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  1. Stackups
  2. Application & Data
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  4. Databases
  5. Google Cloud Data Fusion vs Vertica

Google Cloud Data Fusion vs Vertica

OverviewComparisonAlternatives

Overview

Vertica
Vertica
Stacks88
Followers120
Votes16
Google Cloud Data Fusion
Google Cloud Data Fusion
Stacks25
Followers156
Votes1

Google Cloud Data Fusion vs Vertica: What are the differences?

Google Cloud Data Fusion: Fully managed, code-free data integration at any scale. A fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. With a graphical interface and a broad open-source library of preconfigured connectors and transformations, and more; Vertica: Storage platform designed to handle large volumes of data. It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Google Cloud Data Fusion and Vertica can be categorized as "Big Data" tools.

Some of the features offered by Google Cloud Data Fusion are:

  • Code-free self-service
  • Collaborative data engineering
  • GCP-native

On the other hand, Vertica provides the following key features:

  • 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

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Detailed Comparison

Vertica
Vertica
Google Cloud Data Fusion
Google Cloud Data Fusion

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

A fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. With a graphical interface and a broad open-source library of preconfigured connectors and transformations, and more.

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
Code-free self-service; Collaborative data engineering; GCP-native; Enterprise-grade security; Integration metadata and lineage; Seamless operations; Comprehensive integration toolkit; Hybrid enablement
Statistics
Stacks
88
Stacks
25
Followers
120
Followers
156
Votes
16
Votes
1
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
Pros
  • 1
    Lower total cost of pipeline ownership
Integrations
Oracle
Oracle
Golang
Golang
MongoDB
MongoDB
MySQL
MySQL
Sass
Sass
Mode
Mode
PowerBI
PowerBI
Tableau
Tableau
Talend
Talend
Google Cloud Storage
Google Cloud Storage
Google BigQuery
Google BigQuery

What are some alternatives to Vertica, Google Cloud Data Fusion?

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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