<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Send Data to Qdrant on Qdrant - Vector Search Engine</title><link>https://deploy-preview-2342--condescending-goldwasser-91acf0.netlify.app/documentation/send-data/</link><description>Recent content in Send Data to Qdrant on Qdrant - Vector Search Engine</description><generator>Hugo</generator><language>en-us</language><managingEditor>info@qdrant.tech (Andrey Vasnetsov)</managingEditor><webMaster>info@qdrant.tech (Andrey Vasnetsov)</webMaster><atom:link href="https://deploy-preview-2342--condescending-goldwasser-91acf0.netlify.app/documentation/send-data/index.xml" rel="self" type="application/rss+xml"/><item><title>Databricks Ingestion</title><link>https://deploy-preview-2342--condescending-goldwasser-91acf0.netlify.app/documentation/send-data/databricks/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><author>info@qdrant.tech (Andrey Vasnetsov)</author><guid>https://deploy-preview-2342--condescending-goldwasser-91acf0.netlify.app/documentation/send-data/databricks/</guid><description>&lt;h1 id="ingest-databricks-data-into-qdrant"&gt;Ingest Databricks Data into Qdrant&lt;/h1&gt;
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 &lt;th&gt;Time: 30 min&lt;/th&gt;
 &lt;th&gt;Level: Intermediate&lt;/th&gt;
 &lt;th&gt;&lt;a href="https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/4750876096379825/93425612168199/6949977306828869/latest.html" target="_blank" rel="noopener nofollow"&gt;Complete Notebook&lt;/a&gt;&lt;/th&gt;
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&lt;p&gt;&lt;a href="https://www.databricks.com/" target="_blank" rel="noopener nofollow"&gt;Databricks&lt;/a&gt; is a unified analytics platform for working with big data and AI. It&amp;rsquo;s built around Apache Spark, a powerful open-source distributed computing system well-suited for processing large-scale datasets and performing complex analytics tasks.&lt;/p&gt;
&lt;p&gt;Apache Spark is designed to scale horizontally, meaning it can handle expensive operations like generating vector embeddings by distributing computation across a cluster of machines. This scalability is crucial when dealing with large datasets.&lt;/p&gt;</description></item><item><title>Querying with Airflow</title><link>https://deploy-preview-2342--condescending-goldwasser-91acf0.netlify.app/documentation/send-data/qdrant-airflow-astronomer/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><author>info@qdrant.tech (Andrey Vasnetsov)</author><guid>https://deploy-preview-2342--condescending-goldwasser-91acf0.netlify.app/documentation/send-data/qdrant-airflow-astronomer/</guid><description>&lt;h1 id="qdrant-semantic-querying-with-airflow-and-astronomer"&gt;Qdrant Semantic Querying with Airflow and Astronomer&lt;/h1&gt;
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 &lt;th&gt;Time: 45 min&lt;/th&gt;
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&lt;p&gt;In this tutorial, you will use Qdrant as a &lt;a href="https://airflow.apache.org/docs/apache-airflow-providers-qdrant/stable/index.html" target="_blank" rel="noopener nofollow"&gt;provider&lt;/a&gt; in &lt;a href="https://airflow.apache.org/" target="_blank" rel="noopener nofollow"&gt;Apache Airflow&lt;/a&gt;, an open-source tool that lets you setup data-engineering workflows.&lt;/p&gt;
&lt;p&gt;You will write the pipeline as a DAG (Directed Acyclic Graph) in Python. With this, you can leverage the powerful suite of Python&amp;rsquo;s capabilities and libraries to achieve almost anything your data pipeline needs.&lt;/p&gt;</description></item><item><title>Kafka Streaming into Qdrant</title><link>https://deploy-preview-2342--condescending-goldwasser-91acf0.netlify.app/documentation/send-data/data-streaming-kafka-qdrant/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><author>info@qdrant.tech (Andrey Vasnetsov)</author><guid>https://deploy-preview-2342--condescending-goldwasser-91acf0.netlify.app/documentation/send-data/data-streaming-kafka-qdrant/</guid><description>&lt;h1 id="stream-real-time-data-into-qdrant-with-kafka-and-confluent"&gt;Stream Real-Time Data into Qdrant with Kafka and Confluent&lt;/h1&gt;
&lt;p&gt;&lt;strong&gt;Author:&lt;/strong&gt; &lt;a href="https://www.linkedin.com/in/kameshwara-pavan-kumar-mantha-91678b21/" target="_blank" rel="noopener nofollow"&gt;M K Pavan Kumar&lt;/a&gt; , research scholar at &lt;a href="https://iiitk.ac.in" target="_blank" rel="noopener nofollow"&gt;IIITDM, Kurnool&lt;/a&gt;. Specialist in hallucination mitigation techniques and RAG methodologies.
• &lt;a href="https://github.com/pavanjava" target="_blank" rel="noopener nofollow"&gt;GitHub&lt;/a&gt; • &lt;a href="https://medium.com/@manthapavankumar11" target="_blank" rel="noopener nofollow"&gt;Medium&lt;/a&gt;&lt;/p&gt;
&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;This guide will walk you through the detailed steps of installing and setting up the &lt;a href="https://github.com/qdrant/qdrant-kafka" target="_blank" rel="noopener nofollow"&gt;Qdrant Sink Connector&lt;/a&gt;, building the necessary infrastructure, and creating a practical playground application. By the end of this article, you will have a deep understanding of how to leverage this powerful integration to streamline your data workflows, ultimately enhancing the performance and capabilities of your data-driven real-time semantic search and RAG applications.&lt;/p&gt;</description></item></channel></rss>