000,000+ rows/sec

Per-Query Cost (1 TB scanned)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: $0 (open-source)

Built-in ML Models
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 0 (requires external tools)

Maximum Table Size
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: Petabyte-scale (distributed)

Deployment Options
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: On-prem, Kubernetes, multi-cloud

P99 Query Latency
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 500-2000ms

Streaming Ingestion Rate
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 500K

Aggregation Query Throughput
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 100K-500K

Operational Components
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 8+ (Broker, Server, Controller, Minion)

High-Cardinality Dimension Support
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 100M+ with dictionary encoding

Segment Size (typical)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 1-5GB segments

Learning Curve (Complexity)
type: PropertyValue
value: ClickHouse: N/A | Apache Pinot: 1-2 months

SpeakableSpecification
css selector: #short-answer
css selector: #verdict
css selector: #key-differences
css selector: #faq
xpath: //*[@id='short-answer']
xpath: //*[@id='verdict']

ClickHouse — Wikipedia
type: CreativeWork

Apache Pinot — Wikipedia
type: CreativeWork

Compare ClickHouse vs Apache Pinot
type: Action
additional type: CompareAction
target: https://www.aversusb.net/compare/clickhouse-vs-pinot)
object: ClickHouse
object: Apache Pinot

SQL Compatibility
type: PropertyValue
value reference: Features

Average Query Latency (1 Billion Row Scan)
type: PropertyValue
value reference: Performance

Monthly Cost per TB Stored
type: PropertyValue
value reference: Cost

Time to Production Deployment
type: PropertyValue
value reference: Setup

Maximum Concurrent Queries
type: PropertyValue
value reference: Scalability

Uptime SLA Guarantee
type: PropertyValue
value reference: Reliability

Native AWS Service Integration
type: PropertyValue
value reference: Integration

Data Ingestion Rate
type: PropertyValue
value reference: Performance

Average Query Latency (1TB dataset)
type: PropertyValue
value reference: Performance

Cost per TB Scanned
type: PropertyValue
value reference: Pricing

Initial Setup Time
type: PropertyValue
value reference: Development Efficiency

Max Concurrent Queries (single cluster)
type: PropertyValue
value reference: Scalability

Built-in ML Capabilities
type: PropertyValue
value reference: Features

Support for Time-Series Data
type: PropertyValue
value reference: Use Case Fit

Enterprise SLA Availability
type: PropertyValue
value reference: Reliability

Peak Ingestion Speed
type: PropertyValue
value reference: Data Ingestion

ANSI SQL Compliance
type: PropertyValue
value reference: SQL Support

Deployment Components
type: PropertyValue
value reference: Operational Complexity

Time to First Query
type: PropertyValue
value reference: Setup Speed

Average Query Latency (Standard Aggregation)
type: PropertyValue
value reference: Performance

Streaming Ingestion Latency
type: PropertyValue
value reference: Real-time Capability

Memory Per 1TB Data
type: PropertyValue
value reference: Resource Requirements

Cluster Node Types Required
type: PropertyValue
value reference: Operational Complexity

Kafka Integration Latency
type: PropertyValue
value reference: Streaming

First Release Year
type: PropertyValue
value reference: Maturity

Setup Time
type: PropertyValue
value reference: Operations

Maximum Dataset Size
type: PropertyValue
value reference: Scalability

Query Latency (1B rows, COUNT aggregation)
type: PropertyValue
value reference: Performance

Data Compression Ratio
type: PropertyValue
value reference: Storage

Operational Complexity
type: PropertyValue
value reference: Operations

Community Size (GitHub Stars)
type: PropertyValue
value reference: Community

License Restrictions
type: PropertyValue
value reference: Licensing

Query Latency (1B rows, 100 dimensions)
type: PropertyValue
value reference: Performance

Memory Footprint per 1GB Data
type: PropertyValue
value reference: Resource Efficiency

Maximum Events/Sec per Node
type: PropertyValue
value reference: Throughput

Typical Cluster Setup Cost
type: PropertyValue
value reference: Economics

Multi-table JOIN Support
type: PropertyValue
value reference: Query Features

Enterprise Deployments
type: PropertyValue
value reference: Market Position

Query Latency (p95 on Real-Time Data)
type: PropertyValue
value reference: Performance

Minimum Cluster Size for 1TB Dataset
type: PropertyValue
value reference: Infrastructure

