software
Druid vs Redshift 2026: Real-Time vs Warehouse
Druid is a real-time OLAP database optimized for sub-second analytics on streaming data, while Redshift is a data warehouse designed for batch processing of large analytical queries. Druid excels at time-series and event data with millisecond latency, whereas Redshift offers superior query performance on petabyte-scale datasets through columnar compression.
AD
Apache Druid
Real-time OLAP database optimized for time-series analytics and streaming ingestion.
Real-time monitoring dashboards, live user analytics, clickstream analysis, sensor/IoT data, fintech tick data
Score63%
VS
AR
Amazon Redshift
AWS-native MPP data warehouse optimized for large-scale batch analytics and predictable workloads.
Enterprise data warehousing, business intelligence, historical data analysis, complex ad-hoc queries, organizations already on AWS
Score63%
100 attributes7 differences16 pros/cons
Quick Answer
AI SummaryDruid is a real-time OLAP database optimized for sub-second analytics on streaming data, while Redshift is a data warehouse designed for batch processing of large analytical queries. Druid excels at time-series and event data with millisecond latency, whereas Redshift offers superior query performance on petabyte-scale datasets through columnar compression.
Our Verdict
AI-assistedChoose Druid if you need real-time dashboards, live event streaming analytics, or sub-second query latency on time-series data—it's built for speed on incoming data. Choose Redshift if you're running a traditional data warehouse with complex analytical queries on historical data, need AWS ecosystem integration, or prefer a fully managed solution with minimal operational overhead.
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A
Apache Druid
8.6/10
Amazon Redshift
6.4/10
A
A
Choose Apache Druid if
Best pickReal-time monitoring dashboards, live user analytics, clickstream analysis, sensor/IoT data, fintech tick data
A
Choose Amazon Redshift if
Enterprise data warehousing, business intelligence, historical data analysis, complex ad-hoc queries, organizations already on AWS
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Key Differences at a Glance
- Query Latency:✓ Apache Druid wins(50-500ms (sub-second) vs 1-10 seconds (batch optimized))
- Primary Use Case:Real-time OLAP and time-series analytics vs Large-scale data warehouse and batch analytics
- Data Ingestion Rate:✓ Apache Druid wins(Up to 1M events/second vs Batch-oriented (slower streaming))
Key Facts & Figures
81 numeric metrics compared
| Metric | Apache Druid | Amazon Redshift | Ratio |
|---|---|---|---|
| Query Latency (1B rows, 100 dimensions)(milliseconds) | 50-150ms | — | — |
| Memory Footprint per 1GB Data(MB) | 600-900MB | — | — |
| Maximum Events/Sec per Node(events/sec) | 100K-500K | — | — |
| Typical Cluster Setup Cost(USD/month (3-node)) | $2500-5000 | — | — |
| Enterprise Deployments(thousands) | 500+ (Airbnb, Netflix, etc) | — | — |
| Query Latency (p95 on Real-Time Data)(milliseconds) | 100-500ms | — | — |
| Minimum Cluster Size for 1TB Dataset(nodes) | 3-5 nodes | — | — |
| GitHub Stars (Community Activity)(count) | 15,800 | — | — |
| Max Ingestion Throughput(events/second) | 500,000 | — | — |
| Query Latency (50th percentile)(milliseconds) | 150 | — | — |
| Data Compression Ratio (metrics)(ratio) | 10:1 | — | — |
| GitHub Stars(stars) | 15,200 | — | — |
| Minimum Cluster Node Count(nodes) | 3 | — | — |
| Third-Party Integrations(integrations) | 300+ | — | — |
