Apache Spark: Master Big Data with PySpark and DataBricks

This class is meant to help you learn how to do ETL operations in Databricks using pyspark, build production-ready ML models, learn about spark optimization, and learn about distributed computing.

There are a lot of different things that go into the development of big

They work with huge data processing systems and databases in large-scale computing environments. Big data engineers help businesses figure out how well they’re doing, figure out market demographics, and figure out what will happen in the future and how the market will change.

In this case, Azure Databricks:

Azure Databricks is a data analytics platform that works well with Microsoft’s Azure cloud service. Databricks SQL, Databricks Data Science & Engineering, and Databricks Machine Learning are three environments for building data-heavy applications on Azure Databricks.

People live in a house called the Data Lake.

An idea called a data lakehouse is a way to store data that includes parts of the data warehouse and parts of the data lake. People who work in “data lakehouses,” which are like “data warehouses,” use the same data structures and management features to store information in “data lakes,” which are usually cheaper to store.

Spark streaming that is structured:

Structured Streaming is a stream processing engine built on top of the Spark SQL engine.

Structured Streaming is a way to process streams quickly, scalable, fault-tolerant, and end-to-end at the same time without the user having to think about streaming.

People who study natural language:

Natural Language Processing, or NLP for short, is the automatic manipulation of natural language, like speech and text, by software. This is what NLP is all about.

For more than 50 years, people have been studying how computers can process natural language. It came from linguistics, which was a field that started when computers came out.