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Learn PySpark, earn certificates with paid and free online courses from Imperial College London, UC Berkeley and other top universities around the world. Read reviews to decide if a class is right for you.
Learn how to use Spark with Python, including Spark Streaming, Machine Learning, Spark 2.0 DataFrames and more!
Master PySpark and Big Data analytics with this concise course. Learn data handling, aggregation, filtering, and more in just 1-2 hours.
Master Apache Spark using Spark SQL as well as PySpark with Python3 with complementary lab access
Mastering AWS & PySpark: Spark, PySpark, AWS, Spark Ecosystem, Hadoop, and Spark Applications [AWS, Hadoop, Pyspark]
Learn to create big data streaming pipelines with Apache Spark using Python. Analyze live Tweet data, integrate with Apache Kafka, and master Spark's latest features. Offered by Udemy.
Big data Python Spark PySpark coding framework logging error handling unit testing PyCharm PostgreSQL Hive data pipeline
Learn PySpark, an interface for Apache Spark in Python. PySpark is often used for large-scale data processing and machine learning.
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
Get up and running with Apache Spark quickly. This practical hands-on course shows Python users how to work with Apache PySpark to leverage the power of Spark for data science.
In this demo, you'll learn how to get data into PySpark.
This syllabus covers an introduction to Pyspark, DataFrames, Mlib, Databricks, and Linear Regression In-Depth Maths Intuition.
Learn PySpark best practices from a Cloudera data scientist in under an hour. Understand dependency management, testing, and Spark's computational model for efficient data analysis.
Learn to architect for data quality in the lakehouse using Delta Lake and PySpark with Databricks. Understand data observability techniques and reliability across metadata, storage, and query engine tiers.
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