When should I use InfluxDB instead of ScyllaDB?

High-performance time-series storage for metrics, logs, and IoT sensor data with time-based queries. Infrastructure monitoring and real-time analytics dashboards showing time-windowed aggregations. Teams needing a purpose-built time-series database with a built-in query language

When should I use ScyllaDB instead of InfluxDB?

High-performance Cassandra-compatible wide-column store with lower latency via C++ implementation. Teams wanting Cassandra semantics with better throughput and without JVM GC pause latency spikes. Workloads where Cassandra's JVM garbage collection causes unpredictable tail latency

What are the main weaknesses of InfluxDB?

Not suitable for relational or document data outside the time-series paradigm. InfluxDB 3.x changed pricing and architecture significantly, breaking compatibility with 1.x and 2.x. Flux query language (2.x) had a steep learning curve and was deprecated in favor of SQL in 3.x

What are the main weaknesses of ScyllaDB?

Cassandra compatibility is not 100%; subtle behavioral differences can surface during migration. Smaller community and ecosystem than Apache Cassandra for tooling and integrations. Self-managed deployment requires Cassandra operational expertise plus ScyllaDB-specific knowledge

InfluxDB vs ScyllaDB: Key Differences for Python Data Engineering

Databases & Data Warehouses

InfluxDB

Time Series Database

★ 4.4

MIT (1.x) / Commercial (3.x Cloud)

pip install influxdb-client

ScyllaDB

High-Performance Cassandra Alternative

★ 4.5

AGPL-3.0 (Community)

pip install scylla-driver

Side-by-Side Comparison

InfluxDB

ScyllaDB

InfluxDB

ScyllaDB

Best For

✓High-performance time-series storage for metrics, logs, and IoT sensor data with time-based queries
✓Infrastructure monitoring and real-time analytics dashboards showing time-windowed aggregations
✓Teams needing a purpose-built time-series database with a built-in query language

✓High-performance Cassandra-compatible wide-column store with lower latency via C++ implementation
✓Teams wanting Cassandra semantics with better throughput and without JVM GC pause latency spikes
✓Workloads where Cassandra's JVM garbage collection causes unpredictable tail latency

Best For

✓High-performance time-series storage for metrics, logs, and IoT sensor data with time-based queries
✓Infrastructure monitoring and real-time analytics dashboards showing time-windowed aggregations
✓Teams needing a purpose-built time-series database with a built-in query language

✓High-performance Cassandra-compatible wide-column store with lower latency via C++ implementation
✓Teams wanting Cassandra semantics with better throughput and without JVM GC pause latency spikes
✓Workloads where Cassandra's JVM garbage collection causes unpredictable tail latency

Weaknesses

•Not suitable for relational or document data outside the time-series paradigm
•InfluxDB 3.x changed pricing and architecture significantly, breaking compatibility with 1.x and 2.x
•Flux query language (2.x) had a steep learning curve and was deprecated in favor of SQL in 3.x

•Cassandra compatibility is not 100%; subtle behavioral differences can surface during migration
•Smaller community and ecosystem than Apache Cassandra for tooling and integrations
•Self-managed deployment requires Cassandra operational expertise plus ScyllaDB-specific knowledge

Weaknesses

•Not suitable for relational or document data outside the time-series paradigm
•InfluxDB 3.x changed pricing and architecture significantly, breaking compatibility with 1.x and 2.x
•Flux query language (2.x) had a steep learning curve and was deprecated in favor of SQL in 3.x

•Cassandra compatibility is not 100%; subtle behavioral differences can surface during migration
•Smaller community and ecosystem than Apache Cassandra for tooling and integrations
•Self-managed deployment requires Cassandra operational expertise plus ScyllaDB-specific knowledge

License

MIT (1.x) / Commercial (3.x Cloud)

AGPL-3.0 (Community)

License

MIT (1.x) / Commercial (3.x Cloud)

AGPL-3.0 (Community)

Install

pip install influxdb-client

pip install scylla-driver

Install

pip install influxdb-client

pip install scylla-driver

Rating

★ 4.4

★ 4.5

Rating

★ 4.4

★ 4.5

Key Features

InfluxDB

1Purpose-built time-series database optimised for metrics, events, and IoT data
2Flux query language for time-series transformations and aggregations
3Automatic data downsampling and retention policies for long-term storage
4Telegraf agent for collecting system, application, and infrastructure metrics
5Native Grafana integration for real-time metrics dashboards

