Trino
Druid
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Trino | Druid | |
---|---|---|
44 | 24 | |
9,519 | 13,180 | |
2.8% | 0.6% | |
10.0 | 9.9 | |
4 days ago | 5 days ago | |
Java | Java | |
Apache License 2.0 | 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.
Trino
- Trino: Fast distributed SQL query engine for big data analytics
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Game analytic power: how we process more than 1 billion events per day
We decided not to waste time reinventing the wheel and simply installed Trino on our servers. It’s a full featured SQL query engine that works on your data. Now our analysts can use it to work with data from AppMetr and execute queries at different levels of complexity.
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Your Thoughts on OLAPs Clickhouse vs Apache Druid vs Starrocks in 2023/2024
DevRel for StarRocks. Trino doesn't have a great caching layer (https://github.com/trinodb/trino/pull/16375) and performance (https://github.com/trinodb/trino/issues/14237) and https://github.com/oap-project/Gluten-Trino. In benchmarks and community user testing, StarRocks has outperformed.
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Making Hard Things Easy
What if my SQL engine is Presto, Trino [1], or a similar query engine? If it's federating multiple source databases we peel the SQL back and get... SQL? Or you peel the SQL back and get... S3 + Mongo + Hadoop? Junior analysts would work at 1/10th the speed if they had to use those raw.
- Trino, a open query engine that runs at ludicrous speed
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Questions about Athena, Trino and Iceberg
The good thing is that the concepts in terms to the SQL supported by Trino transfers between them all. So its completely reasonable to start with one and move to another. In fact that is something that happens regularly. I invite to you check out the talks from the Trino Fest event that is just wrapping up today. There are presentations about all these aspects and different scenarios users encounter. All videos and slides will go live on the Trino website soon. Also feel free to join the Trino slack to chat about about all this with other users.
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Multi-Databases across Multiple Servers - MySQL
There are distributed query engines like Trino that help with this sort of problem https://trino.io/
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Iceberg on Cloudtrail Logs with Athena
This issue in particular is a killer for me: https://github.com/trinodb/trino/issues/10974
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Data Lake, Real-time Analytics, or Both? Exploring Presto and ClickHouse
AFAIK Presto was forked and Trino https://trino.io/ is now the leading SQL Query engine .
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Apache Iceberg as storage for on-premise data store (cluster)
Trino or Hive for SQL querying. Get Trino/Hive to talk to Nessie.
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 “top 10 product list” might change in real-time.
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Show HN: The simplest tiny analytics tool – 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 NiFi®, Apache Kafka® and Apache Druid®
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. Here’s my analytical pipeline for Github stars data using Nifi, Kafka and Druid.
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Apache Druid® - 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.
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Building an arm64 container for Apache Druid for your Apple Silicon
Fortunately, it is super easy to build your own leveraging the binary distribution and existing docker.sh.
What are some alternatives?
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
iced - A cross-platform GUI library for Rust, inspired by Elm
dremio-oss - Dremio - the missing link in modern data
cube.js - 📊 Cube — The Semantic Layer for Building Data Applications
Presto - The official home of the Presto distributed SQL query engine for big data
Apache Cassandra - Mirror of Apache Cassandra
Apache Drill - Apache Drill is a distributed MPP query layer for self describing data
Apache HBase - Apache HBase
Apache Calcite - Apache Calcite
egui - egui: an easy-to-use immediate mode GUI in Rust that runs on both web and native
ClickHouse - ClickHouse® is a free analytics DBMS for big data
Scylla - NoSQL data store using the seastar framework, compatible with Apache Cassandra