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  5. Apache Parquet vs Vertica

Apache Parquet vs Vertica

OverviewComparisonAlternatives

Overview

Vertica
Vertica
Stacks90
Followers120
Votes16
Apache Parquet
Apache Parquet
Stacks97
Followers190
Votes0

Apache Parquet vs Vertica: What are the differences?

# Apache Parquet vs Vertica

Apache Parquet and Vertica are both popular data storage and processing solutions used in big data environments. However, there are key differences between the two platforms that make them suitable for different use cases.

1. **File Format**: Apache Parquet is a columnar storage format that is optimized for analytics workloads, enabling efficient scans and aggregations. On the other hand, Vertica uses a row-based storage format that may be more suitable for transactional workloads or OLTP scenarios.
2. **Performance**: Vertica is known for its high performance and query optimization capabilities, which make it a preferred choice for real-time analytics and complex queries. Apache Parquet, while efficient for analytical workloads, may not provide the same level of performance optimization as Vertica.
3. **Scalability**: Vertica is designed to scale out horizontally by adding more nodes to a cluster, allowing it to handle increasing data volumes and query loads. Apache Parquet, being a file format, does not inherently provide the same scalability features as a distributed database system like Vertica.
4. **Data Processing**: Vertica includes built-in data processing tools and functionalities, such as machine learning algorithms and advanced analytics capabilities. Apache Parquet, being primarily a storage format, requires integration with other processing frameworks or tools for data analysis and processing.
5. **Cost**: Vertica is a commercial product and may require licensing fees for enterprise deployments, while Apache Parquet is an open-source project and can be used freely without any additional costs, making it a cost-effective option for organizations with budget constraints.
6. **Ease of Use**: Vertica provides a user-friendly interface and SQL-like querying language that simplifies data analysis and reporting tasks. Apache Parquet, being a storage format, requires additional tools or frameworks for data manipulation and analysis, which may add complexity to the workflow.

In Summary, Apache Parquet and Vertica offer different advantages and trade-offs in terms of file format, performance, scalability, data processing capabilities, cost, and ease of use, making it essential for organizations to choose the platform that aligns with their specific requirements and goals.```

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Detailed Comparison

Vertica
Vertica
Apache Parquet
Apache Parquet

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

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.

Analyze All of Your Data. No longer move data or settle for siloed views;Achieve Scale and Performance;Fear of growing data volumes and users is a thing of the past;Future-Proof Your Analytics
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
90
Stacks
97
Followers
120
Followers
190
Votes
16
Votes
0
Pros & Cons
Pros
  • 3
    Shared nothing or shared everything architecture
  • 1
    Partition pruning and predicate push down on Parquet
  • 1
    Vertica is the only product which offers partition prun
  • 1
    Query-Optimized Storage
  • 1
    Fully automated Database Designer tool
No community feedback yet
Integrations
Oracle
Oracle
Golang
Golang
MongoDB
MongoDB
MySQL
MySQL
Sass
Sass
Mode
Mode
PowerBI
PowerBI
Tableau
Tableau
Talend
Talend
Hadoop
Hadoop
Java
Java
Apache Impala
Apache Impala
Apache Thrift
Apache Thrift
Apache Hive
Apache Hive
Pig
Pig

What are some alternatives to Vertica, Apache Parquet?

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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