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  5. Apache Parquet vs Oracle PL/SQL

Apache Parquet vs Oracle PL/SQL

OverviewComparisonAlternatives

Overview

Apache Parquet
Apache Parquet
Stacks97
Followers190
Votes0
Oracle PL/SQL
Oracle PL/SQL
Stacks749
Followers598
Votes8

Apache Parquet vs Oracle PL/SQL: What are the differences?

Introduction

In this article, we will discuss the key differences between Apache Parquet and Oracle PL/SQL, two technologies commonly used in the field of data processing and management.

  1. File Format: Apache Parquet is a columnar storage file format designed for big data processing. It uses a highly efficient compression algorithm that enables faster reading and writing of data. On the other hand, Oracle PL/SQL is a procedural language designed for Oracle database management systems. It is used primarily for writing stored procedures and functions within the database.

  2. Data Organization: Parquet organizes data in a columnar format, where each column is stored separately. This allows for efficient data compression and retrieval, especially when dealing with large datasets and complex queries. PL/SQL, on the other hand, organizes data in a row-based format, where each row is stored together. This makes it more suitable for traditional relational database operations.

  3. Data Types: Parquet supports a wide range of data types, including primitive types like integers, floats, and strings, as well as complex types like arrays and maps. PL/SQL, on the other hand, supports a similar range of data types but is primarily focused on relational data types such as VARCHAR2, NUMBER, and DATE.

  4. Processing Model: Parquet is designed to work well with distributed processing frameworks like Apache Spark and Apache Hadoop. It leverages parallel processing and distributed storage to improve query performance and scalability. PL/SQL, on the other hand, is executed within the Oracle database itself and is optimized for efficient data manipulation and retrieval within a single database server.

  5. Language Features: PL/SQL is a full-fledged programming language with features like control structures, exception handling, and modular code organization. It provides a rich set of built-in functions and packages for interacting with the Oracle database. Parquet, on the other hand, is not a programming language but rather a file format and storage optimization technique.

  6. Vendor Lock-in: Oracle PL/SQL is tightly integrated with Oracle database systems and is specific to the Oracle ecosystem. This can result in vendor lock-in, as applications written in PL/SQL may not be easily portable to other database platforms. Parquet, on the other hand, is an open-source file format that can be used with different processing frameworks and databases, reducing vendor lock-in.

In summary, Apache Parquet is a columnar storage file format optimized for big data processing and works well with distributed processing frameworks. Oracle PL/SQL, on the other hand, is a procedural language designed for Oracle database management systems, providing functionality for stored procedures and functions within the database.

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Detailed Comparison

Apache Parquet
Apache Parquet
Oracle PL/SQL
Oracle PL/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.

It is a powerful, yet straightforward database programming language. It is easy to both write and read, and comes packed with lots of out-of-the-box optimizations and security features.

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
Stacks
97
Stacks
749
Followers
190
Followers
598
Votes
0
Votes
8
Pros & Cons
No community feedback yet
Pros
  • 2
    Multiple ways to accomplish the same end
  • 2
    Powerful
  • 1
    Pl/sql
  • 1
    Massive, continuous investment by Oracle Corp
  • 1
    Extensible to external langiages
Cons
  • 2
    High commercial license cost
Integrations
Hadoop
Hadoop
Java
Java
Apache Impala
Apache Impala
Apache Thrift
Apache Thrift
Apache Hive
Apache Hive
Pig
Pig
Python
Python
PHP
PHP
.NET
.NET
Node.js
Node.js
Oracle
Oracle
Hadoop
Hadoop
Java
Java

What are some alternatives to Apache Parquet, Oracle PL/SQL?

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.

GraphQL

GraphQL

GraphQL is a data query language and runtime designed and used at Facebook to request and deliver data to mobile and web apps since 2012.

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.

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