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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Hadoop vs Microsoft SQL Server vs Oracle

Hadoop vs Microsoft SQL Server vs Oracle

OverviewDecisionsComparisonAlternatives

Overview

Oracle
Oracle
Stacks2.6K
Followers1.8K
Votes113
Microsoft SQL Server
Microsoft SQL Server
Stacks21.3K
Followers15.5K
Votes540
Hadoop
Hadoop
Stacks2.7K
Followers2.3K
Votes56
GitHub Stars15.3K
Forks9.1K

Hadoop vs Microsoft SQL Server vs Oracle: What are the differences?

  1. Scalability: Hadoop is designed to handle massive amounts of data and can easily scale by adding more nodes to the cluster. On the other hand, Microsoft SQL Server and Oracle have limitations in terms of scalability, especially when dealing with petabytes of data.
  2. Cost: Hadoop is an open-source framework, making it more cost-effective compared to proprietary solutions like Microsoft SQL Server and Oracle, which require substantial licensing fees and ongoing maintenance costs.
  3. Data Processing Model: Hadoop uses a distributed processing model, enabling parallel processing of data across multiple nodes in a cluster. In contrast, Microsoft SQL Server and Oracle primarily rely on a centralized processing model, which can be a bottleneck for handling large volumes of data.
  4. Data Type Support: Hadoop is well-suited for processing unstructured and semi-structured data, such as text documents and log files, while Microsoft SQL Server and Oracle excel in handling structured data stored in relational databases.
  5. Ecosystem: Hadoop offers a rich ecosystem of tools and technologies for data processing and analytics, including Apache Spark, Hive, and HBase. In comparison, the ecosystem around Microsoft SQL Server and Oracle is more tightly integrated with their respective platforms, providing a more streamlined but less flexible environment.

In Summary, Hadoop excels in scalability, cost-effectiveness, data processing model, handling unstructured data, and its diverse ecosystem compared to Microsoft SQL Server and Oracle.

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Advice on Oracle, Microsoft SQL Server, Hadoop

Erin
Erin

IT Specialist

Mar 10, 2020

Needs adviceonMicrosoft SQL ServerMicrosoft SQL ServerMySQLMySQLPostgreSQLPostgreSQL

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:

  1. 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?
  2. I have not dealt with a sound based data type before. How do I store that and put it in a table? Thank you.
668k views668k
Comments
Daniel
Daniel

Data Engineer at Dimensigon

Jul 18, 2020

Decided

We have chosen Tibero over Oracle because we want to offer a PL/SQL-as-a-Service that the users can deploy in any Cloud without concerns from our website at some standard cost. With Oracle Database, developers would have to worry about what they implement and the related costs of each feature but the licensing model from Tibero is just 1 price and we have all features included, so we don't have to worry and developers using our SQLaaS neither. PostgreSQL would be open source. We have chosen Tibero over Oracle because we want to offer a PL/SQL that you can deploy in any Cloud without concerns. PostgreSQL would be the open source option but we need to offer an SQLaaS with encryption and more enterprise features in the background and best value option we have found, it was Tibero Database for PL/SQL-based applications.

495k views495k
Comments
Mr
Mr

SVP CTO

Apr 22, 2021

Needs adviceonMarkLogicMarkLogicHadoopHadoopSnowflakeSnowflake

For a property and casualty insurance company, we currently use MarkLogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus Snowflake versus a hadoop or all three of these platforms redundant with one another?

136k views136k
Comments

Detailed Comparison

Oracle
Oracle
Microsoft SQL Server
Microsoft SQL Server
Hadoop
Hadoop

Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

Statistics
GitHub Stars
-
GitHub Stars
-
GitHub Stars
15.3K
GitHub Forks
-
GitHub Forks
-
GitHub Forks
9.1K
Stacks
2.6K
Stacks
21.3K
Stacks
2.7K
Followers
1.8K
Followers
15.5K
Followers
2.3K
Votes
113
Votes
540
Votes
56
Pros & Cons
Pros
  • 44
    Reliable
  • 33
    Enterprise
  • 15
    High Availability
  • 5
    Hard to maintain
  • 5
    Expensive
Cons
  • 14
    Expensive
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
    Allwayon can loose data in asycronious mode
  • 1
    Replication can loose the data
  • 1
    Data pages is only 8k
Pros
  • 39
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Java syntax
  • 1
    Amazon aws

What are some alternatives to Oracle, Microsoft SQL Server, Hadoop?

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.

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.

InfluxDB

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.

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