Memgraph
Druid
Memgraph | Druid | |
---|---|---|
44 | 25 | |
2,096 | 13,197 | |
2.5% | 0.3% | |
9.7 | 9.9 | |
2 days ago | 6 days ago | |
C++ | Java | |
Business Source License (BSL) | Apache License 2.0 |
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.
Memgraph
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Ask HN: Who is hiring? (March 2024)
Memgraph | Staff C++ Database Engineer | REMOTE (Central/Western Europe, LatAm, or North America) https://memgraph.com/
Memgraph is a Seed stage, open source graph database vendor. Graph DBs are a great solution for GenAI, logistics, cybersecurity and fintech so we are looking to grow aggressively this year.
We're looking for a staff-level engineer to set technical direction, mentor junior team members, and solve some very difficult problems.
Either DM me (the hiring manager) or apply here: https://join.com/companies/memgraph/10684850-staff-software-...
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Ask HN: Were Graph Databases a Mirage?
It's not possible to escape tradeoffs. To deal with tradeoffs, focus is important. API to tradeoffs is also important.
I bet somebody will raise a similar question in a few years time when the list under https://db-engines.com/en/ranking/graph+dbms will be bigger.
DISCLAIMER: Coming from https://github.com/memgraph/memgraph
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In-memory vs. disk-based databases: Why do you need a larger than memory architecture?
Albeit the significant engineering endeavor, the larger-than-memory architecture is a super valuable asset to Memgraph users since it allows them to store large amounts of data cheaply on disk without sacrificing the performance of in-memory computation. We are actively working on resolving issues introduced with the new storage mode, so feel free to ask, open an issue, or pull a request. We will be more than happy to help. Until next time š«”
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When to Use a NoSQL Database
NoSQL databases are non-relational databases with flexible schema designed for high performance at a massive scale. Unlike traditional relational databases, which use tables and predefined schemas, NoSQL databases use a variety of data models. There are 4 main types of NoSQL databases - document, graph, key-value, and column-oriented databases. NoSQL databases generally are well-suited for unstructured data, large-scale applications, and agile development processes. The most popular examples of NoSQL databases are MongoDB (document), Memgraph (graph), Redis (key-value store) and Apache HBase (column-oriented).
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Understanding Cosine Similarity in Python with Scikit-Learn
Whether it's about identifying similar user profiles in a social network, detecting similar patterns in a communication network, or classifying nodes in a semantic network, cosine similarity contributes valuable insights. Combined with a powerful graph database system, such as Memgraph, it gives a better understanding of complex networks. Memgraph is an open-source in-memory graph database built to handle real-time use cases at an enterprise scale. Memgraph supports strongly-consistent ACID transactions and uses the standardized Cypher query language for structuring, manipulating, and exploring data.
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History of Open-Source Licenses: What License to Choose?
It should be noted this article is on the blog of a project which advertises itself as open source, under a BSL license that puts limitations on distribution and use.
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Introduction to Benchgraph and its Architecture
At the moment, benchgraph is a project under Memgraph repository (previously Mgbench). It consists of Python scripts and a C++ client. Python scripts are used to manage the benchmark execution by preparing the workload, configurations, and so on, while the C++ client actually executes the benchmark.
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How to Benchmark Memgraph [or Neo4j] with Benchgraph?
These five steps will result in something similar to this simplified version of demo.py example:
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Are indices used as much in Graph databases like Neo4j as in SQL databases?
Take a look at this blog post about choosing the optimal index. It focuses on Memgraph graph database but it offers a theoretical background that is not vendor related.
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How to Identify Essential Proteins Using Betweenness Centrality
In this tutorial, we will utilize betweenness centrality for identifying essential proteins. For this task, we are using Memgraph, a graph analytics platform, which can perform complex graph analysis on all sorts of networks. Even though we will use betweenness centrality, other graph algorithms can also be applied to the protein-protein interaction network, such as other centrality measures or the PageRank algorithm.