ScyllaDB

1Cassandra-compatible NoSQL database written in C++ for 10x lower latency
2Shard-per-core architecture eliminates cross-core synchronization overhead
3Drop-in Cassandra replacement — uses the same CQL and drivers
4Compaction strategies optimized for write-heavy workloads
5ScyllaDB Cloud for fully managed deployment on AWS, GCP, and Azure

How Python Data Engineers Use These Tools

InfluxDB

Python data engineers use the influxdb-client library to write time-series metrics from pipeline monitoring, IoT sensors, and application performance data into InfluxDB. It is commonly used alongside Grafana to build real-time pipeline health dashboards showing record throughput, task latency, and error rates. InfluxDB is also used as the storage layer for infrastructure monitoring pipelines built with Telegraf and Kapacitor.

ScyllaDB

Python data engineers use ScyllaDB as a Cassandra replacement when latency is critical — the `cassandra-driver` library works unchanged since ScyllaDB is wire-compatible. Engineers migrating from Cassandra to ScyllaDB typically see P99 write latency drop from milliseconds to microseconds without any Python code changes.

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Individual Tool Pages

View InfluxDB details →View ScyllaDB details →

Side-by-Side Comparison

InfluxDB

ScyllaDB

InfluxDB

ScyllaDB

Best For

✓High-performance time-series storage for metrics, logs, and IoT sensor data with time-based queries
✓Infrastructure monitoring and real-time analytics dashboards showing time-windowed aggregations
✓Teams needing a purpose-built time-series database with a built-in query language

✓High-performance Cassandra-compatible wide-column store with lower latency via C++ implementation
✓Teams wanting Cassandra semantics with better throughput and without JVM GC pause latency spikes
✓Workloads where Cassandra's JVM garbage collection causes unpredictable tail latency

Best For

✓High-performance time-series storage for metrics, logs, and IoT sensor data with time-based queries
✓Infrastructure monitoring and real-time analytics dashboards showing time-windowed aggregations
✓Teams needing a purpose-built time-series database with a built-in query language

✓High-performance Cassandra-compatible wide-column store with lower latency via C++ implementation
✓Teams wanting Cassandra semantics with better throughput and without JVM GC pause latency spikes
✓Workloads where Cassandra's JVM garbage collection causes unpredictable tail latency

Weaknesses

•Not suitable for relational or document data outside the time-series paradigm
•InfluxDB 3.x changed pricing and architecture significantly, breaking compatibility with 1.x and 2.x
•Flux query language (2.x) had a steep learning curve and was deprecated in favor of SQL in 3.x

•Cassandra compatibility is not 100%; subtle behavioral differences can surface during migration
•Smaller community and ecosystem than Apache Cassandra for tooling and integrations
•Self-managed deployment requires Cassandra operational expertise plus ScyllaDB-specific knowledge

Weaknesses

•Not suitable for relational or document data outside the time-series paradigm
•InfluxDB 3.x changed pricing and architecture significantly, breaking compatibility with 1.x and 2.x
•Flux query language (2.x) had a steep learning curve and was deprecated in favor of SQL in 3.x

•Cassandra compatibility is not 100%; subtle behavioral differences can surface during migration
•Smaller community and ecosystem than Apache Cassandra for tooling and integrations
•Self-managed deployment requires Cassandra operational expertise plus ScyllaDB-specific knowledge

License

MIT (1.x) / Commercial (3.x Cloud)

AGPL-3.0 (Community)

License

MIT (1.x) / Commercial (3.x Cloud)

AGPL-3.0 (Community)

Install

pip install influxdb-client

pip install scylla-driver

Install

pip install influxdb-client

pip install scylla-driver

Rating

★ 4.4

★ 4.5

Rating

★ 4.4

★ 4.5

Key Features

InfluxDB

1Purpose-built time-series database optimised for metrics, events, and IoT data
2Flux query language for time-series transformations and aggregations
3Automatic data downsampling and retention policies for long-term storage
4Telegraf agent for collecting system, application, and infrastructure metrics
5Native Grafana integration for real-time metrics dashboards

ScyllaDB

1Cassandra-compatible NoSQL database written in C++ for 10x lower latency
2Shard-per-core architecture eliminates cross-core synchronization overhead
3Drop-in Cassandra replacement — uses the same CQL and drivers
4Compaction strategies optimized for write-heavy workloads
5ScyllaDB Cloud for fully managed deployment on AWS, GCP, and Azure

How Python Data Engineers Use These Tools