Need advice about which tool to choose?Ask the StackShare community!
Airflow vs Microsoft SQL Server: What are the differences?
Introduction
In this article, we will discuss the key differences between Airflow and Microsoft SQL Server.
Scalability: Airflow is designed to handle large-scale data processing tasks and can be easily scaled horizontally by adding more workers. On the other hand, Microsoft SQL Server is primarily a relational database management system (RDBMS) that is not inherently designed for handling big data processing tasks at scale.
Workflow Management: Airflow is a workflow management platform that allows users to define, schedule, and monitor complex data pipelines. It provides a rich set of features for managing dependencies and executing tasks in a distributed manner. Microsoft SQL Server, on the other hand, is not specifically built for workflow management but rather focuses on data storage and retrieval.
Data Processing: Airflow supports a wide range of data processing frameworks and tools such as Apache Spark, Hadoop, and SQL databases. It allows users to easily integrate these tools into their data pipelines and execute complex data transformations. On the other hand, Microsoft SQL Server provides its own set of data processing capabilities through SQL queries and stored procedures.
Real-Time Processing: Airflow provides support for real-time data processing through integrations with streaming frameworks like Apache Kafka and Apache Flink. It allows users to build real-time data pipelines and process streaming data in parallel. Microsoft SQL Server, on the other hand, is not specifically built for real-time data processing and may not be suitable for handling high-velocity streaming data.
Deployment and Management: Airflow can be deployed on various platforms such as on-premises servers, cloud-based infrastructure, and containerized environments. It provides tools for managing deployments, monitoring performance, and scaling the system as needed. Microsoft SQL Server, on the other hand, is typically deployed on dedicated servers or virtual machines and may require additional infrastructure setup for high availability and scalability.
Open-Source vs Proprietary: Airflow is an open-source project maintained by the Apache Software Foundation and has a large community of contributors and users. It benefits from continuous development and improvement through community collaboration. On the other hand, Microsoft SQL Server is a proprietary product developed by Microsoft, which means it comes with licensing costs and limited flexibility in terms of customization and feature development.
In summary, Airflow and Microsoft SQL Server differ in terms of scalability, workflow management capabilities, support for data processing frameworks, real-time processing capabilities, deployment options, and licensing models. Each tool has its own strengths and is better suited for specific use cases and requirements.
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 or PostgreSQL on a Linux 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.
Hi Erin,
Honestly both databases will do the job just fine. I personally prefer Postgres.
Much more important is how you store the audio. While you could technically use a blob type column, it's really not ideal to be storing audio files which are "several hours long" in a database row. Instead consider storing the audio files in an object store (hosted options include backblaze b2 or aws s3) and persisting the key (which references that object) in your database column.
Hi Erin, Chances are you would want to store the files in a blob type. Both MySQL and Postgres support this. Can you explain a little more about your need to store the files in the database? I may be more effective to store the files on a file system or something like S3. To answer your qustion based on what you are descibing I would slighly lean towards PostgreSQL since it tends to be a little better on the data warehousing side.
Hey Erin! I would recommend checking out Directus before you start work on building your own app for them. I just stumbled upon it, and so far extremely happy with the functionalities. If your client is just looking for a simple web app for their own data, then Directus may be a great option. It offers "database mirroring", so that you can connect it to any database and set up functionality around it!
Hi Erin! First of all, you'd probably want to go with a managed service. Don't spin up your own MySQL installation on your own Linux box. If you are on AWS, thet have different offerings for database services. Standard RDS vs. Aurora. Aurora would be my preferred choice given the benefits it offers, storage optimizations it comes with... etc. Such managed services easily allow you to apply new security patches and upgrades, set up backups, replication... etc. Doing this on your own would either be risky, inefficient, or you might just give up. As far as which database to chose, you'll have the choice between Postgresql, MySQL, Maria DB, SQL Server... etc. I personally would recommend MySQL (latest version available), as the official tooling for it (MySQL Workbench) is great, stable, and moreover free. Other database services exist, I'd recommend you also explore Dynamo DB.
Regardless, you'd certainly only keep high-level records, meta data in Database, and the actual files, most-likely in S3, so that you can keep all options open in terms of what you'll do with them.
Hi Erin,
- Coming from "Big" DB engines, such as Oracle or MSSQL, go for PostgreSQL. You'll get all the features you need with PostgreSQL.
- Your case seems to point to a "NoSQL" or Document Database use case. Since you get covered on this with PostgreSQL which achieves excellent performances on JSON based objects, this is a second reason to choose PostgreSQL. MongoDB might be an excellent option as well if you need "sharding" and excellent map-reduce mechanisms for very massive data sets. You really should investigate the NoSQL option for your use case.
- Starting with AWS Aurora is an excellent advise. since "vendor lock-in" is limited, but I did not check for JSON based object / NoSQL features.
- If you stick to Linux server, the PostgreSQL or MySQL provided with your distribution are straightforward to install (i.e. apt install postgresql). For PostgreSQL, make sure you're comfortable with the pg_hba.conf, especially for IP restrictions & accesses.
Regards,
I recommend Postgres as well. Superior performance overall and a more robust architecture.
I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.
For a non-streaming approach:
You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.
Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation
Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing
Pros of Airflow
- Features53
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of Microsoft SQL Server
- Reliable and easy to use139
- High performance101
- Great with .net95
- Works well with .net65
- Easy to maintain56
- Azure support21
- Always on17
- Full Index Support17
- Enterprise manager is fantastic10
- In-Memory OLTP Engine9
- Easy to setup and configure2
- Security is forefront2
- Great documentation1
- Faster Than Oracle1
- Columnstore indexes1
- Decent management tools1
- Docker Delivery1
- Max numar of connection is 140001
Sign up to add or upvote prosMake informed product decisions
Cons of Airflow
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1
Cons of Microsoft SQL Server
- Expensive Licensing4
- Microsoft2
- Data pages is only 8k1
- Allwayon can loose data in asycronious mode1
- Replication can loose the data1
- The maximum number of connections is only 14000 connect1
Sign up to add or upvote consMake informed product decisions
What is Airflow?
What is Microsoft SQL Server?
Need advice about which tool to choose?Ask the StackShare community!
What companies use Microsoft SQL Server?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with Airflow?
What tools integrate with Microsoft SQL Server?
Sign up to get full access to all the tool integrationsMake informed product decisions
Blog Posts
+17