[GitHub] [arrow-site] nealrichardson commented on a change in pull request #168: Cross-posted blog post with DuckDB

[GitBox <gi...@apache.org>]

nealrichardson commented on a change in pull request #168:
URL: https://github.com/apache/arrow-site/pull/168#discussion_r762037257



##########
File path: _posts/2021-12-9-arrow-duckdb.md
##########
@@ -0,0 +1,381 @@
+---
+layout: post
+title: "DuckDB quacks Arrow: A zero-copy data integration between Arrow and DuckDB"
+date: "2021-12-9 00:00:00"
+author: Pedro Holanda, Jonathan Keane
+categories: [application]
+---
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements.  See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License.  You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+*TLDR: The zero-copy integration between DuckDB and Apache Arrow allows for rapid analysis of larger than memory datasets in Python and R using either SQL or relational APIs.*
+
+Part of [Apache Arrow](https://arrow.apache.org) is an in-memory data format optimized for analytical libraries. Like Pandas and R Dataframes, it uses a columnar data model. But the Arrow project contains more than just the format: The Arrow C++ library, which is accessible in Python, R, and Ruby via bindings, has additional features that allow you to compute efficiently on datasets. These additional features are on top of the implementation of the in-memory format described above. The datasets may span multiple files in Parquet, CSV, or other formats, and files may even be on remote or cloud storage like HDFS or Amazon S3. The Arrow C++ query engine supports the streaming of query results, has an efficient implementation of complex data types (e.g., Lists, Structs, Maps), and can perform important scan optimizations like Projection and Filter Pushdown.

Review comment:
       yes, above this paragraph




-- 
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

To unsubscribe, e-mail: commits-unsubscribe@arrow.apache.org

For queries about this service, please contact Infrastructure at:
users@infra.apache.org