GitHub Stars (Community Activity)
type: PropertyValue
value reference: Community

Storage Compression Ratio
type: PropertyValue
value reference: Storage

Typical Use Case Flexibility
type: PropertyValue
value reference: Features

JOIN Operation Support
type: PropertyValue
value reference: Features

Query Latency (P95)
type: PropertyValue
value reference: Performance

Per-Query Cost (1TB scan)
type: PropertyValue
value reference: Pricing

Setup Time to Production
type: PropertyValue
value reference: Deployment

Events/Second Ingestion
type: PropertyValue
value reference: Throughput

Annual TCO (100TB dataset)
type: PropertyValue
value reference: Total Cost

Maximum Recommended Cluster Size
type: PropertyValue
value reference: Scalability

Full-Text Search Capability
type: PropertyValue
value reference: Features

Time to First Production Query
type: PropertyValue
value reference: Ease of Use

SQL Support
type: PropertyValue
value reference: Query Language

Typical Memory Per Node
type: PropertyValue
value reference: Resource Efficiency

Setup Time (Minutes)
type: PropertyValue
value reference: Ease of Use

Query Latency on 1GB Dataset
type: PropertyValue
value reference: Performance

Maximum Scalable Dataset Size
type: PropertyValue
value reference: Scalability

Minimum Cluster Nodes Required
type: PropertyValue
value reference: Infrastructure

Real-time Streaming Ingestion
type: PropertyValue
value reference: Features

Concurrent Queries Supported
type: PropertyValue
value reference: Performance

Supported Programming Languages
type: PropertyValue
value reference: Functionality

Annual Infrastructure Cost (1TB dataset)
type: PropertyValue
value reference: Cost

Concurrent User Support
type: PropertyValue
value reference: Collaboration

Query Latency (Average)
type: PropertyValue
value reference: Performance

Data Freshness
type: PropertyValue
value reference: Data Pipeline

License Type
type: PropertyValue
value reference: Licensing

Deployment Flexibility
type: PropertyValue
value reference: Architecture

Ingestion Streaming Support
type: PropertyValue
value reference: Data Pipeline

Base Monthly Cost (Small Cluster)
type: PropertyValue
value reference: Cost

Query Latency (Median)
type: PropertyValue
value reference: Performance

On-Demand Query Pricing
type: PropertyValue
value reference: Cost

Infrastructure Management
type: PropertyValue
value reference: Operations

Maximum Daily Event Throughput
type: PropertyValue
value reference: Scalability

Time to Deploy
type: PropertyValue
value reference: Performance

Concurrent Users Supported
type: PropertyValue
value reference: Scalability

Ingestion Rate (events/second)
type: PropertyValue
value reference: Performance

Query Latency (1B rows)
type: PropertyValue
value reference: Performance

Maximum Recommended Dataset Size
type: PropertyValue
value reference: Performance

Deployment Time
type: PropertyValue
value reference: Performance

Minimum Cluster Size
type: PropertyValue
value reference: Infrastructure

Memory Per Node
type: PropertyValue
value reference: Resource Efficiency

Multi-node Support
type: PropertyValue
value reference: Architecture

Real-time Upsert Support
type: PropertyValue
value reference: Features

Typical Query Latency (1B rows, GROUP BY)
type: PropertyValue
value reference: Performance

Index Size to Data Ratio
type: PropertyValue
value reference: Storage Efficiency

Full-Text Search Optimization
type: PropertyValue
value reference: Search Capability

GitHub Stars (Community Size Proxy)
type: PropertyValue
value reference: Ecosystem

Real-Time Ingestion Support
type: PropertyValue
value reference: Data Ingestion

Typical Deployment Complexity
type: PropertyValue
value reference: Operations

Maximum Practical Dataset Size
type: PropertyValue
value reference: Scalability

SQL Query Support
type: PropertyValue
value reference: Capability

Query Latency (Typical Analytical Query)
type: PropertyValue
value reference: Performance

Data Ingestion Throughput
type: PropertyValue
value reference: Performance

Per-Query Cost (1 TB scanned)
type: PropertyValue
value reference: Pricing

Built-in ML Models
type: PropertyValue
value reference: Features

Maximum Table Size
type: PropertyValue
value reference: Scalability

Deployment Options
type: PropertyValue
value reference: Infrastructure

P99 Query Latency
type: PropertyValue
value reference: Performance

Streaming Ingestion Rate
type: PropertyValue
value reference: Ingestion

Aggregation Query Throughput
type: PropertyValue
value reference: Performance

Operational Components
type: PropertyValue
value reference: Operations

High-Cardinality Dimension Support
type: PropertyValue
value reference: Features

Segment Size (typical)
type: PropertyValue
value reference: Storage

Learning Curve (Complexity)
type: PropertyValue
value reference: Operations