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

Apache Parquet vs Cloudflow

OverviewComparisonAlternatives

Overview

Apache Parquet
Apache Parquet
Stacks97
Followers190
Votes0
Cloudflow
Cloudflow
Stacks5
Followers13
Votes0
GitHub Stars323
Forks89

Apache Parquet vs Cloudflow: What are the differences?

What is Apache Parquet? *A free and open-source column-oriented data storage format *. 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.

What is Cloudflow? *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.

Apache Parquet belongs to "Databases" category of the tech stack, while Cloudflow can be primarily classified under "Big Data Tools".

Some of the features offered by Apache Parquet are:

  • Columnar storage format
  • Type-specific encoding
  • Pig integration

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

Apache Parquet and Cloudflow are both open source tools. Apache Parquet with 1.06K GitHub stars and 928 forks on GitHub appears to be more popular than Cloudflow with 172 GitHub stars and 50 GitHub forks.

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Detailed Comparison

Apache Parquet
Apache Parquet
Cloudflow
Cloudflow

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 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.

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
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
97
Stacks
5
Followers
190
Followers
13
Votes
0
Votes
0
Integrations
Hadoop
Hadoop
Java
Java
Apache Impala
Apache Impala
Apache Thrift
Apache Thrift
Apache Hive
Apache Hive
Pig
Pig
Kubernetes
Kubernetes
Apache Spark
Apache Spark
Akka
Akka
Apache Flink
Apache Flink

What are some alternatives to Apache Parquet, 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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