hudi VS pinot

Compare hudi vs pinot and see what are their differences.

pinot

Apache Pinot - A realtime distributed OLAP datastore (by apache)
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hudi pinot
20 15
5,012 5,082
2.0% 1.7%
9.9 9.9
about 14 hours ago 6 days ago
Java Java
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

hudi

Posts with mentions or reviews of hudi. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-18.
  • Getting Started with Flink SQL, Apache Iceberg and DynamoDB Catalog
    4 projects | dev.to | 18 Dec 2023
    Apache Iceberg is one of the three types of lakehouse, the other two are Apache Hudi and Delta Lake.
  • The "Big Three's" Data Storage Offerings
    2 projects | /r/dataengineering | 15 Jun 2023
    Structured, Semi-structured and Unstructured can be stored in one single format, a lakehouse storage format like Delta, Iceberg or Hudi (assuming those don't require low-latency SLAs like subsecond).
  • Data-eng related highlights from the latest Thoughtworks Tech Radar
    3 projects | /r/dataengineering | 26 Apr 2023
    Apache Hudi
  • How-to-Guide: Contributing to Open Source
    19 projects | /r/dataengineering | 11 Jun 2022
    Apache Hudi
  • 4 best opensource projects about big data you should try out
    4 projects | dev.to | 24 Mar 2022
    1.Hudi
  • How Does The Data Lakehouse Enhance The Customer Data Stack?
    3 projects | dev.to | 31 Jan 2022
    A Lakehouse is an architecture that builds on top of the data lake concept and enhances it with functionality commonly found in database systems. The limitations of the data lake led to the emergence of a number of technologies including Apache Iceberg and Apache Hudi. These technologies define a Table Format on top of storage formats like ORC and Parquet on which additional functionality like transactions can be built.
  • SCD type 2 in spark
    2 projects | /r/dataengineering | 15 Oct 2021
    Use Hudi Or Delta Lake
  • Would ParquetWriter from pyarrow automatically flush?
    4 projects | /r/learnpython | 11 Sep 2021
  • Apache Hudi - The Streaming Data Lake Platform
    8 projects | dev.to | 27 Jul 2021
    But first, we needed to tackle the basics - transactions and mutability - on the data lake. In many ways, Apache Hudi pioneered the transactional data lake movement as we know it today. Specifically, during a time when more special-purpose systems were being born, Hudi introduced a server-less, transaction layer, which worked over the general-purpose Hadoop FileSystem abstraction on Cloud Stores/HDFS. This model helped Hudi to scale writers/readers to 1000s of cores on day one, compared to warehouses which offer a richer set of transactional guarantees but are often bottlenecked by the 10s of servers that need to handle them. We also experience a lot of joy to see similar systems (Delta Lake for e.g) later adopt the same server-less transaction layer model that we originally shared way back in early '17. We consciously introduced two table types Copy On Write (with simpler operability) and Merge On Read (for greater flexibility) and now these terms are used in projects outside Hudi, to refer to similar ideas being borrowed from Hudi. Through open sourcing and graduating from the Apache Incubator, we have made some great progress elevating these ideas across the industry, as well as bringing them to life with a cohesive software stack. Given the exciting developments in the past year or so that have propelled data lakes further mainstream, we thought some perspective can help users see Hudi with the right lens, appreciate what it stands for, and be a part of where it’s headed. At this time, we also wanted to shine some light on all the great work done by 180+ contributors on the project, working with more than 2000 unique users over slack/github/jira, contributing all the different capabilities Hudi has gained over the past years, from its humble beginnings.

pinot

Posts with mentions or reviews of pinot. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-02-28.
  • How to choose the right type of database
    15 projects | dev.to | 28 Feb 2024
    Apache Pinot: Tailored for providing ultra-low latency analytics at scale. Apache Pinot is widely used for real-time analytical solutions where rapid data insights and decision-making are critical.
  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    10 projects | dev.to | 10 Feb 2024
    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.
  • 🦿🛴Smarcity garbage reporting automation w/ ollama
    6 projects | dev.to | 31 Jan 2024
    Consume data into third party software (then let Open Search or Apache Spark or Apache Pinot) for analysis/datascience, GIS systems (so you can put reports on a map) or any ticket management system
  • Apache Pinot 1.0
    2 projects | news.ycombinator.com | 19 Sep 2023
    There is indeed Spark support for writing new data into Pinot (https://docs.pinot.apache.org/basics/data-import/batch-inges...) as well as to query it (https://github.com/apache/pinot/blob/master/pinot-connectors...).

    This does not run inside the Pinot cluster - you can use standard Spark execution engine to run this ingestion. In addition, Pinot also supports an out of the box ingestion capability from batch sources using the Minion framework (https://docs.pinot.apache.org/basics/components/cluster/mini...) that does not need any external component (like Spark)

  • Ask HN: Who is hiring? (June 2023)
    14 projects | news.ycombinator.com | 1 Jun 2023
    StarTree | Onsite | Mountain View CA, Bangalore India | Site Lead, SRE, Software Engineers (Backend, Data Infrastructure, Platform), Staff Security Engineer Compliance and Governance

    You can find all the job postings here: https://startree.ai/careers

    My name is Peter Corless and I am the Director of Product Marketing at StarTree (https://startree.ai/). We are a Mountain View, California based company and aer now opening an engineering operation in Bangalore, India.

    We make StarTree Cloud, an Online Analytical Processing (OLAP) database-as-a-service (DBaaS) for real-time, user-facing analytics, powered by Apache Pinot.

    Apache Pinot (https://pinot.apache.org/) is a top-level Apache Software Foundation (ASF) project that came out of LinkedIn. A lot of the PMCs for the Apache Pinot project work at StarTree. It is also used at Uber, Stripe, DoorDash, Just Eat Takeaway (GrubHub), and a lot of other organizations.

    Apache Pinot is known for its ability to provide high concurrency — hundreds of thousands of QPS — against petabytes of data. It uses the star-tree index to provide really fast responses measured in milliseconds.

    We're past 100 employees and looking for people who want to help grow us to the next orders of magnitude.

    Let me know if you have questions or interest.

  • Seeking Feedback on Siddhi
    3 projects | /r/dataengineering | 13 Mar 2023
  • When you should use columnar databases and not Postgres, MySQL, or MongoDB
    5 projects | dev.to | 25 Oct 2022
    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.
  • Building Apache Pinot and Presto
    2 projects | dev.to | 9 Oct 2022
    Recently, we have been surveying some streaming database solutions and the primary target is Apache Pinot, which fits our needs from the description and is therefore the primary target.
  • Reducing Database Loading
    2 projects | dev.to | 11 Sep 2022
    There are many mainstream streaming databases, and Apache Pinot is the most popular one recently.
  • How-to-Guide: Contributing to Open Source
    19 projects | /r/dataengineering | 11 Jun 2022
    Apache Pinot

What are some alternatives?

When comparing hudi and pinot you can also consider the following projects:

iceberg - Apache Iceberg

kudu - Mirror of Apache Kudu

Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)

debezium - Change data capture for a variety of databases. Please log issues at https://issues.redhat.com/browse/DBZ.

delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs

Apache Avro - Apache Avro is a data serialization system.

Apache Orc - Apache ORC - the smallest, fastest columnar storage for Hadoop workloads

Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing

RocksDB - A library that provides an embeddable, persistent key-value store for fast storage.

javalin - A simple and modern Java and Kotlin web framework [Moved to: https://github.com/javalin/javalin]

big-data-pipeline-lambda-arch - A full big data pipeline (Lambda Architecture) with Spark, Kafka, HDFS and Cassandra.