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Learn MorePros of Confluent
Pros of MongoDB
Pros of Confluent
- Free for casual use4
- No hypercloud lock-in3
- Dashboard for kafka insight3
- Easily scalable2
- Zero devops2
Pros of MongoDB
- Document-oriented storage827
- No sql593
- Ease of use553
- Fast464
- High performance410
- Free255
- Open source218
- Flexible180
- Replication & high availability145
- Easy to maintain112
- Querying42
- Easy scalability39
- Auto-sharding38
- High availability37
- Map/reduce31
- Document database27
- Easy setup25
- Full index support25
- Reliable16
- Fast in-place updates15
- Agile programming, flexible, fast14
- No database migrations12
- Easy integration with Node.Js8
- Enterprise8
- Enterprise Support6
- Great NoSQL DB5
- Support for many languages through different drivers4
- Schemaless3
- Aggregation Framework3
- Drivers support is good3
- Fast2
- Managed service2
- Easy to Scale2
- Awesome2
- Consistent2
- Good GUI1
- Acid Compliant1
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Cons of Confluent
Cons of MongoDB
Cons of Confluent
- Proprietary1
Cons of MongoDB
- Very slowly for connected models that require joins6
- Not acid compliant3
- Proprietary query language2
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What is Confluent?
It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream
What is 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.
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What companies use Confluent?
What companies use MongoDB?
What companies use Confluent?
What companies use MongoDB?
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What tools integrate with Confluent?
What tools integrate with MongoDB?
What tools integrate with Confluent?
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What are some alternatives to Confluent and MongoDB?
Databricks
Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
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.