| Memory Overhead (1M events)(MB per node) | 120 | — | — |
| Query Latency (p99)(milliseconds) | 500ms | 8000ms | |
| Data Ingestion Rate(GB/sec) | 1,000,000 | 0.5-2 | |
| Maximum Cluster Size(nodes) | Unlimited (distributed) | Petabyte scale | — |
| Typical Query Cost (per TB scanned)(USD) | $0.10-$0.50 | $1.50-$5.00 | |
| Setup Time (to production)(days) | 14-30 | 1-3 | |
| SQL Standard Compliance(% compatibility) | ~60% ANSI SQL | 95% (PostgreSQL-compatible) | |
| Typical Memory Per Node(GB) | 16-64 | 160-256 | |
| P99 Query Latency(milliseconds) | 100-500ms | — | — |
| Median Query Latency(milliseconds) | 10-100ms | — | — |
| Data Ingestion Latency(seconds) | 1-5 seconds (streaming) | Batch (minutes to hours typical) | |
| Maximum Dataset Size Supported(GB) | Petabyte+ (with cluster scaling) | — | — |
| Query Latency (P99 percentile)(milliseconds) | 250ms | — | — |
| Maximum Ingestion Rate(events/second) | 1,000,000+ | — | — |
| Storage Cost(USD per TB per month) | $5 (self-hosted avg) | — | — |
| Concurrent Query Capacity(concurrent users) | 300 | — | — |
| Time to First Query(minutes) | 45 (self-hosted setup) | — | — |
| Minimum Cluster Size(nodes) | 3 (recommended) | — | — |
| Streaming Ingestion Rate(events/second) | 1M+ | — | — |
| Aggregation Query Throughput(queries/second) | 50K-100K | — | — |
| Storage Compression Ratio(ratio) | 10:1 to 20:1 | — | — |
| Operational Components(count) | 12+ (Coordinator, Broker, Historical, MiddleManager, Overlord, Router) | — | — |
| High-Cardinality Dimension Support(unique values) | 1M-10M practical limit | — | — |
| Segment Size (typical)(GB) | 100-500MB segments | — | — |
| Learning Curve (Complexity)(months to production) | 2-3 months | — | — |
| Query Latency (Average)(milliseconds) | 1-10 seconds | 1-10 seconds | |
| Data Freshness(seconds) | Minutes to hours typical | Minutes to hours typical | |
| Concurrent User Support(users) | 2,000+ | 2,000+ | |
| Ingestion Streaming Support(events per second) | Limited via Kinesis/S3 batch | Limited via Kinesis/S3 batch | |
| Base Monthly Cost (Small Cluster)(USD) | $2,160-8,640 (dc2.large, 2-4 nodes) | $2,160-8,640 (dc2.large, 2-4 nodes) | |
| Query Latency (1B row scan, 10 column aggregate)(milliseconds) | 500-2000ms | 500-2000ms | |
| Storage Cost (per TB/month)(USD) | Included in node cost | Included in node cost | |
| Typical Data Compression Ratio(x) | 4-8x | 4-8x | |
| Minimum Cluster Size (nodes)(nodes) | 2 (minimum production) | 2 (minimum production) | |
| Max Concurrent Queries (default config)(queries) | 32 (base, expandable via Concurrency Scaling) | 32 (base, expandable via Concurrency Scaling) | |
| AWS Service Integration (native)(count) | 20+ (native connectors) | 20+ (native connectors) | |
| Base Hourly Cost (2-node cluster)(USD/hour) | $0.50 (DC2.large) | $0.50 (DC2.large) | |
| Query Performance (TPC-DS 100GB)(seconds) | ~16 seconds | ~16 seconds | |
| Scaling Adjustment Time(minutes) | 10-15 (requires cluster resize + restart) | 10-15 (requires cluster resize + restart) | |
| Maximum Single Query Data Scanned(petabytes) | 100+ | 100+ | |
| Cloud Providers Supported(count) | 1 (AWS only) | 1 (AWS only) | |
| Annual Contract Discount(percent) | Up to 30% | Up to 30% | |
| Configuration Tuning Required(hours (estimated)) | 40-80 (distribution keys, sort keys, vacuum) | 40-80 (distribution keys, sort keys, vacuum) | |
| Annual TCO (100TB storage, average usage)(USD) | $200,000 | $200,000 | |
| TPC-DS Query Benchmark (100GB dataset)(seconds) | 42 | 42 | |
