Big data architecture with Redis
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August 23, 2019

Big data architecture with Redis

Score 9 out of 10
Vetted Review
Verified User
Review Source

Overall Satisfaction with Redis

The R&D department uses Redis as 'in-memory' hot data storage (data storage layer of a machine learning big data architecture).
  • Low latency
  • Support hundreds of connections
  • Significant learning curve
  • Could be costly if not designed right
  • Scalable solution.
  • Cost effective after some optimizations.
Low latency data layer abstraction in a containerized environment.
  • Price
  • Product Features
  • Prior Experience with the Product
  • Existing Relationship with the Vendor
High availability and low latency were crucial features that turned the table for Redis adoption.
Our successful app development process is partially associated with Redis due to the following:
  • Simple and elegant Python module which enables us to produce a scalable solution while maintain our code clean.
  • Getting stored data fast was a big peace of the puzzle for our product backend.
Redshift has relatively high latency and thus produces unscalable solution.
Good for big data storage architectures.
Less suitable if persistence is required.

Redis Feature Ratings

Performance
9
Availability
9
Concurrency
9
Security
8
Scalability
8
Data model flexibility
8
Deployment model flexibility
8