Automated. Scalable. Efficient.

Let experts handle the complexity of integrating infrastructure, while you focus on implementing your product roadmap.

01

Create an Account

02

Create the Sync Relationship

Enter the credentials for SQL Server & Redis to create the sync relationship

03

Start Building

We start synchronizing SQL Server to Redis and provide REST APIs for your Backend

End Users

Your App

redfly
Backend API (Generated)

redfly
Unified DAL (Generated)

redfly
Frameworks & APIs

Database

Search Indexes

Redis Cache

redfly Services

Industry Feedback

We spoke with Distinguished Engineers from all over the world about their experiences working with Databases & Redis. These engineers have worked in reputed companies, major open-source foundations, startups & large enterprises.

High Throughput and Low Latency: Many companies are using caching to improve the performance of their applications by reducing the load on their primary databases. This is particularly important for high-throughput environments where quick response times are critical.

Improving API Response Times: Caching can significantly enhance the performance of APIs by reducing the time required to process and return data.

Balancing Read and Write Operations: Efficiently managing read and write operations to prevent database deadlocks and ensure smooth performance.

Supporting AI & ML Workloads: Redis can be used to manage the data requirements of AI and ML models, providing quick access to the necessary data for training and inference.

Existing Implementations

We can pre-fetch cached data and keeping your cache up to date without the need for cache invalidation/ TTL. This greatly simplifies your code, while keeping your application responsive without hits to the database.

Push Clients

We can easily provide database change notifications very efficiently, to reduce complexity with multiple push clients & operations.

High Transaction Environments

Relational databases are effective for caching read-heavy queries but struggle with caching transactional data where Redis can provide significant benefits.

Legacy Applications

A lot of code change is required to introduce caching. Our platform makes it easier to implement caching by supporting joins and queries with multiple columns on Redis

What Tech leaders say

These are some of the things we learned during our discussions with engineers from all over the world.

I would like to get database change notifications via Redis. Otherwise, there is complexity in dealing with multiple push clients handling different operations at the same time.

CTO
Renewable Energy

A small, insignificant query can cause major problems when there is high concurrency. There is nothing to diagnose or fix when the individual query returns in 10ms. This is where developers fail to cache and end up with problems.

CTO
AI Voice Assistant

Throughput is an issue in supply chain where lots of data comes in. SQL Server does not scale well horizontally to meet the demanding response requirements when clients drop a lot of files

CTO
Supply Chain

This is a serious problem in the transactional space. We use Redis everywhere. It's on the high transaction apps where we start to run into challenges.

CTO
Consulting

DynamoDB can scale to infinite or as deep as your pockets will go. You can throw as much data as you want at it. It will never even stutter. But it's very expensive. Caching can reduce variable Disk IO costs.

CTO
Startup

Programming paradigms have changed with technologies like React, Hotwire and Turbo which require a different way of retrieving data from the server. This means use faster techniques like Redis, because the old way does not work well anymore.

Solution Architect

The industry struggles with legacy applications where a lot of code change is needed to introduce caching. Sometimes it can take a week, sometimes a month. It would be great to have a proxy layer which can cache transparently.

CPO
Mortgage Company

SQL Server is fairly effective in taking a one time hit for a query and caching for subsequent calls. However, it does not work very well for transactional data.

Senior Data Architect
IT Services

We are getting more and more requests for real-time reports. But we are unable to do that with the current system. There is a lag of almost 1 hour. Even now people are struggling because it takes a lot of time to access data via reports.

Data Architect
Non-Profit

A cache is needed to store about a million words (few MB) per call which needs to be held on till the end of the user session. This is important when a user is interacting with an LLM Chatbot over multiple messages in a session.

AI Engineer