| Setup Time to Production(minutes) | 40-60 hours | 40-60 hours | |
| Maximum Concurrent Users(users) | 50 (standard) | 50 (standard) | |
| Data Marketplace Size(datasets) | ~200 (limited) | ~200 (limited) | |
| Reserved Instance Discount(percent) | 70% | 70% | |
| Average Query Latency (1 Billion Row Scan)(ms) | 350ms | 350ms | |
| Monthly Cost per TB Stored(USD) | $2.25 | $2.25 | |
| Maximum Concurrent Queries(queries/sec) | 10,000+ | 10,000+ | |
| Uptime SLA Guarantee(percent) | 99.99% | 99.99% | |
| Native AWS Service Integration(count) | 12+ (S3, Glue, Lambda, Athena, QuickSight, IAM, EventBridge, etc.) | 12+ (S3, Glue, Lambda, Athena, QuickSight, IAM, EventBridge, etc.) | |
| Typical Query Latency (100GB Dataset)(milliseconds) | 500ms-5000ms | 500ms-5000ms | |
| Storage Cost per TB per Month(USD) | $0.25-$0.50 | $0.25-$0.50 | |
| Typical Setup Time for Production(days) | 3-7 days | 3-7 days | |
| Data Ingestion Freshness(seconds) | 300-3600 seconds | 300-3600 seconds | |
| Maximum Concurrent Queries (Standard Node)(queries) | 15-25 | 15-25 | |
| SQL Dialect Compatibility(percentage) | Full PostgreSQL compatible (95%+ ANSI SQL) | Full PostgreSQL compatible (95%+ ANSI SQL) | |
| Time to Production Deployment(days) | 21-28 days | 21-28 days | |
| Estimated Annual Cost (1 PB throughput)(USD) | $180,000-$240,000 | $180,000-$240,000 | |
| Cloud Provider Support(count) | 1 (AWS only) | 1 (AWS only) | |
| Median Ad-hoc Query Response Time(seconds) | 120-240 seconds | 120-240 seconds | |
| Concurrent Users per Instance(users) | 50-100 typical | 50-100 typical | |
| Data Compression Ratio(ratio) | 10:1 average | 10:1 average | |
| Minimum Compute Billing Unit(seconds) | Per-second (reserved/on-demand) | Per-second (reserved/on-demand) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
AD
2Apache DruidAmazon Redshift leads2 ties
AR
3Amazon RedshiftApache Druid
Attribute
Amazon Redshift
- 50-500ms (sub-second)(winner)Query Latency1-10 seconds (batch optimized)
- Real-time OLAP and time-series analyticsPrimary Use CaseLarge-scale data warehouse and batch analytics
- Up to 1M events/second(winner)Data Ingestion RateBatch-oriented (slower streaming)
- $3,000-$15,000 for mid-sizeTypical Cluster Cost (Monthly)$2,000-$20,000+ depending on node type
- Time-based rolling windows (hot/cold data)Data Retention ModelPersistent all-data storage(winner)
- Complex (requires Zookeeper, deep tuning)Ease of SetupSimple (AWS-managed, minimal config)(winner)
- Druid SQL (proprietary variant)SQL Dialect SupportPostgreSQL-compatible SQL(winner)
- Query Latency
Apache Druid
50-500ms (sub-second)(winner)
Amazon Redshift
1-10 seconds (batch optimized)
- Primary Use Case
Apache Druid
Real-time OLAP and time-series analytics
Amazon Redshift
Large-scale data warehouse and batch analytics
- Data Ingestion Rate
Apache Druid
Up to 1M events/second(winner)
Amazon Redshift
Batch-oriented (slower streaming)
- Typical Cluster Cost (Monthly)
Apache Druid
$3,000-$15,000 for mid-size
Amazon Redshift
$2,000-$20,000+ depending on node type
- Data Retention Model
Apache Druid
Time-based rolling windows (hot/cold data)
Amazon Redshift
Persistent all-data storage(winner)
- Ease of Setup
Apache Druid
Complex (requires Zookeeper, deep tuning)
Amazon Redshift
Simple (AWS-managed, minimal config)(winner)
- SQL Dialect Support
Apache Druid
Druid SQL (proprietary variant)
Amazon Redshift
PostgreSQL-compatible SQL(winner)
Full Comparison
| Attribute | Apache Druid | Amazon Redshift |
|---|---|---|
