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

O Apache Spark é um sistema de computação distribuído, poderoso e de código aberto que pode processar big data. É conhecido por sua velocidade e facilidade de uso, o que o torna popular entre engenheiros de software e cientistas de dados.

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.

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.

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.

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.

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