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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Cloudflow vs Vertica

Cloudflow vs Vertica

OverviewComparisonAlternatives

Overview

Vertica
Vertica
Stacks88
Followers120
Votes16
Cloudflow
Cloudflow
Stacks5
Followers13
Votes0
GitHub Stars323
Forks89

Vertica vs Cloudflow: What are the differences?

Developers describe Vertica as "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. On the other hand, Cloudflow is detailed as "*Streaming Data Pipeline on Kubernetes *". It enables you to quickly develop, orchestrate, and operate distributed streaming applications on Kubernetes. With Cloudflow, streaming applications are comprised of small composable components wired together with schema-based contracts. It can dramatically accelerate streaming application development—​reducing the time required to create, package, and deploy—​from weeks to hours.

Vertica and Cloudflow are primarily classified as "Databases" and "Big Data" tools respectively.

Some of the features offered by Vertica are:

  • 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

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

  • Apache Spark, Apache Flink, and Akka Streams
  • Focus only on business logic, leave the boilerplate to us
  • We provide all the tooling for going from business logic to a deployable Docker image

Cloudflow is an open source tool with 172 GitHub stars and 50 GitHub forks. Here's a link to Cloudflow's open source repository on GitHub.

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Detailed Comparison

Vertica
Vertica
Cloudflow
Cloudflow

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

It enables you to quickly develop, orchestrate, and operate distributed streaming applications on Kubernetes. With Cloudflow, streaming applications are comprised of small composable components wired together with schema-based contracts. It can dramatically accelerate streaming application development—​reducing the time required to create, package, and deploy—​from weeks to hours.

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
Apache Spark, Apache Flink, and Akka Streams; Focus only on business logic, leave the boilerplate to us; We provide all the tooling for going from business logic to a deployable Docker image; We provide Kubernetes tooling to deploy your distributed system with a single command, and manage durable connections between processing stages; With a Lightbend subscription, you get all the tools you need to provide insights, observability, and lifecycle management for evolving your distributed streaming application
Statistics
GitHub Stars
-
GitHub Stars
323
GitHub Forks
-
GitHub Forks
89
Stacks
88
Stacks
5
Followers
120
Followers
13
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
Kubernetes
Kubernetes
Apache Spark
Apache Spark
Akka
Akka
Apache Flink
Apache Flink

What are some alternatives to Vertica, Cloudflow?

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.

Kubernetes

Kubernetes

Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.

Rancher

Rancher

Rancher is an open source container management platform that includes full distributions of Kubernetes, Apache Mesos and Docker Swarm, and makes it simple to operate container clusters on any cloud or infrastructure platform.

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.

Docker Compose

Docker Compose

With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running.

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.

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