Insinyur Data Profesional dalam Python

Discover modern data architecture's key components, from ingestion and serving to governance and orchestration.

The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds.

Learn the essentials of VMs, containers, Docker, and Kubernetes. Understand the differences to get started!

Kursus ini memperkenalkan dbt untuk pemodelan data, transformasi, pengujian, dan pembuatan dokumentasi.

Pelajari konsep dasar pemrograman berorientasi objek (OOP), membuat kelas dan objek kustom!

Dalam Pengenalan DevOps ini, Anda akan menguasai dasar-dasar DevOps dan mempelajari konsep-konsep kunci, alat, dan teknik untuk meningkatkan produktivitas.

Sharpen your debugging skills to enhance sales data accuracy.

Dapatkan pengenalan tentang Docker dan temukan pentingnya dalam kotak alat profesional data. Pelajari tentang kontainer Docker, gambar, dan lainnya.

Master PySpark to handle big data with ease—learn to process, query, and optimize massive datasets for powerful analytics!

This chapter introduces the exciting world of Big Data, as well as the various concepts and different frameworks for processing Big Data. You will understand why Apache Spark is considered the best framework for BigData.

The main abstraction Spark provides is a resilient distributed dataset (RDD), which is the fundamental and backbone data type of this engine. This chapter introduces RDDs and shows how RDDs can be created and executed using RDD Transformations and Actions.

In this chapter, you'll learn about Spark SQL which is a Spark module for structured data processing. It provides a programming abstraction called DataFrames and can also act as a distributed SQL query engine. This chapter shows how Spark SQL allows you to use DataFrames in Python.

Step into a data engineer's shoes and master data cleaning with PySpark on an e-commerce orders dataset!

In this chapter, we learn how to download data files from web servers via the command line. In the process, we also learn about documentation manuals, option flags, and multi-file processing.

In the last chapter, we bridge the connection between command line and other data science languages and learn how they can work together. Using Python as a case study, we learn to execute Python on the command line, to install dependencies using the package manager pip, and to build an entire model pipeline using the command line.

Pelajari perbedaan antara batching dan streaming, skalabilitas sistem streaming, dan penerapan di dunia nyata.

Understand how data engineering can impact your business.