Apache Kudu vs Microsoft SQL Server

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Apache Kudu
Apache Kudu

26
41
+ 1
3
Microsoft SQL Server
Microsoft SQL Server

3.8K
2.7K
+ 1
525
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Kudu vs Microsoft SQL Server: What are the differences?

Kudu: Fast Analytics on Fast Data. A columnar storage manager developed for the Hadoop platform. A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data; Microsoft SQL Server: 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.

Kudu and Microsoft SQL Server are primarily classified as "Big Data" and "Databases" tools respectively.

"Realtime Analytics" is the primary reason why developers consider Kudu over the competitors, whereas "Reliable and easy to use" was stated as the key factor in picking Microsoft SQL Server.

Kudu is an open source tool with 789 GitHub stars and 263 GitHub forks. Here's a link to Kudu's open source repository on GitHub.

- No public GitHub repository available -

What is Apache Kudu?

A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.

What is Microsoft SQL Server?

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.
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Why do developers choose Apache Kudu?
Why do developers choose Microsoft SQL Server?

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What are some alternatives to Apache Kudu and Microsoft SQL Server?
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.
HBase
Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Apache Impala
Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.
Hadoop
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.
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Decisions about Apache Kudu and Microsoft SQL Server
Amazon ElastiCache
Amazon ElastiCache
Amazon Elasticsearch Service
Amazon Elasticsearch Service
AWS Elastic Load Balancing (ELB)
AWS Elastic Load Balancing (ELB)
Memcached
Memcached
Redis
Redis
Python
Python
AWS Lambda
AWS Lambda
Amazon RDS
Amazon RDS
Microsoft SQL Server
Microsoft SQL Server
MariaDB
MariaDB
Amazon RDS for PostgreSQL
Amazon RDS for PostgreSQL
Rails
Rails
Ruby
Ruby
Heroku
Heroku
AWS Elastic Beanstalk
AWS Elastic Beanstalk

We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.

We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.

In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.

Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache

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Reviews of Apache Kudu and Microsoft SQL Server
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How developers use Apache Kudu and Microsoft SQL Server
Avatar of Brillium, Inc.
Brillium, Inc. uses Microsoft SQL ServerMicrosoft SQL Server

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.

Avatar of Andrew Miller
Andrew Miller uses Microsoft SQL ServerMicrosoft SQL Server

Defacto, industry standard for backend relational databases. Entity Framework makes designing, migrating & maintaining SQL Server databases a breeze. LocalDB is especially helpful during development.

Avatar of David Flynn
David Flynn uses Microsoft SQL ServerMicrosoft SQL Server

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.

Avatar of Olo
Olo uses Microsoft SQL ServerMicrosoft SQL Server

Main transactional database. SQL Server 2012 Enterprise with AlwaysOn Availability Groups for high availability and disaster recovery.

Avatar of Arbor Health, LLC
Arbor Health, LLC uses Microsoft SQL ServerMicrosoft SQL Server

Managing script output and input, as well as data cleansing.

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How much does Microsoft SQL Server cost?
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