Overview

Microsoft SQL Server

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Followers15.5K

Votes540

Apache Parquet

Stacks97

Followers190

Votes0

Apache Parquet vs Microsoft SQL Server: What are the differences?

Apache Parquet vs Microsoft SQL Server

Apache Parquet and Microsoft SQL Server are both data storage solutions commonly used in the industry. While they serve the same purpose of storing and managing data, there are key differences between them. Here are the main differences:

Storage Format: Apache Parquet is a columnar storage file format, while Microsoft SQL Server uses a relational database management system (RDBMS) to store data. In Parquet, data is grouped by columns, which allows for efficient compression and faster query execution. SQL Server, on the other hand, organizes data in tables with rows and columns, following a relational model.
Compression Techniques: Parquet offers various compression techniques such as Snappy, Gzip, and LZO. These compression techniques significantly reduce storage space and improve query performance. In contrast, SQL Server has its own compression algorithms optimized for relational data, but may not offer the same level of compression as Parquet.
Data Types: Parquet supports a wide range of complex data types, including nested structures and lists, making it suitable for handling complex data. SQL Server, being a relational database, primarily supports basic data types such as integers, strings, and dates. Complex data types in SQL Server are often represented using normalization techniques.
Query Performance: Due to its columnar storage format and advanced compression techniques, Parquet excels in analytical workloads. It can efficiently skip irrelevant data during queries, resulting in faster query performance. SQL Server, being a fully-fledged RDBMS, is optimized for transactional workloads and offers features like indexing and caching to improve query performance.
Scalability: Parquet is designed to be highly scalable and distributed, making it suitable for big data processing frameworks like Apache Hadoop and Apache Spark. It can handle large volumes of data across multiple nodes, allowing for parallel processing. SQL Server, on the other hand, is more suitable for traditional, scale-up scenarios where a single server or a cluster of servers handle the workload.
Cost: Parquet is an open-source file format that can be used free of charge. It can be integrated with various data processing frameworks, making it a cost-efficient solution. SQL Server, on the other hand, is a licensed product with associated costs for licensing, maintenance, and support.

In summary, Apache Parquet offers efficient columnar storage, advanced compression, support for complex data types, and excellent query performance for analytical workloads. It is highly scalable and cost-efficient. Microsoft SQL Server, on the other hand, follows a relational model, offers transactional workloads optimization, and is more suitable for traditional scale-up scenarios.

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Advice on Microsoft SQL Server, Apache Parquet

Erin

IT Specialist

Mar 10, 2020

Needs adviceon

Microsoft SQL Server

MySQL

PostgreSQL

I am a Microsoft SQL Server programmer who is a bit out of practice. I have been asked to assist on a new project. The overall purpose is to organize a large number of recordings so that they can be searched. I have an enormous music library but my songs are several hours long. I need to include things like time, date and location of the recording. I don't have a problem with the general database design. I have two primary questions:

I need to use either @{MySQL}|tool:1025| or @{PostgreSQL}|tool:1028| on a @{Linux}|tool:10483| based OS. Which would be better for this application?
I have not dealt with a sound based data type before. How do I store that and put it in a table? Thank you.

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Detailed Comparison

Microsoft SQL Server	Apache Parquet
Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.	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.
-	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
Statistics
Stacks 21.3K	Stacks 97
Followers 15.5K	Followers 190
Votes 540	Votes 0
Pros & Cons
Pros 139 Reliable and easy to use 101 High performance 95 Great with .net 65 Works well with .net 56 Easy to maintain Cons 4 Expensive Licensing 2 Microsoft 1 Replication can loose the data 1 Allwayon can loose data in asycronious mode 1 The maximum number of connections is only 14000 connect	No community feedback yet
Integrations
No integrations available	Hadoop Java Apache Impala Apache Thrift Apache Hive Pig

What are some alternatives to Microsoft SQL Server, Apache Parquet?

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

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 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.

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

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 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

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 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

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.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.