| Query Latency (1B rows, 100 dimensions)(milliseconds) | 50-150ms | — |
| Query Latency (p95 on Real-Time Data)(milliseconds) | 100-500ms | — |
| Max Ingestion Throughput(events/second) | 500,000 | — |
| Query Latency (50th percentile)(milliseconds) | 150 | — |
| Query Latency (p99)(milliseconds) | 500ms(winner) | 8000ms |
Show 16 more attributesData Ingestion Rate(GB/sec) 1,000,000 0.5-2 P99 Query Latency(milliseconds) 100-500ms — Median Query Latency(milliseconds) 10-100ms — Data Ingestion Latency(seconds) 1-5 seconds (streaming) Batch (minutes to hours typical) Maximum Dataset Size Supported(GB) Petabyte+ (with cluster scaling) — Query Latency (P99 percentile)(milliseconds) 250ms — Aggregation Query Throughput(queries/second) 50K-100K — Query Latency (Average)(milliseconds) 1-10 seconds — Query Latency (1B row scan, 10 column aggregate)(milliseconds) 500-2000ms — Query Performance (TPC-DS 100GB)(seconds) ~16 seconds — TPC-DS Query Benchmark (100GB dataset)(seconds) 42 — Average Query Latency (1 Billion Row Scan)(ms) 350ms — Typical Query Latency (100GB Dataset)(milliseconds) 500ms-5000ms — Data Ingestion Freshness(seconds) 300-3600 seconds — Maximum Concurrent Queries (Standard Node)(queries) 15-25 — Median Ad-hoc Query Response Time(seconds) 120-240 seconds — | ||
| Memory Footprint per 1GB Data(MB) | 600-900MB | — |
| Typical Memory Per Node(GB) | 16-64(winner) | 160-256 |
| Maximum Events/Sec per Node(events/sec) | 100K-500K | — |
| Max Concurrent Queries (default config)(queries) | 32 (base, expandable via Concurrency Scaling) | — |
| Typical Cluster Setup Cost(USD/month (3-node)) | $2500-5000 | — |
| Multi-table JOIN Support(capability level) | Limited (requires denormalization) | — |
| SQL Compatibility(percentage) | Custom JSON/Druid QL | — |
| Typical Use Case Flexibility | Real-time metrics (specialized) | — |
| JOIN Operation Support | Limited (basic) | — |
| Full-Text Search Capability | Basic (limited analyzers) | — |
| Third-Party Integrations(integrations) | 300+ | — |
Show 2 more attributesHigh-Cardinality Dimension Support(unique values) 1M-10M practical limit — Native Multi-Cloud Data Sharing(boolean) No (requires replication) — | ||
| Enterprise Deployments(thousands) | 500+ (Airbnb, Netflix, etc) | — |
| Minimum Cluster Size for 1TB Dataset(nodes) | 3-5 nodes | — |
| Deployment Options | Self-hosted, cloud, hybrid, open-source | — |
| Deployment Flexibility | AWS only | — |
| Minimum Cluster Size (nodes)(nodes) | 2 (minimum production) | — |
| Cloud Platform Support | AWS only | — |
Show 1 more attributeCloud Provider Support(count) 1 (AWS only) — | ||
| Native SQL Support | Druid SQL (Full) | — |
| GitHub Stars (Community Activity)(count) | 15,800 | — |
| Data Compression Ratio (metrics)(ratio) | 10:1 | — |
| Segment Size (typical)(GB) | 100-500MB segments | — |
| Columnar Compression Ratio(ratio (data reduction %)) | 6:1 to 15:1 | — |
| GitHub Stars(stars) | 15,200 | — |
| Minimum Cluster Node Count(nodes) | 3 | — |
| Setup Time (to production)(days) | 14-30 | 1-3(winner) |
| Operational Management Overhead(text) | High (cluster tuning, scaling, monitoring) | — |
| Operational Components(count) | 12+ (Coordinator, Broker, Historical, MiddleManager, Overlord, Router) | — |
| Learning Curve (Complexity)(months to production) | 2-3 months | — |
Show 2 more attributesScaling Adjustment Time(minutes) 10-15 (requires cluster resize + restart) — Configuration Tuning Required(hours (estimated)) 40-80 (distribution keys, sort keys, vacuum) — | ||
