beam
Neo4j
Our great sponsors
beam | Neo4j | |
---|---|---|
30 | 49 | |
7,508 | 12,454 | |
1.5% | 1.5% | |
10.0 | 9.9 | |
5 days ago | 2 days ago | |
Java | Java | |
Apache License 2.0 | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
beam
-
Ask HN: Does (or why does) anyone use MapReduce anymore?
The "streaming systems" book answers your question and more: https://www.oreilly.com/library/view/streaming-systems/97814.... It gives you a history of how batch processing started with MapReduce, and how attempts at scaling by moving towards streaming systems gave us all the subsequent frameworks (Spark, Beam, etc.).
As for the framework called MapReduce, it isn't used much, but its descendant https://beam.apache.org very much is. Nowadays people often use "map reduce" as a shorthand for whatever batch processing system they're building on top of.
-
beam VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
-
How do Streaming Aggregation Pipelines work?
Apache Beam is one of many tools that you can use
-
Releasing Temporian, a Python library for processing temporal data, built together with Google
Flexible runtime ☁️: Temporian programs can run seamlessly in-process in Python, on large datasets using Apache Beam.
-
Kafka cluster loses or duplicates messages
To perform the tests I'm using a Kafka cluster on Kubernetes from the Beam repo (here).
- Apache Beam
-
Real Time Data Infra Stack
Apache Beam: Streaming framework which can be run on several runner such as Apache Flink and GCP Dataflow
-
Google Cloud Reference
Apache Beam: Batch/streaming data processing 🔗Link
-
Composer out of resources - "INFO Task exited with return code Negsignal.SIGKILL"
What you are looking for is Dataflow. It can be a bit tricky to wrap your head around at first, but I highly suggest leaning into this technology for most of your data engineering needs. It's based on the open source Apache Beam framework that originated at Google. We use an internal version of this system at Google for virtually all of our pipeline tasks, from a few GB, to Exabyte scale systems -- it can do it all.
-
Pub/Sub parallel processing best practices
That being said, there is a learning curve in understanding how Apache Beam works. Take a look at the beam website for more information.
Neo4j
-
How to choose the right type of database
Neo4j: An ACID-compliant graph database with a high-performance distributed architecture. Ideal for complex relationship and pattern analysis in domains like social networks.
-
Looks Like the Free Software Foundation Forced Neo4j's Hand
After spending millions fighting the committer of ONgDB who removed the commons clause from the AGPL branded license, it looks like the Free Software Foundation got involved and forced them to remove the commons clause or change the license to their own proprietary license.
https://github.com/neo4j/neo4j/commit/b6237ca4e31706b1efbd0f...
-
Getting Started with GenAI Stack powered with Docker, LangChain, Neo4j and Ollama
The GenAI Stack came about through a collaboration between Docker, Neo4j, LangChain, and Ollama. The goal of the collaboration was to create a pre-built GenAI stack of best-in-class technologies that are well integrated, come with sample applications, and make it easy for developers to get up and running. The goal of the collaboration was to create a pre-built GenAI stack of best-in-class technologies that are well integrated, come with sample applications, and make it easy for developers to get up and running.
-
Database Review: Top Five Missing Features from Database APIs
Neo4j (GraphQL)
-
How to Choose the Right Document-Oriented NoSQL Database for Your Application
NoSQL is a term that we have become very familiar with in recent times and it is used to describe a set of databases that don't make use of SQL when writing & composing queries. There are loads of different types of NoSQL databases ranging from key-value databases like the Reddis to document-oriented databases like MongoDB and Firestore to graph databases like Neo4J to multi-paradigm databases like FaunaDB and Cassandra.
-
Loading data
this thread on this github issue could be useful.
-
[For Hire] Senior Developer with 14 years experience. Canadian expat in a low cost of living country | From 500 EUR per project/month
Recently I have taken an interest in big data. https://neo4j.com/ , https://cassandra.apache.org/ , https://clickhouse.com/, https://www.elastic.co/ - are all databases I have experience with. Neo4j and Cassandra only as a hobby, but Clickhouse I have used in production, and Elasticsearch I have used for some 7 years now.
-
SQL Versus NoSQL Databases: Which to Use, When, and Why
For organizations and their applications that are designed to detect fraud, like International Consortium of Investigative Journalists, or try to improve customer experience via personalization, as in the case of Tourism Media, a NoSQL graph database like Neo4j is a good match. In these kinds of use cases, the quantity of data we're dealing with is enormous, and the pattern we're searching for in the data is often complex.
-
Graph Databases vs Relational Databases: What and why?
First, you need to choose a specific graph database platform to work with, such as Neo4j, OrientDB, JanusGraph, Arangodb or Amazon Neptune. Once you have selected a platform, you can then start working with graph data using the platform's query language.
-
The Basics of Querying with Cypher in PostgreSQL using Apache Age
Welcome to the world of graph databases! When it comes to modelling complex and highly connected data, graph databases have proven to be an efficient and intuitive solution. And one of the most popular graph databases out there is Neo4j, which uses a query language called Cypher.
What are some alternatives?
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
Apache AGE - Graph database optimized for fast analysis and real-time data processing. It is provided as an extension to PostgreSQL. [Moved to: https://github.com/apache/age]
Apache Hadoop - Apache Hadoop
Hasura - Blazing fast, instant realtime GraphQL APIs on your DB with fine grained access control, also trigger webhooks on database events.
Scio - A Scala API for Apache Beam and Google Cloud Dataflow.
FlockDB - A distributed, fault-tolerant graph database
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
RedisGraph - A graph database as a Redis module
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
ArangoDB - 🥑 ArangoDB is a native multi-model database with flexible data models for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.
Apache Hive - Apache Hive
janusgraph - JanusGraph: an open-source, distributed graph database