Apache Cassandra
Redis
MongoDB
ArangoDB
neo4j
PostgreSQL
OrientDB
MySQL
OpenCV
Pandas
Scikit-learn
NumPy
Dataiku
Exploratory
htm.java
Figure Eight
Apache Cassandra OpenCVOpenCV might be a bit more popular than Apache Cassandra. We know about 52 links to it since March 2021 and only 42 links to Apache Cassandra. 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.
Cassandra is a highly scalable, distributed NoSQL database designed to handle large amounts of data across many commodity servers without a single point of failure. - Source: dev.to / 4 days ago
Distributed storage Distributed storage systems like Cassandra, DynamoDB, and Voldemort also use consistent hashing. In these systems, data is partitioned across many servers. Consistent hashing is used to map data to the servers that store the data. When new servers are added or removed, consistent hashing minimizes the amount of data that needs to be remapped to different servers. - Source: dev.to / about 2 months ago
On the other hand, NoSQL databases are non-relational databases. They store data in flexible, JSON-like documents, key-value pairs, or wide-column stores. Examples include MongoDB, Couchbase, and Cassandra. - Source: dev.to / 3 months ago
HBase and Cassandra: Both cater to non-structured Big Data. Cassandra is geared towards scenarios requiring high availability with eventual consistency, while HBase offers strong consistency and is better suited for read-heavy applications where data consistency is paramount. - Source: dev.to / 4 months ago
Dear r/python, we are happy to present you with our first open-source project. We have managed to implement a new driver for Python that works with Apache Cassandra, ScyllaDB and AWS Keyspaces. Source: 9 months ago
How to Accomplish: Use statistical analysis tools and libraries (e.g., Pandas for tabular data) to calculate and visualize these characteristics. For image datasets, custom scripts to analyze object sizes or mask distributions can be useful. Tools like OpenCV can assist in analyzing image properties, while libraries like Pandas and NumPy are excellent for tabular and numerical analysis. To address class... - Source: dev.to / 16 days ago
Open the camera feed — and use the OpenCV library for real-time computer vision processing. - Source: dev.to / about 1 month ago
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks. - Source: dev.to / 7 months ago
You might be able to achieve this with scripting tools like AutoHotkey or Python with libraries for GUI automation and image recognition (e.g., PyAutoGUI https://pyautogui.readthedocs.io/en/latest/, OpenCV https://opencv.org/). Source: 7 months ago
- [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection. Source: 11 months ago
Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.
NumPy - NumPy is the fundamental package for scientific computing with Python
