When should I use Apache Cassandra instead of FiloDB?

High-availability, high-write-throughput distributed database across multiple data centers. Time-series, IoT sensor data, and event logs where write volume far outweighs read complexity. Systems requiring no single point of failure with linear horizontal scaling by adding nodes

When should I use FiloDB instead of Apache Cassandra?

Distributed columnar analytics on time-series and event data using Apache Cassandra as storage. Combining fast time-range queries with Cassandra's horizontal scalability. IoT and metrics workloads needing efficient columnar storage on distributed Cassandra infrastructure

What are the main weaknesses of Apache Cassandra?

No native support for ad-hoc queries or joins; data must be modeled around known access patterns. Eventual consistency by default — strong consistency is possible but costs latency. Operational complexity: tuning compaction, repair, and replication requires deep Cassandra expertise

What are the main weaknesses of FiloDB?

Small community with limited documentation and few active production deployments. Inactive development — the project has not seen regular releases in recent years. Requires deep expertise to deploy and tune alongside a Cassandra cluster

Apache Cassandra vs FiloDB: Key Differences for Python Data Engineering

Databases & Data Warehouses

Apache Cassandra

Distributed Wide-Column Store

★ 4.3

Apache-2.0

pip install cassandra-driver

FiloDB

Distributed Columnar Streaming Database

★ 3.7

Apache-2.0

N/A — Scala-based, no pip install

Side-by-Side Comparison

Apache Cassandra

FiloDB

Apache Cassandra

FiloDB

Best For

✓High-availability, high-write-throughput distributed database across multiple data centers
✓Time-series, IoT sensor data, and event logs where write volume far outweighs read complexity
✓Systems requiring no single point of failure with linear horizontal scaling by adding nodes

✓Distributed columnar analytics on time-series and event data using Apache Cassandra as storage
✓Combining fast time-range queries with Cassandra's horizontal scalability
✓IoT and metrics workloads needing efficient columnar storage on distributed Cassandra infrastructure

Best For

✓High-availability, high-write-throughput distributed database across multiple data centers
✓Time-series, IoT sensor data, and event logs where write volume far outweighs read complexity
✓Systems requiring no single point of failure with linear horizontal scaling by adding nodes

✓Distributed columnar analytics on time-series and event data using Apache Cassandra as storage
✓Combining fast time-range queries with Cassandra's horizontal scalability
✓IoT and metrics workloads needing efficient columnar storage on distributed Cassandra infrastructure

Weaknesses

•No native support for ad-hoc queries or joins; data must be modeled around known access patterns
•Eventual consistency by default — strong consistency is possible but costs latency
•Operational complexity: tuning compaction, repair, and replication requires deep Cassandra expertise

•Small community with limited documentation and few active production deployments
•Inactive development — the project has not seen regular releases in recent years
•Requires deep expertise to deploy and tune alongside a Cassandra cluster

Weaknesses

•No native support for ad-hoc queries or joins; data must be modeled around known access patterns
•Eventual consistency by default — strong consistency is possible but costs latency
•Operational complexity: tuning compaction, repair, and replication requires deep Cassandra expertise

•Small community with limited documentation and few active production deployments
•Inactive development — the project has not seen regular releases in recent years
•Requires deep expertise to deploy and tune alongside a Cassandra cluster

License

Apache-2.0

License

Apache-2.0

Install

pip install cassandra-driver

N/A — Scala-based, no pip install

Install

pip install cassandra-driver

N/A — Scala-based, no pip install

Rating

★ 4.3

★ 3.7

Rating

★ 4.3

★ 3.7

Key Features

Apache Cassandra

1Distributed, masterless architecture for linear horizontal scalability
2Tunable consistency: trade consistency for availability at write time
3Wide-column data model optimized for time-series and event storage
4CQL (Cassandra Query Language) provides a SQL-like interface
5Multi-datacenter replication for geo-distributed data access

