ianmcook commented on code in PR #609:
URL: https://github.com/apache/arrow-site/pull/609#discussion_r1983968132
##########
_posts/2025-03-04-fast-streaming-inserts-in-duckdb-with-adbc.md:
##########
@@ -0,0 +1,201 @@
+---
+layout: post
+title: "Fast Streaming Inserts in DuckDB with ADBC"
+description: "ADBC enables high throughput insertion into DuckDB"
+date: "2025-03-04 00:00:00"
+author: loicalleyne
+categories: [application]
+image:
+ path: /img/adbc-duckdb/adbc-duckdb.png
+ height: 560
+ width: 1200
+---
+
+<!--
+{% 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 %}
+-->
+
+# Fast Streaming Inserts in DuckDB with ADBC
+
+<img src="{{ site.baseurl }}/img/adbc-duckdb/adbc-duckdb.png" width="100%"
class="img-responsive" alt="" aria-hidden="true">
+# TL;DR
+
+DuckDB is rapidly becoming an essential part of data practitioners' toolbox,
finding use cases in data engineering, machine learning, and local analytics.
In many cases DuckDB has been used to query and process data that has already
been saved to storage (file-based or external database) by another process.
Arrow Database Connectivity APIs enable high-throughput data processing using
DuckDB as the engine.
+
+# How it started
+
+The company I work for is the leading digital out-of-home marketing platform,
including a programmatic ad tech stack. For several years, my technical
operations team was making use of logs emitted by the real-time programmatic
auction system in the [Apache Avro](http://avro.apache.org/) format. Over time
we've built an entire operations and analytics back end using this data. Avro
files are row-based which is less than ideal for analytics at scale, in fact
it's downright painful. So much so that I developed and contributed an Avro
reader feature to the [Apache Arrow Go](https://github.com/apache/arrow-go)
library to be able to convert Avro files to parquet. This data pipeline is now
humming along transforming hundreds of GB/day from Avro to Parquet.
+
+Since "any problem in computer science can be solved with another layer of
indirection", the original system has grown layers (like an onion) and started
to emit other logs, this time in [Apache Parquet](https://parquet.apache.org/)
format...
+<figure style="text-align: center;">
+ <img src="{{ site.baseurl }}/img/adbc-duckdb/muchrejoicing.gif" width="80%"
class="img-responsive" alt="Figure 1: And there was much rejoicing">
Review Comment:
Any chance there's a higher-resolution version of this available? It's
pretty pixelated.
--
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: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
