Top Tweets for #Pyarrow
🎉 With #QuixStreams, you can easily serialize and deserialize #PyArrow messages in real time. Say hello to seamless integration with QuixStreams. Get ready to revolutionize the way you work with data! @PyArrow #DataProcessing #RealTimeAnalytics 🚀📊
https://t.co/sin21zAays

In 1.59.0:
* There’s a new decorator `@resolve_from_config`. Thanks to Jan Hurst for the addition🙏!
* new @dltHub data saver & loader - see https://t.co/vzvbIxy2oj for use
* @pandas_dev table loader by Swapnil D.
* we now have a #pyarrow result!
2/4

News for the week:
TL;DR:
- Released 1.59.0 with more user contributions! We have a decorator, #pyarrow feature, and more data savers with @pandas_dev & @dltHub
- @ProjectJupyter tutorial with Hamilton and blog with data from @PeopleDataLabs
#opensource #python
1/4
You can easily query a Delta table with DuckDB. 🦆🦀
#DeltaLake tables can be converted to PyArrow datasets and #duckdb can read PyArrow datasets.
Query engines should support reading #PyArrow datasets so users have some out-of-the-box interoperability that's performant.

How fast can a CSV file be processed? I explain in detail comparing many options such as @pandas_dev, @duckdb, @DataPolars, #Python, #R, #rustlang and more in this new blog post:
https://t.co/j4a4Mv099A
数据架构再次升级
引入 #DeltaLake 存储, #DeltaLake + #pyarrow + #duckdb 查询分析
重点是使用https://t.co/tHEDW31Kjb
好处如下
1. 替换之前手动维护 S3 路径表代码
2. 支持 ASOF join,砍掉一大波 查询后代码过滤 的逻辑
3. compact + vacuum 优化表存储
4. z_order 查询加速
🤡🤡🤡
Dataframe Interoperability in action! We explain the dataframe interchange protocol and how it’s pushing composability forward in the #Python ecosystem. We demo it using @ApacheArrow to visualize a #PyArrow table with @vega_vis Altair without conversion. https://t.co/6SbbCq15jO
“This is a game-changer.” In this blog, Tyler White, Solutions Architect at @SnowflakeDB, shows how the new Snowflake Connector for #Python eliminates @ApacheArrow #PyArrow version conflicts. Check it out! https://t.co/clH68w73do
Preview release of the nanoarrow-based @SnowflakeDB Connector for #Python is available! This connector is ~10x smaller in size and removes a hard dependency on a specific version of #PyArrow. Learn how the nanoarrow integration makes it possible. https://t.co/kWkOZSj0vS
Language interoperability is a key to productivity. Today, @fmic_ shows how to create a report using a @uscensusbureau PUMS dataset stored in @SnowflakeDB by passing data from #Python to #rstats using #Parquet, #PyArrow, and @IbisData https://t.co/MVc4VOesQv
Explore Lance, the new data format revolutionizing #MachineLearning. From fast random access to data versioning, Lance streamlines your #AI workflow. It integrates with popular tools like #pandas, #duckdb, #polars and #pyarrow. #DataScience https://t.co/mdcAGbUh3u

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