| Memory Overhead (1M events)(MB per node) | 120 | — |
| Maximum Cluster Size(nodes) | Unlimited (distributed) | Petabyte scale |
| Concurrent Query Capacity(concurrent users) | 300 | — |
| Minimum Cluster Size(nodes) | 3 (recommended) | — |
| Maximum Concurrent Users(users) | 50 (standard) | — |
| Maximum Concurrent Queries(queries/sec) | 10,000+ | — |
Show 2 more attributesHorizontal Scalability(text) Linear scaling with node count limits — Concurrent Users per Instance(users) 50-100 typical — | ||
| Typical Query Cost (per TB scanned)(USD) | $0.10-$0.50(winner) | $1.50-$5.00 |
| Query Cost (On-Demand)(USD per TB scanned) | Included in storage/infrastructure | — |
| Base Monthly Cost (Small Cluster)(USD) | $2,160-8,640 (dc2.large, 2-4 nodes) | — |
| Storage Cost (per TB/month)(USD) | Included in node cost | — |
| Annual TCO (100TB storage, average usage)(USD) | $200,000 | — |
Show 3 more attributesMonthly Cost per TB Stored(USD) $2.25 — Storage Cost per TB per Month(USD) $0.25-$0.50 — Estimated Annual Cost (1 PB throughput)(USD) $180,000-$240,000 — | ||
| Supported Data Retention(duration) | Time-window based (7 days to 3 years typical) | Unlimited historical storage |
| SQL Standard Compliance(% compatibility) | ~60% ANSI SQL | 95% (PostgreSQL-compatible)(winner) |
| SQL Compliance | Proprietary Druid SQL with extensions | — |
| SQL Dialect Compatibility(percentage) | Full PostgreSQL compatible (95%+ ANSI SQL) | — |
| Maximum Ingestion Rate(events/second) | 1,000,000+ | — |
| Storage Cost(USD per TB per month) | $5 (self-hosted avg) | — |
| Base Hourly Cost (2-node cluster)(USD/hour) | $0.50 (DC2.large) | — |
| Annual Contract Discount(percent) | Up to 30% | — |
| Reserved Instance Discount(percent) | 70% | — |
| Time to First Query(minutes) | 45 (self-hosted setup) | — |
| Streaming Ingestion Rate(events/second) | 1M+ | — |
| Storage Compression Ratio(ratio) | 10:1 to 20:1 | — |
| Data Freshness(seconds) | Minutes to hours typical | — |
| Ingestion Streaming Support(events per second) | Limited via Kinesis/S3 batch | — |
| Concurrent User Support(users) | 2,000+ | — |
| License Type | AWS Proprietary Managed Service | — |
| Typical Data Compression Ratio(x) | 4-8x | — |
| Data Compression Ratio(ratio) | 10:1 average | — |
| AWS Service Integration (native)(count) | 20+ (native connectors) | — |
| Data Marketplace Size(datasets) | ~200 (limited) | — |
| GitHub Stars (as of 2026)(thousands) | Not open-source (proprietary) | — |
| Maximum Single Query Data Scanned(petabytes) | 100+ | — |
| Cloud Providers Supported(count) | 1 (AWS only) | — |
| Native AWS Service Integration(count) | 12+ (S3, Glue, Lambda, Athena, QuickSight, IAM, EventBridge, etc.) | — |
| Setup Time to Production(minutes) | 40-60 hours | — |
| Compute-Storage Decoupling | Coupled (scale together) | — |
| Uptime SLA Guarantee(percent) | 99.99% | — |
| Typical Setup Time for Production(days) | 3-7 days | — |
| Time to Production Deployment(days) | 21-28 days | — |
| Minimum Compute Billing Unit(seconds) | Per-second (reserved/on-demand) | — |
AApache Druid
AAmazon Redshift
Query Latency (1B rows, 100 dimensions)(milliseconds)
50-150ms
—
Query Latency (p95 on Real-Time Data)(milliseconds)
100-500ms
—
Max Ingestion Throughput(events/second)
500,000
—
Query Latency (50th percentile)(milliseconds)
150
—
Query Latency (p99)(milliseconds)
500ms
8000ms
Show 16 more attributes
Data Ingestion Rate(GB/sec)
1,000,000
0.5-2
P99 Query Latency(milliseconds)
100-500ms
—
