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Learn MorePros of Amazon Managed Workflows for Apache Airflow
Pros of PostgreSQL
Pros of Amazon Managed Workflows for Apache Airflow
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Pros of PostgreSQL
- Relational database764
- High availability510
- Enterprise class database439
- Sql383
- Sql + nosql304
- Great community173
- Easy to setup147
- Heroku131
- Secure by default130
- Postgis113
- Supports Key-Value50
- Great JSON support48
- Cross platform34
- Extensible33
- Replication28
- Triggers26
- Multiversion concurrency control23
- Rollback23
- Open source21
- Heroku Add-on18
- Stable, Simple and Good Performance17
- Powerful15
- Lets be serious, what other SQL DB would you go for?13
- Good documentation11
- Scalable9
- Reliable8
- Intelligent optimizer8
- Free8
- Transactional DDL7
- Modern7
- One stop solution for all things sql no matter the os6
- Relational database with MVCC5
- Faster Development5
- Full-Text Search4
- Developer friendly4
- Open-source3
- search3
- Great DB for Transactional system or Application3
- Free version3
- Excellent source code3
- Relational datanbase3
- Text2
- Full-text2
- Can handle up to petabytes worth of size1
- Multiple procedural languages supported1
- Composability1
- Native0
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Cons of Amazon Managed Workflows for Apache Airflow
Cons of PostgreSQL
Cons of Amazon Managed Workflows for Apache Airflow
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Cons of PostgreSQL
- Table/index bloatings10
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What is Amazon Managed Workflows for Apache Airflow?
It is a managed orchestration service for Apache Airflow1 that makes it easier to set up and operate end-to-end data pipelines in the cloud at scale. With Managed Workflows, you can use Airflow and Python to create workflows without having to manage the underlying infrastructure for scalability, availability, and security. Managed Workflows automatically scales its workflow execution capacity to meet your needs, and is integrated with AWS security services to help provide you with fast and secure access to data.
What is 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.
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What companies use Amazon Managed Workflows for Apache Airflow?
What companies use PostgreSQL?
What companies use Amazon Managed Workflows for Apache Airflow?
What companies use PostgreSQL?
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What tools integrate with Amazon Managed Workflows for Apache Airflow?
What tools integrate with PostgreSQL?
What tools integrate with Amazon Managed Workflows for Apache Airflow?
What tools integrate with PostgreSQL?
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What are some alternatives to Amazon Managed Workflows for Apache Airflow and PostgreSQL?
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 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.
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.
Redis
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
Amazon S3
Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web