FiloDB

1Distributed time-series database optimized for Prometheus-style metrics
2Column-oriented storage using Apache Parquet encoding for compression
3Built on Apache Cassandra for distributed storage and replication
4Supports PromQL for querying metrics alongside native Scala/Java API
5Chunk-based storage with configurable retention and downsampling

How Python Data Engineers Use These Tools

Apache Cassandra

Python data engineers use the `cassandra-driver` library to read from and write to Cassandra clusters in high-write-throughput scenarios — IoT event ingestion, clickstream storage, and time-series data. Engineers model data around query patterns rather than normalization, designing partition keys that keep related rows co-located for fast reads.

FiloDB

Python data engineers working in observability infrastructure use FiloDB as a long-term storage backend for Prometheus metrics. Python scripts query FiloDB via its HTTP API to pull historical metric data for anomaly detection models or SLA reporting — enabling ML-driven monitoring use cases that require more data history than Prometheus retains locally.

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Individual Tool Pages

View Apache Cassandra details →View FiloDB details →

Side-by-Side Comparison

Apache Cassandra

FiloDB

Apache Cassandra

FiloDB

Best For

✓High-availability, high-write-throughput distributed database across multiple data centers
✓Time-series, IoT sensor data, and event logs where write volume far outweighs read complexity
✓Systems requiring no single point of failure with linear horizontal scaling by adding nodes

✓Distributed columnar analytics on time-series and event data using Apache Cassandra as storage
✓Combining fast time-range queries with Cassandra's horizontal scalability
✓IoT and metrics workloads needing efficient columnar storage on distributed Cassandra infrastructure

Best For

✓High-availability, high-write-throughput distributed database across multiple data centers
✓Time-series, IoT sensor data, and event logs where write volume far outweighs read complexity
✓Systems requiring no single point of failure with linear horizontal scaling by adding nodes

✓Distributed columnar analytics on time-series and event data using Apache Cassandra as storage
✓Combining fast time-range queries with Cassandra's horizontal scalability
✓IoT and metrics workloads needing efficient columnar storage on distributed Cassandra infrastructure

Weaknesses

•No native support for ad-hoc queries or joins; data must be modeled around known access patterns
•Eventual consistency by default — strong consistency is possible but costs latency
•Operational complexity: tuning compaction, repair, and replication requires deep Cassandra expertise

•Small community with limited documentation and few active production deployments
•Inactive development — the project has not seen regular releases in recent years
•Requires deep expertise to deploy and tune alongside a Cassandra cluster

Weaknesses

•No native support for ad-hoc queries or joins; data must be modeled around known access patterns
•Eventual consistency by default — strong consistency is possible but costs latency
•Operational complexity: tuning compaction, repair, and replication requires deep Cassandra expertise

•Small community with limited documentation and few active production deployments
•Inactive development — the project has not seen regular releases in recent years
•Requires deep expertise to deploy and tune alongside a Cassandra cluster

License

Apache-2.0

License

Apache-2.0

Install

pip install cassandra-driver

N/A — Scala-based, no pip install

Install

pip install cassandra-driver

N/A — Scala-based, no pip install

Rating

★ 4.3

★ 3.7

Rating

★ 4.3

★ 3.7

Key Features

Apache Cassandra

1Distributed, masterless architecture for linear horizontal scalability
2Tunable consistency: trade consistency for availability at write time
3Wide-column data model optimized for time-series and event storage
4CQL (Cassandra Query Language) provides a SQL-like interface
5Multi-datacenter replication for geo-distributed data access

FiloDB

1Distributed time-series database optimized for Prometheus-style metrics
2Column-oriented storage using Apache Parquet encoding for compression
3Built on Apache Cassandra for distributed storage and replication
4Supports PromQL for querying metrics alongside native Scala/Java API
5Chunk-based storage with configurable retention and downsampling

How Python Data Engineers Use These Tools