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 Apache Pig. While we know about 216 links to Redis, we've tracked only 2 mentions of Apache Pig. 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 / 2 days ago
Slap on some Redis, sprinkle in a few set() calls, and boom—10x faster responses. - Source: dev.to / 2 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 / 15 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 1 month ago
Instead of spinning up Redis, use an unlogged table in PostgreSQL for fast, ephemeral storage. - Source: dev.to / about 2 months ago
Pig, a platform/programming language for authoring parallelizable jobs. - Source: dev.to / over 2 years ago
In the early days of the Big Data era when K8s hasn't even been born yet, the common open source go-to solution was the Hadoop stack. We have written several old-fashioned Map-Reduce jobs, scripts using Pig until we came across Spark. Since then Spark has became one of the most popular data processing engines. It is very easy to start using Lighter on YARN deployments. Just run a docker with proper configuration... - Source: dev.to / over 3 years ago
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.
ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.
Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.
Apache Cassandra - The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.
Presto DB - Distributed SQL Query Engine for Big Data (by Facebook)