Median Query Latency(milliseconds)
10-100ms
—
Data Ingestion Latency(seconds)
1-5 seconds (streaming)
Batch (minutes to hours typical)
Maximum Dataset Size Supported(GB)
Petabyte+ (with cluster scaling)
—
Query Latency (P99 percentile)(milliseconds)
250ms
—
Aggregation Query Throughput(queries/second)
50K-100K
—
Query Latency (Average)(milliseconds)
1-10 seconds
—
Query Latency (1B row scan, 10 column aggregate)(milliseconds)
500-2000ms
—
Query Performance (TPC-DS 100GB)(seconds)
~16 seconds
—
TPC-DS Query Benchmark (100GB dataset)(seconds)
42
—
Average Query Latency (1 Billion Row Scan)(ms)
350ms
—
Typical Query Latency (100GB Dataset)(milliseconds)
500ms-5000ms
—
Data Ingestion Freshness(seconds)
300-3600 seconds
—
Maximum Concurrent Queries (Standard Node)(queries)
15-25
—
Median Ad-hoc Query Response Time(seconds)
120-240 seconds
—
Memory Footprint per 1GB Data(MB)
600-900MB
—
Typical Memory Per Node(GB)
16-64
160-256
Maximum Events/Sec per Node(events/sec)
100K-500K
—
Max Concurrent Queries (default config)(queries)
32 (base, expandable via Concurrency Scaling)
—
Typical Cluster Setup Cost(USD/month (3-node))
$2500-5000
—
Multi-table JOIN Support(capability level)
Limited (requires denormalization)
—
SQL Compatibility(percentage)
Custom JSON/Druid QL
—
Typical Use Case Flexibility
Real-time metrics (specialized)
—
JOIN Operation Support
Limited (basic)
—
Full-Text Search Capability
Basic (limited analyzers)
—
Third-Party Integrations(integrations)
300+
—
Show 2 more attributes
High-Cardinality Dimension Support(unique values)
1M-10M practical limit
—
Native Multi-Cloud Data Sharing(boolean)
No (requires replication)
—
Enterprise Deployments(thousands)
500+ (Airbnb, Netflix, etc)
—
Minimum Cluster Size for 1TB Dataset(nodes)
3-5 nodes
—
Deployment Options
Self-hosted, cloud, hybrid, open-source
—
Deployment Flexibility
AWS only
—
Minimum Cluster Size (nodes)(nodes)
2 (minimum production)
—
Cloud Platform Support
AWS only
—
Show 1 more attribute
Cloud Provider Support(count)
1 (AWS only)
—
Native SQL Support
Druid SQL (Full)
—
GitHub Stars (Community Activity)(count)
15,800
—
Data Compression Ratio (metrics)(ratio)
10:1
—
Segment Size (typical)(GB)
100-500MB segments
—
Columnar Compression Ratio(ratio (data reduction %))
6:1 to 15:1
—
GitHub Stars(stars)
15,200
—
Minimum Cluster Node Count(nodes)
3
—
Setup Time (to production)(days)
14-30
1-3
Operational Management Overhead(text)
High (cluster tuning, scaling, monitoring)
—
Operational Components(count)
12+ (Coordinator, Broker, Historical, MiddleManager, Overlord, Router)
—
Learning Curve (Complexity)(months to production)
2-3 months
—
Show 2 more attributes
Scaling Adjustment Time(minutes)
10-15 (requires cluster resize + restart)
—
Configuration Tuning Required(hours (estimated))
40-80 (distribution keys, sort keys, vacuum)
—
Memory Overhead (1M events)(MB per node)
120
—
Maximum Cluster Size(nodes)
Unlimited (distributed)
Petabyte scale
Concurrent Query Capacity(concurrent users)
300
—
Minimum Cluster Size(nodes)
3 (recommended)
—
Maximum Concurrent Users(users)
50 (standard)
—
Maximum Concurrent Queries(queries/sec)
10,000+
—
Show 2 more attributes
Horizontal Scalability(text)
Linear scaling with node count limits
—
Concurrent Users per Instance(users)
50-100 typical
—
Typical Query Cost (per TB scanned)(USD)
$0.10-$0.50
$1.50-$5.00
Query Cost (On-Demand)(USD per TB scanned)
Included in storage/infrastructure
—
Base Monthly Cost (Small Cluster)(USD)
$2,160-8,640 (dc2.large, 2-4 nodes)
—
Storage Cost (per TB/month)(USD)
