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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Apache Flink vs Microsoft SQL Server

Apache Flink vs Microsoft SQL Server

OverviewDecisionsComparisonAlternatives

Overview

Microsoft SQL Server
Microsoft SQL Server
Stacks21.3K
Followers15.5K
Votes540
Apache Flink
Apache Flink
Stacks534
Followers879
Votes38
GitHub Stars25.4K
Forks13.7K

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

# Apache Flink vs Microsoft SQL Server

Apache Flink and Microsoft SQL Server are two popular tools used for data processing and analysis, but they have key differences that set them apart.

1. **Real-time vs Batch Processing**: One of the main differences between Apache Flink and Microsoft SQL Server is their processing capabilities. Apache Flink is known for its real-time processing capabilities, allowing users to analyze and act on data as it arrives. On the other hand, Microsoft SQL Server is more focused on batch processing, where data is processed in chunks at scheduled intervals.

2. **Scalability**: Apache Flink is designed to be highly scalable, allowing users to process massive amounts of data across distributed systems. It can easily scale up or down based on the workload. Microsoft SQL Server, however, may face limitations in scalability, especially when dealing with large volumes of data.

3. **Programming Language**: Apache Flink is mainly written in Java and Scala, making it more suitable for Java developers. On the other hand, Microsoft SQL Server uses SQL for its queries and procedures, which may be more familiar to users with a background in database management.

4. **Use Cases**: While both Apache Flink and Microsoft SQL Server can be used for data processing and analysis, they are often used in different contexts. Apache Flink is preferred for real-time streaming applications, such as IoT data processing, fraud detection, and monitoring systems. Microsoft SQL Server, on the other hand, is commonly used in traditional data warehousing and business intelligence scenarios.

5. **Open-Source vs Proprietary**: Another key difference between Apache Flink and Microsoft SQL Server is their licensing model. Apache Flink is an open-source project, which means it is free to use and modify. On the contrary, Microsoft SQL Server is a proprietary software that requires a paid license for commercial use.

In Summary, Apache Flink and Microsoft SQL Server differ in their processing capabilities, scalability, programming language, use cases, and licensing model.

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Advice on Microsoft SQL Server, Apache Flink

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
Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k
Comments

Detailed Comparison

Microsoft SQL Server
Microsoft SQL Server
Apache Flink
Apache Flink

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

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

-
Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance
Statistics
GitHub Stars
-
GitHub Stars
25.4K
GitHub Forks
-
GitHub Forks
13.7K
Stacks
21.3K
Stacks
534
Followers
15.5K
Followers
879
Votes
540
Votes
38
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
    Allwayon can loose data in asycronious mode
  • 1
    Data pages is only 8k
  • 1
    The maximum number of connections is only 14000 connect
Pros
  • 16
    Unified batch and stream processing
  • 8
    Out-of-the box connector to kinesis,s3,hdfs
  • 8
    Easy to use streaming apis
  • 4
    Open Source
  • 2
    Low latency
Integrations
No integrations available
YARN Hadoop
YARN Hadoop
Hadoop
Hadoop
HBase
HBase
Kafka
Kafka

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

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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