Apache Parquet vs Cassandra

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Apache Parquet

Stacks97

Followers190

Votes0

Apache Parquet vs Cassandra: What are the differences?

Key Differences between Apache Parquet and Cassandra

Apache Parquet and Cassandra are both widely used technologies in the data storage and processing domain. While they have some similarities, there are several key differences between them.

Data Storage Model: Apache Parquet is a columnar storage file format that is designed for efficient data processing and analytics. It stores data in a column-wise manner, which allows for fast and efficient querying. On the other hand, Cassandra is a distributed NoSQL database that is designed for high availability and scalability. It uses a partitioned row store model to store data across a cluster of machines.
Data Model: Apache Parquet does not have a built-in data model. It is a schema evolution-supporting format that can work with various data models, including relational and nested data structures. Cassandra, on the other hand, has a flexible and dynamic data model based on a key-value pair model. It allows for dynamic column addition and removal without affecting the existing data.
Data Consistency: Apache Parquet does not provide any built-in mechanisms for data consistency and does not support transactions. It is primarily used for batch processing and analytics workloads. On the other hand, Cassandra provides built-in mechanisms for data consistency and supports distributed transactions. It is designed for high write throughput and can handle real-time workloads.
Data Replication and Distribution: Apache Parquet does not have built-in replication and distribution mechanisms, as it is typically used with other distributed storage systems like Hadoop Distributed File System (HDFS) or cloud storage solutions. Cassandra, on the other hand, has built-in replication and distribution mechanisms, which allow it to provide high availability and fault tolerance.
Querying and Indexing: Apache Parquet is optimized for efficient analytical processing and supports predicate pushdown, which improves query performance by pushing filters down to the storage layer. It also supports dictionary encoding and compression techniques to further improve query performance. Cassandra, on the other hand, supports a query language called Cassandra Query Language (CQL) and provides secondary indexing capabilities. It is designed for real-time querying and provides low-latency read and write operations.
Data Consistency and Availability Guarantees: Apache Parquet does not provide any specific guarantees for data consistency or availability. It relies on the underlying storage system for durability and availability. Cassandra, on the other hand, provides tunable consistency and availability levels. It uses a distributed architecture with replication and automatic data repair mechanisms to ensure high data availability and fault tolerance.

In Summary, Apache Parquet is a columnar storage file format optimized for analytics workloads, while Cassandra is a distributed NoSQL database designed for high availability and scalability with support for real-time querying and transactions.

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Advice on Cassandra, Apache Parquet

Vinay

Head of Engineering

Sep 19, 2019

Needs advice

The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.

The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.

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Comments

Detailed Comparison

Cassandra	Apache Parquet
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.	It is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language.
-	Columnar storage format;Type-specific encoding; Pig integration; Cascading integration; Crunch integration; Apache Arrow integration; Apache Scrooge integration;Adaptive dictionary encoding; Predicate pushdown; Column stats
Statistics
GitHub Stars 9.5K	GitHub Stars -
GitHub Forks 3.8K	GitHub Forks -
Stacks 3.6K	Stacks 97
Followers 3.5K	Followers 190
Votes 507	Votes 0
Pros & Cons
Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Size 1 Updates	No community feedback yet
Integrations
No integrations available	Hadoop Java Apache Impala Apache Thrift Apache Hive Pig

What are some alternatives to Cassandra, Apache Parquet?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

Related Comparisons

Apache Parquet vs Cassandra: What are the differences?

Key Differences between Apache Parquet and Cassandra

Apache Parquet and Cassandra are both widely used technologies in the data storage and processing domain. While they have some similarities, there are several key differences between them.

Data Storage Model: Apache Parquet is a columnar storage file format that is designed for efficient data processing and analytics. It stores data in a column-wise manner, which allows for fast and efficient querying. On the other hand, Cassandra is a distributed NoSQL database that is designed for high availability and scalability. It uses a partitioned row store model to store data across a cluster of machines.
Data Model: Apache Parquet does not have a built-in data model. It is a schema evolution-supporting format that can work with various data models, including relational and nested data structures. Cassandra, on the other hand, has a flexible and dynamic data model based on a key-value pair model. It allows for dynamic column addition and removal without affecting the existing data.
Data Consistency: Apache Parquet does not provide any built-in mechanisms for data consistency and does not support transactions. It is primarily used for batch processing and analytics workloads. On the other hand, Cassandra provides built-in mechanisms for data consistency and supports distributed transactions. It is designed for high write throughput and can handle real-time workloads.
Data Replication and Distribution: Apache Parquet does not have built-in replication and distribution mechanisms, as it is typically used with other distributed storage systems like Hadoop Distributed File System (HDFS) or cloud storage solutions. Cassandra, on the other hand, has built-in replication and distribution mechanisms, which allow it to provide high availability and fault tolerance.
Querying and Indexing: Apache Parquet is optimized for efficient analytical processing and supports predicate pushdown, which improves query performance by pushing filters down to the storage layer. It also supports dictionary encoding and compression techniques to further improve query performance. Cassandra, on the other hand, supports a query language called Cassandra Query Language (CQL) and provides secondary indexing capabilities. It is designed for real-time querying and provides low-latency read and write operations.
Data Consistency and Availability Guarantees: Apache Parquet does not provide any specific guarantees for data consistency or availability. It relies on the underlying storage system for durability and availability. Cassandra, on the other hand, provides tunable consistency and availability levels. It uses a distributed architecture with replication and automatic data repair mechanisms to ensure high data availability and fault tolerance.

Apache Parquet vs Cassandra

Overview

Apache Parquet vs Cassandra: What are the differences?