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

Apache Kylin vs Apache Parquet

OverviewComparisonAlternatives

Overview

Apache Parquet
Apache Parquet
Stacks97
Followers190
Votes0
Apache Kylin
Apache Kylin
Stacks61
Followers236
Votes24
GitHub Stars3.8K
Forks1.5K

Apache Kylin vs Apache Parquet: What are the differences?

Apache Kylin: OLAP Engine for Big Data. Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc; 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.

Apache Kylin and Apache Parquet can be primarily classified as "Big Data" tools.

Some of the features offered by Apache Kylin are:

  • Extremely Fast OLAP Engine at Scale
  • ANSI SQL Interface on Hadoop
  • Interactive Query Capability

On the other hand, Apache Parquet provides the following key features:

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

Apache Kylin and Apache Parquet are both open source tools. It seems that Apache Kylin with 2.32K GitHub stars and 1.03K forks on GitHub has more adoption than Apache Parquet with 918 GitHub stars and 805 GitHub forks.

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Detailed Comparison

Apache Parquet
Apache Parquet
Apache Kylin
Apache Kylin

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.

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

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
Extremely Fast OLAP Engine at Scale; ANSI SQL Interface on Hadoop; Interactive Query Capability; MOLAP Cube; Seamless Integration with BI Tools
Statistics
GitHub Stars
-
GitHub Stars
3.8K
GitHub Forks
-
GitHub Forks
1.5K
Stacks
97
Stacks
61
Followers
190
Followers
236
Votes
0
Votes
24
Pros & Cons
No community feedback yet
Pros
  • 7
    Star schema and snowflake schema support
  • 5
    Seamless BI integration
  • 4
    OLAP on Hadoop
  • 3
    Easy install
  • 3
    Sub-second latency on extreme large dataset
Integrations
Hadoop
Hadoop
Java
Java
Apache Impala
Apache Impala
Apache Thrift
Apache Thrift
Apache Hive
Apache Hive
Pig
Pig
Hadoop
Hadoop
Apache Spark
Apache Spark
Tableau
Tableau
PowerBI
PowerBI
Superset
Superset

What are some alternatives to Apache Parquet, Apache Kylin?

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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