Apache Parquet vs Scylla: What are the differences?

Introduction

Apache Parquet is an open-source columnar storage format designed for big data processing frameworks, while Scylla is an open-source distributed NoSQL database. Although both technologies are used in big data processing, they have key differences that set them apart.

1. Storage Format: Apache Parquet is a storage format that organizes data in a columnar format, which makes it more efficient for large-scale analytics workloads. On the other hand, Scylla is a NoSQL database that stores data in a distributed manner using sharding and replication.

2. Data Modeling: Apache Parquet is schema-on-read, meaning it does not enforce a strict schema for the stored data. This flexibility allows for easy evolution of the schema over time. In contrast, Scylla follows a schema-on-write approach, where a predefined schema is enforced for the stored data. This ensures data consistency and integrity.

3. Data Consistency: Apache Parquet does not provide any built-in mechanisms for ensuring data consistency. It primarily focuses on efficient data storage and retrieval. In contrast, Scylla ensures data consistency using techniques like consensus algorithms and distributed transactions. This ensures that data is always in a consistent state across the distributed database.

4. Querying Language: Apache Parquet does not have a built-in querying language. It is typically used in conjunction with query engines like Apache Hive or Apache Impala. These query engines provide SQL-like interfaces to perform queries on Parquet data. On the other hand, Scylla has its own query language called CQL (Cassandra Query Language), which is similar to SQL and allows for powerful data querying and manipulation.

5. Data Scalability: Apache Parquet is designed to handle large-scale datasets by organizing data in a columnar format and utilizing compression techniques. It can efficiently process and analyze massive amounts of data. However, it is not primarily focused on distributed storage and scalability. In contrast, Scylla is specifically designed to handle distributed storage and scalability. It can seamlessly scale horizontally by adding more nodes to the cluster, ensuring high availability and fault tolerance.

6. Data Durability: Apache Parquet is not designed for high data durability as it assumes that the data is stored in a reliable storage system. On the other hand, Scylla ensures high data durability by replicating data across multiple nodes in a distributed manner. Even in the event of node failures, Scylla can maintain data availability and recover the lost data using replication techniques.

In summary, Apache Parquet is a columnar storage format focused on efficient big data processing, while Scylla is a distributed NoSQL database focused on high scalability, data consistency, and durability.