pinot VS Neo4j

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.

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.

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

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)

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.

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.

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.

There are many mainstream streaming databases, and Apache Pinot is the most popular one recently.

Apache Pinot

Neo4j

Neo4j: An ACID-compliant graph database with a high-performance distributed architecture. Ideal for complex relationship and pattern analysis in domains like social networks.

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...

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.

Neo4j (GraphQL)

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.

this thread on this github issue could be useful.

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.

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.

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.