pinot
ApacheKafka
pinot | ApacheKafka | |
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15 | 104 | |
5,167 | 28 | |
1.4% | - | |
9.9 | 0.0 | |
5 days ago | 6 months ago | |
Java | ||
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.
pinot
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How to choose the right type of database
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.
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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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🦿🛴Smarcity garbage reporting automation w/ ollama
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
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Apache Pinot 1.0
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)
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Ask HN: Who is hiring? (June 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
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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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Building Apache Pinot and Presto
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.
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Reducing Database Loading
There are many mainstream streaming databases, and Apache Pinot is the most popular one recently.
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How-to-Guide: Contributing to Open Source
Apache Pinot
ApacheKafka
- PubNubとIFTTTによるSMS通知システム
- PubNub 및 IFTTT를 사용한 SMS 알림 시스템
- Système de notification par SMS avec PubNub et IFTTT
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Wie man Ereignisse von PubNub zu RabbitMQ streamt
Senden an Kafka (d. h. Senden der Daten an Apache Kafka)
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Choosing Between a Streaming Database and a Stream Processing Framework in Python
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.
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How to Use Reductstore as a Data Sink for Kafka
Apache Kafka is a distributed streaming platform capable of handling high throughput of data, while ReductStore is a databases for unstructured data optimized for storing and querying along time.
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🦿🛴Smarcity garbage reporting automation w/ ollama
*Push data *(original source image, GPS, timestamp) in a common place (Apache Kafka,...)
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How to Build & Deploy Scalable Microservices with NodeJS, TypeScript and Docker || A Comprehesive Guide
RabbitMQ comes with administrative tools to manage user permissions and broker security and is perfect for low latency message delivery and complex routing. In comparison, Apache Kafka architecture provides secure event streams with Transport Layer Security(TLS) and is best suited for big data use cases requiring the best throughput.
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Easy Guide to Integrating Kafka: Practical Solutions for Managing Blob Data
Apache Kafka is a distributed streaming platform to share data between applications and services in real-time.
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Go concurrency simplified. Part 4: Post office as a data pipeline
also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.
What are some alternatives?
hudi - Upserts, Deletes And Incremental Processing on Big Data.
dramatiq - A fast and reliable background task processing library for Python 3.
Trino - Official repository of Trino, the distributed SQL query engine for big data, former
outbox-inbox-patterns - Repository to support the article "Building a Knowledge Base Service With Neo4j, Kafka, and the Outbox Pattern"
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Jenkins - Jenkins automation server
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
RabbitMQ - Open source RabbitMQ: core server and tier 1 (built-in) plugins
kafka-observability - An exploration of observability for Kafka client applications
istio - Connect, secure, control, and observe services.
Apache Pulsar - Apache Pulsar - distributed pub-sub messaging system
Grafana - The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.