Druid
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System Design: Databases and DBMS
Apache Druid
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How to choose the right type of database
Apache Druid: Focused on real-time analytics and interactive queries on large datasets. Druid is well-suited for high-performance applications in user-facing analytics, network monitoring, and business intelligence.
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Choosing Between a Streaming Database and a Stream Processing Framework in Python
Online analytical processing (OLAP) databases like Apache Druid, Apache Pinot, and ClickHouse shine in addressing user-initiated analytical queries. You might write a query to analyze historical data to find the most-clicked products over the past month efficiently using OLAP databases. When contrasting with streaming databases, they may not be optimized for incremental computation, leading to challenges in maintaining the freshness of results. The query in the streaming database focuses on recent data, making it suitable for continuous monitoring. Using streaming databases, you can run queries like finding the top 10 sold products where the ātop 10 product listā might change in real-time.
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Show HN: The simplest tiny analytics tool ā storywise
https://github.com/apache/druid
It's always a question of tradeoffs.
The awesome-selfhosted project has a nice list of open-source analytics projects. It's really good inspiration to dig into these projects and find out about the technology choices that other open-source tools in the space have made.
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Analysing Github Stars - Extracting and analyzing data from Github using Apache NiFiĀ®, Apache KafkaĀ® and Apache DruidĀ®
Spencer Kimball (now CEO at CockroachDB) wrote an interesting article on this topic in 2021 where they created spencerkimball/stargazers based on a Python script. So I started thinking: could I create a data pipeline using Nifi and Kafka (two OSS tools often used with Druid) to get the API data into Druid - and then use SQL to do the analytics? The answer was yes! And I have documented the outcome below. Hereās my analytical pipeline for Github stars data using Nifi, Kafka and Druid.
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Apache DruidĀ® - an enterprise architect's overview
Apache Druid is part of the modern data architecture. It uses a special data format designed for analytical workloads, using extreme parallelisation to get data in and get data out. A shared-nothing, microservices architecture helps you to build highly-available, extreme scale analytics features into your applications.
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Real Time Data Infra Stack
Apache Druid
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When you should use columnar databases and not Postgres, MySQL, or MongoDB
But then you realize there are other databases out there focused specifically on analytical use cases with lots of data and complex queries. Newcomers like ClickHouse, Pinot, and Druid (all open source) respond to a new class of problem: The need to develop applications using endpoints published on analytical queries that were previously confined only to the data warehouse and BI tools.
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Druids by Datadog
Datadog's product is a bit too close to Apache Druid to have named their design system so similarly.
From https://druid.apache.org/ :
> Druid unlocks new types of queries and workflows for clickstream, APM, supply chain, network telemetry, digital marketing, risk/fraud, and many other types of data. Druid is purpose built for rapid, ad-hoc queries on both real-time and historical data.
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Mom at 54 is thinking about coding and a complete career shift. Thoughts?
Maybe rare for someone to be seeking their first coding job at that age. But plenty of us are in our 50s or older and still coding up a storm. And not necessarily ancient tech or anything. My current project exposes analytics data from Apache Druid and Cassandra via Go microservices hosted in K8s.
What are some alternatives?
faust - Python Stream Processing. A Faust fork
iced - A cross-platform GUI library for Rust, inspired by Elm
kuzu - Embeddable property graph database management system built for query speed and scalability. Implements Cypher.
cube.js - š Cube ā The Semantic Layer for Building Data Applications
Apache AGE - Graph database optimized for fast analysis and real-time data processing. It is provided as an extension to PostgreSQL.
Apache Cassandra - Mirror of Apache Cassandra
serverless-graphql - Serverless GraphQL Examples for AWS AppSync and Apollo
Apache HBase - Apache HBase
cugraph - cuGraph - RAPIDS Graph Analytics Library
egui - egui: an easy-to-use immediate mode GUI in Rust that runs on both web and native
demo-news-recommendation - Exploring News Recommendation With Neo4j GDS
Scylla - NoSQL data store using the seastar framework, compatible with Apache Cassandra