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  1. Stackups
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  4. Databases
  5. Apache Parquet vs Azure Synapse

Apache Parquet vs Azure Synapse

OverviewComparisonAlternatives

Overview

Apache Parquet
Apache Parquet
Stacks97
Followers190
Votes0
Azure Synapse
Azure Synapse
Stacks104
Followers230
Votes10

Apache Parquet vs Azure Synapse: What are the differences?

Developers describe Apache Parquet as "*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. On the other hand, Azure Synapse is detailed as "Analytics service that brings together enterprise data warehousing and Big Data analytics". It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Apache Parquet belongs to "Databases" category of the tech stack, while Azure Synapse 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, Azure Synapse provides the following key features:

  • Complete T-SQL based analytics – Generally Available
  • Deeply integrated Apache Spark
  • Hybrid data integration

Apache Parquet is an open source tool with 1.14K GitHub stars and 986 GitHub forks. Here's a link to Apache Parquet's open source repository on GitHub.

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Detailed Comparison

Apache Parquet
Apache Parquet
Azure Synapse
Azure Synapse

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 is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

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
Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
Stacks
97
Stacks
104
Followers
190
Followers
230
Votes
0
Votes
10
Pros & Cons
No community feedback yet
Pros
  • 4
    ETL
  • 3
    Security
  • 2
    Serverless
  • 1
    Doesn't support cross database query
Cons
  • 1
    Concurrency
  • 1
    Dictionary Size Limitation - CCI
Integrations
Hadoop
Hadoop
Java
Java
Apache Impala
Apache Impala
Apache Thrift
Apache Thrift
Apache Hive
Apache Hive
Pig
Pig
No integrations available

What are some alternatives to Apache Parquet, Azure Synapse?

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.

Metabase

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

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