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  1. Stackups
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  4. Databases
  5. Apache Derby vs Apache Parquet

Apache Derby vs Apache Parquet

OverviewComparisonAlternatives

Overview

Apache Parquet
Apache Parquet
Stacks97
Followers190
Votes0
Apache Derby
Apache Derby
Stacks103
Followers22
Votes0
GitHub Stars369
Forks141

Apache Derby vs Apache Parquet: What are the differences?

# Apache Derby vs Apache Parquet

Apache Derby and Apache Parquet are both popular technologies in the world of data storage and processing. While Apache Derby is a full-featured, relational database management system, Apache Parquet is a columnar storage format for the Apache Hadoop ecosystem. Here are some key differences between the two:

1. **Data Model**: Apache Derby uses a traditional relational data model, storing data in tables with rows and columns. On the other hand, Apache Parquet utilizes a columnar storage format, which can significantly improve query performance and reduce storage space by storing data by columns rather than by rows.

2. **Storage Efficiency**: Apache Parquet is designed for efficient storage and processing of columnar data, offering improved space utilization and faster query performance compared to Apache Derby. Due to its optimized storage format, Apache Parquet is preferred for big data processing and analytics workloads.

3. **File Format**: While Apache Derby uses a file format specific to its relational database system, Apache Parquet follows a structured, self-describing file format that can be used across multiple Hadoop-based systems. This makes Apache Parquet more interoperable and versatile for handling various big data processing tasks.

4. **Performance Optimization**: Apache Parquet is highly optimized for analytic workloads, enabling faster query processing and IO performance when dealing with large datasets. In contrast, Apache Derby may not be as performant for big data analytics tasks due to its relational database nature.

5. **Use Cases**: Apache Derby is typically used for small-scale relational database applications that require ACID compliance and SQL support. On the other hand, Apache Parquet is more suitable for big data processing, such as data warehousing, analytics, and machine learning, where performance and scalability are crucial.

6. **Integration**: Apache Derby is commonly integrated as an embedded database within Java applications, providing local data storage capabilities. In comparison, Apache Parquet is more integrated with big data frameworks such as Apache Hive, Apache Spark, and Apache Impala for distributed processing and analytics tasks.

In Summary, Apache Derby is a traditional relational database system suited for small-scale applications, while Apache Parquet is a columnar storage format optimized for big data analytics and processing tasks.

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Detailed Comparison

Apache Parquet
Apache Parquet
Apache Derby
Apache Derby

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.

It is an open source relational database implemented entirely in Java and available under the Apache License.

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
Small footprint; Based on the Java, JDBC, and SQL standards; Provides an embedded JDBC driver
Statistics
GitHub Stars
-
GitHub Stars
369
GitHub Forks
-
GitHub Forks
141
Stacks
97
Stacks
103
Followers
190
Followers
22
Votes
0
Votes
0
Integrations
Hadoop
Hadoop
Java
Java
Apache Impala
Apache Impala
Apache Thrift
Apache Thrift
Apache Hive
Apache Hive
Pig
Pig
Java
Java

What are some alternatives to Apache Parquet, Apache Derby?

MongoDB

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

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

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

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

SQLite

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.

Cassandra

Cassandra

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.

Memcached

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

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

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

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.

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