Top 7 Graph Databases in Stream Processing

1

neo4j

• Graph DB

#Big Data #Databases #Data Analysis 37 social mentions

Data Analytic System

• Live market data - Crypto, equity indices, FX and commodities — every panel names its source and date
• MiCA / CASP tracker - EU exchange authorisation status based on ESMA registers
• Market Observation Pro - 20 custom price levels, CSV and JSON export (free tier: same data, no delay)
• Learning modules and glossary - 8 free modules, free glossary, Kripto Akademia (50 assets, 12 categories)
• Documentary studies and e-books - RIPPLE study with sourced claims; PDF + audiobook; EN, HU, ES

#Finance #Cryptocurrencies #Data Analysis Featured

2

Azure Cosmos DB

• Global Distribution - Azure Cosmos DB allows for the distribution of data across multiple global regions, enhancing availability and delivering low-latency access to data for users around the world.
• Multi-Model Support - It supports multiple data models including document, graph, key-value, and column-family APIs, making it versatile for a variety of applications and use cases.
• Automatic Scaling - The database automatically scales up and down to meet the demands of application traffic, helping to manage workloads efficiently without manual intervention.
• High Throughput and Low Latency - Cosmos DB offers high performance with single-digit millisecond read and write latencies, ensuring fast access to data for applications.
• Comprehensive SLAs - Azure Cosmos DB provides industry-leading SLAs covering availability, throughput, consistency, and latency, offering strong guarantees for customers.

#Databases #Graph Databases #NoSQL Databases 11 social mentions

3

TigerGraph DB

#Big Data #Databases #Graph Databases

4

Memgraph

• Cypher
• API
• Authentication
• Authorization
• Data Import/Export

#Databases #Kafka #Stream Processing 24 social mentions

5

Virtuoso

#Databases #Graph Databases #Go Lang

6

RocksDB

• High Performance - RocksDB is designed for high throughput and low latency, making it suitable for performance-intensive applications. It optimizes for fast read and write operations, leveraging the LSM-tree data structure.
• Rich Feature Set - Includes advanced features like transactions, column families, data compression, and support for various storage engines, providing flexibility for developers to tailor it to their needs.
• Scale and Efficiency - Handles large volumes of data efficiently, making it suitable for applications that require significant scalability. It does this by using efficient memory and disk utilization techniques.
• Embedded Database - As an embedded database, it allows applications to integrate storage capabilities directly into their processes, reducing the overhead associated with connecting to a separate database service.
• Strong Community and Support - Developed by Facebook, RocksDB has a strong community with extensive documentation, regular updates, and active development, ensuring continuous improvement and support.

#Databases #Graph Databases #NoSQL Databases 14 social mentions

7

DuckDB

• Lightweight - DuckDB is a lightweight database that is easy to install and use without requiring a separate server process.
• In-Memory Processing - It supports efficient in-memory execution, which makes it suitable for analytical queries that require quick data processing.
• Columnar Storage - DuckDB uses a columnar storage format that optimizes for analytical workloads by improving read performance for large datasets.
• Integration with Data Science Tools - The database integrates well with popular data science tools and libraries such as Pandas, R, and Jupyter Notebooks.
• SQL Support - DuckDB offers full support for SQL, allowing users to leverage their existing SQL knowledge without having to learn new query languages.

#Big Data #Databases #Data Integration 48 social mentions

DaytaBrain

• Plan vs. Reality - Ties every initiative to the goals, KPIs and assumptions it was approved on, and shows where results are pulling away from the plan.
• Assumption Alerts - Shows which assumption behind a plan has broken, not just that a number turned red.
• Pre-Review Warnings - Emails each owner before their review to flag out-of-date data, slipping KPIs and work behind schedule.
• Decision Recommendations - When an initiative drifts, explains why and recommends what leaders should decide next. People always make the call.
• Decision Tracking - Tracks whether past decisions moved their KPIs, compares acting now with deferring, and reminds you to check in at 30, 60 and 90 days.

#Program Management #Strategy Management #Productivity Featured