Included in node cost
—
Annual TCO (100TB storage, average usage)(USD)
$200,000
—
Show 3 more attributes
Monthly Cost per TB Stored(USD)
$2.25
—
Storage Cost per TB per Month(USD)
$0.25-$0.50
—
Estimated Annual Cost (1 PB throughput)(USD)
$180,000-$240,000
—
Supported Data Retention(duration)
Time-window based (7 days to 3 years typical)
Unlimited historical storage
SQL Standard Compliance(% compatibility)
~60% ANSI SQL
95% (PostgreSQL-compatible)
SQL Compliance
Proprietary Druid SQL with extensions
—
SQL Dialect Compatibility(percentage)
Full PostgreSQL compatible (95%+ ANSI SQL)
—
Maximum Ingestion Rate(events/second)
1,000,000+
—
Storage Cost(USD per TB per month)
$5 (self-hosted avg)
—
Base Hourly Cost (2-node cluster)(USD/hour)
$0.50 (DC2.large)
—
Annual Contract Discount(percent)
Up to 30%
—
Reserved Instance Discount(percent)
70%
—
Time to First Query(minutes)
45 (self-hosted setup)
—
Streaming Ingestion Rate(events/second)
1M+
—
Storage Compression Ratio(ratio)
10:1 to 20:1
—
Data Freshness(seconds)
Minutes to hours typical
—
Ingestion Streaming Support(events per second)
Limited via Kinesis/S3 batch
—
Concurrent User Support(users)
2,000+
—
License Type
AWS Proprietary Managed Service
—
Typical Data Compression Ratio(x)
4-8x
—
Data Compression Ratio(ratio)
10:1 average
—
AWS Service Integration (native)(count)
20+ (native connectors)
—
Data Marketplace Size(datasets)
~200 (limited)
—
GitHub Stars (as of 2026)(thousands)
Not open-source (proprietary)
—
Maximum Single Query Data Scanned(petabytes)
100+
—
Cloud Providers Supported(count)
1 (AWS only)
—
Native AWS Service Integration(count)
12+ (S3, Glue, Lambda, Athena, QuickSight, IAM, EventBridge, etc.)
—
Setup Time to Production(minutes)
40-60 hours
—
Compute-Storage Decoupling
Coupled (scale together)
—
Uptime SLA Guarantee(percent)
99.99%
—
Typical Setup Time for Production(days)
3-7 days
—
Time to Production Deployment(days)
21-28 days
—
Minimum Compute Billing Unit(seconds)
Per-second (reserved/on-demand)
—
Pros & Cons
10 pros·6 cons across both
AD
AR
AD
Apache Druid
+5-3
Pros
- Sub-second query latency (50-500ms) for interactive dashboards
- Ingests up to 1M events/second with minimal lag
- Built-in rollup and aggregation for data compression
- Time-series and event analytics optimized architecture
- Open-source with active community (Apache license)
Cons
- Requires operational expertise (Zookeeper coordination, tuning, cluster management)
- Higher learning curve with proprietary Druid SQL dialect
- Not ideal for ad-hoc analytical queries on historical data
AR
Amazon Redshift
+5-3
Pros
- Fully managed by AWS (automated backups, scaling, patching)
- Petabyte-scale query performance via columnar compression
- PostgreSQL-compatible SQL for familiar query syntax
- Tight integration with AWS ecosystem (S3, Glue, QuickSight)
- Concurrency scaling for unpredictable workloads
Cons
- 1-10 second query latency (not suitable for real-time dashboards)
- Batch-oriented ingestion (slower for streaming data)
- Vendor lock-in to AWS ecosystem
Frequently Asked Questions
5 questions
Use Druid when you need real-time analytics with sub-second latency on streaming data (clickstreams, user events, sensor data, financial ticks). Redshift is better if you're running traditional data warehouse queries on historical data without real-time requirements. Druid's strength is speed on incoming data; Redshift's strength is analyzing massive historical datasets.
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