https://medium.com/data-engineer-things/apache-iceberg-the-hadoop-of-the-modern-data-stack-c83f63a4ebb9

Apache Spark 是一个强大的、开源的、分布式计算系统，可以处理大数据。它以速度和易用性而闻名，因此受到软件工程师和数据科学家的欢迎。然而，要充分发挥 Apache Spark 的潜力，必须采用能够提高性能和效率的最佳实践。

Data processing and statistics gathering are essential tasks in today's data-driven world. Engineers frequently find themselves choosing between tools like PySpark and Scala when embarking on these tasks.

Dask is a flexible library for parallel computing in Python. It is designed to scale from a single machine to a cluster of machines seamlessly. By using Dask, you can manage and manipulate large datasets that are too big to fit into memory on a single machine.

Several years ago, I started a side project that I thought would be fun: collecting and loading SEC filings for ETF and Mutual Fund Holdings on a monthly basis. I wanted to essentially automate the collection of the SEC filings

Data architecture refers to the design and organization of data structures and systems within an organization. It defines how data is collected, stored, and used, serving as a blueprint for managing data assets.

If you're a software engineer, there are various SDK's and connectors available. On the other hand if you just want to look at document content, either the built-in "Query" section on the Couchbase console, or a third-party tool that has a driver to connect.

In today's data-driven world, organizations are generating vast amounts of data every second. This data can be a goldmine for insights when processed and analyzed effectively. One of the most powerful tools in this realm is Apache Spark.

The analytics console looks very much like the query console with the exception of the panels on the right. This is where you can map data structures from the local or remote Couchbase collections as sources. The analytics service makes a copy of the original data, and provides the ability to index it separately from the original source.