pinot VS Apache Pulsar

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

Stream-processing platforms such as Apache Kafka, Apache Pulsar, or Redpanda are specifically engineered to foster event-driven communication in a distributed system and they can be a great choice for developing loosely coupled applications. Stream processing platforms analyze data in motion, offering near-zero latency advantages. For example, consider an alert system for monitoring factory equipment. If a machine's temperature exceeds a certain threshold, a streaming platform can instantly trigger an alert and engineers do timely maintenance.

Edit: Here's a good example of what I'm looking for: https://github.com/apache/pulsar. It is a full application that happens to be deployed (or deployable) with Terraform, and the configuration files are available.

I am the founder of RisingWave (http://risingwave.com/), an open-source SQL streaming database. I am happy to see the launch of Warpstream! I just reviewed the project and here's my personal opinion:

* Apache Kafka is undoubtedly the leading product in the streaming platform space. It offers a simple yet effective API that has become the golden standard. All streaming/messaging vendors need to adhere to Kafka protocol.

* The original Kafka only used local storage to store data, which can be extremely expensive if the data volume is large. That's why many people are advocating for the development of Kafka Tiered Storage (KIP-405: https://cwiki.apache.org/confluence/display/KAFKA/KIP-405%3A...). To my best knowledge, there are at least five vendors selling Kafka or Kafka-compatible products with tiered storage support:

-- Confluent, which builds Kora, the 10X Kafka engine: https://www.confluent.io/10x-apache-kafka/;

-- Aiven, the open-source tiered storage Kafka (source code: https://github.com/Aiven-Open/tiered-storage-for-apache-kafk...

-- Redpanda Data, which cuts your TCO by 6X (https://redpanda.com/platform-tco);

-- DataStax, which commercializes Apache Pulsar (https://pulsar.apache.org/);

-- StreamNative, which commercializes Apache Pulsar (https://pulsar.apache.org/).

* WarpStream claims to be "built directly on top of S3," which I believe is a very aggressive approach that has the potential to drastically reduce costs, even compared to tiered storage. The potential tradeoff is system performance, especially in terms of latency. As new technology, WarpStream brings novelty, and definitely it also needs to convince users that the service is robust and reliable.

* BYOC (Bring Your Own Cloud) is becoming the default option. Most of the vendors listed above offer BYOC, where data is stored in customers' cloud accounts, addressing concerns about data privacy and security.

I believe WarpStream is new technology to this market, and and would encourage the team to publish some detailed numbers to confirm its performance and efficiency!

In recent years, it has become apparent that almost no production system is complete without real-time data. This can also be observed through the rise of streaming platforms such as Apache Kafka, Apache Pulsar, Redpanda, and RabbitMQ.

It's colored black on the refreshed Apache Pulsar site. https://pulsar.apache.org/

So many tools out there, its just which one do you like, I guess. I like Kafka. Works for our environment and we have a few clusters. People have brought up Cribl to replace our kafka (havent really looked into Cribl and we also run NiFi). I have even heard https://pulsar.apache.org/ , which seems to be almost another flavor of Kafka.

RisingWave is an open-source streaming database that has built-in fully-managed CDC source connectors for various databases, also it can collect data from other sources such Kafka, Pulsar, Kinesis, or Redpanda and it allows you to query real-time streams using SQL. You can get a materialized view that is always up-to-date.