Hadoop vs Microsoft SQL Server: What are the differences?
Developers describe Hadoop as "Open-source software for reliable, scalable, distributed computing". 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. On the other hand, Microsoft SQL Server is detailed as "A relational database management system developed by Microsoft". Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.
Hadoop and Microsoft SQL Server can be primarily classified as "Databases" tools.
"Great ecosystem" is the top reason why over 34 developers like Hadoop, while over 134 developers mention "Reliable and easy to use" as the leading cause for choosing Microsoft SQL Server.
Hadoop is an open source tool with 9.26K GitHub stars and 5.78K GitHub forks. Here's a link to Hadoop's open source repository on GitHub.
According to the StackShare community, Microsoft SQL Server has a broader approval, being mentioned in 478 company stacks & 443 developers stacks; compared to Hadoop, which is listed in 237 company stacks and 127 developer stacks.
What is Hadoop?
What is Microsoft SQL Server?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to add, upvote and see more prosMake informed product decisions
What are the cons of using Hadoop?
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions
The MapReduce workflow starts to process experiment data nightly when data of the previous day is copied over from Kafka. At this time, all the raw log requests are transformed into meaningful experiment results and in-depth analysis. To populate experiment data for the dashboard, we have around 50 jobs running to do all the calculations and transforms of data.
We've always counted on SQL Server as our database backend. It has served us well over the years. It isn't the cheapest part of our stack, but with the plethora of tools provided by 3rd parties, we have found an incredible and scalable method of keeping our data available and easy to maintain.
in 2009 we open sourced mrjob, which allows any engineer to write a MapReduce job without contending for resources. We’re only limited by the amount of machines in an Amazon data center (which is an issue we’ve rarely encountered).
Defacto, industry standard for backend relational databases. Entity Framework makes designing, migrating & maintaining SQL Server databases a breeze. LocalDB is especially helpful during development.
The massive volume of discovery data that powers Pinterest and enables people to save Pins, create boards and follow other users, is generated through daily Hadoop jobs...
Our core systems that we integrate with are using SQL Server 2012 / 2016 database servers. We use database views on core system databases to help build our domain model.
Main transactional database. SQL Server 2012 Enterprise with AlwaysOn Availability Groups for high availability and disaster recovery.
Importing/Exporting data, interpreting results. Possible integration with SAS
TBD. Good to have I think. Analytics on loads of data, recommendations?