Based on our record, Delta Lake should be more popular than CouchDB. It has been mentiond 31 times since March 2021. 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.
Delta is pretty great, let's you do upserts into tables in DataBricks much easier than without it. I think the website is here: https://delta.io. - Source: Hacker News / 5 months ago
Apache Iceberg is one of the three types of lakehouse, the other two are Apache Hudi and Delta Lake. - Source: dev.to / 6 months ago
The Apache Spark / Databricks community prefers Apache parquet or Linux Fundation's delta.io over json. Source: 6 months ago
Databricks provides Jupyter lab like notebooks for analysis and ETL pipelines using spark through pyspark, sparkql or scala. I think R is supported as well but it doesn't interop as well with their newer features as well as python and SQL do. It interfaces with cloud storage backend like S3 and offers some improvements to the parquet format of data querying that allows for updating, ordering and merged through... - Source: Hacker News / 12 months ago
Structured, Semi-structured and Unstructured can be stored in one single format, a lakehouse storage format like Delta, Iceberg or Hudi (assuming those don't require low-latency SLAs like subsecond). Source: about 1 year ago
CouchDB is a json based database for simple projects. The fork pouchdb offers lots of support for offline. Source: about 1 year ago
Apache CouchDB belongs to the family of NoSQL databases. It is a document store with a strong focus on Replication and reliability. One of the most significant differences Between CouchDB and a relational database (besides the absence of tables And schemas) is how you query data. Relational databases allow their Users to execute arbitrary and dynamic queries via SQL. Each SQL query may look Completely... - Source: dev.to / over 1 year ago
For non-SQL-based databases, consider MongoDB, or CouchDB, which are very easy to get started with. Source: almost 2 years ago
You can implement the sync algorithm from scratch, or you can use tools like CouchDB and turtleDB to help you. Source: about 2 years ago
I've heard people recommend CouchDB, no personal expience though. It is also nosql, somewhat similar to mongo. The selling feature is easy scalability. I'm planning to take a weekend to try it out myself. Https://couchdb.apache.org/. Source: about 2 years ago
Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
GeoSpock - GeoSpock is the platform for data lake management, providing a unified view of the data assets within an organization and making it easily accessible.
Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.