Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes with radius queries and streams. Redis has built-in replication, Lua scripting, LRU eviction, transactions and different levels of on-disk persistence, and provides high availability via Redis Sentinel and automatic partitioning with Redis Cluster.
Based on our record, Redis seems to be a lot more popular than Azure Synapse Analytics. While we know about 216 links to Redis, we've tracked only 4 mentions of Azure Synapse Analytics. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Of course, these examples are just toys. A more proper use for asynchronous generators is handling things like reading files, accessing network services, and calling slow running things like AI models. So, I'm going to use an asynchronous generator to access a networked service. That service is Redis and we'll be using Node Redis and Redis Query Engine to find Bigfoot. - Source: dev.to / 12 days ago
Slap on some Redis, sprinkle in a few set() calls, and boom—10x faster responses. - Source: dev.to / 12 days ago
Real-time serving: Many push processed data into low-latency serving layers like Redis to power applications needing instant responses (think fraud detection, live recommendations, financial dashboards). - Source: dev.to / 26 days ago
Redis® Cluster is a fully distributed implementation with automated sharding capabilities (horizontal scaling capabilities), designed for high performance and linear scaling up to 1000 nodes. . - Source: dev.to / about 2 months ago
Instead of spinning up Redis, use an unlogged table in PostgreSQL for fast, ephemeral storage. - Source: dev.to / 2 months ago
Azure Synapse Analytics: DbVisualizer now has extended support for dedicated and serverless SQL pools in Azure Synapse Analytics. That includes support for database-scoped credentials, external file formats and data sources, and external tables. For more information, see the Azure Synapse Dedicated and Azure Synapse Serverless pages on the official site. - Source: dev.to / 8 months ago
A data warehouse is a specialized database that's purpose built for gathering and analyzing data. Unlike general-purpose databases like MySQL or PostgreSQL, which are designed to meet the real-time performance and transactional needs of applications, a data warehouse is designed to collect and process the data produced by those applications, collectively and over time, to help you gain insight from it. Examples of... - Source: dev.to / over 2 years ago
You don't run into these kinds of problems with other tools, like the ones I mentioned. I've never tried the Azure ones, but my gut says they may have some scaling issues (synapse analytics looks promising but I have no experience with it). Source: about 3 years ago
Popular managed cloud data warehouse solutions include Azure Synapse Analytics, Azure SQL Database, and Amazon Redshift. - Source: dev.to / about 3 years ago
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
Saturn Cloud - ML in the cloud. Loved by Data Scientists, Control for IT. Advance your business's ML capabilities through the entire experiment tracking lifecycle. Available on multiple clouds: AWS, Azure, GCP, and OCI.
ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.
Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.
Apache Cassandra - The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.