Apache Spark is among the fastest-growing tools in data science, due to its streaming, speed, and scalability capabilities. In particular, the Spark MLlib machine learning library is rapidly becoming a must-have for those working in AI. Stay up to date with the latest Apache Spark skills.
Learn the basics of Apache Spark.
Build your data engineering skills using Spark.
Use Spark as a primary tool for AI and machine learning projects.
Extending Hadoop for Data Science: Streaming, Spark, Storm, and Kafka with Lynn Langit
Extend your Hadoop data science knowledge to other Apache data science tools and attendant technologies including Apache Spark, Storm, Kafka, and more.
2h 53m • COURSE
Apache Spark Essential Training with Ben Sullins
Get up to speed with Spark, and discover how to leverage this powerful platform to efficiently and effectively work with big data.
1h 27m • COURSE
Apache Spark Essential Training: Big Data Engineering with Kumaran Ponnambalam
Discover how to make Apache Spark work with other big data technologies to build data pipelines for data engineering and DevOps.
1h 40m • COURSE
Spark for Machine Learning & AI with Dan Sullivan
Discover how to work with the powerful Apache Spark platform for machine learning. Learn about preprocessing data, applying algorithms to a variety of machine learning problems, and more.
1h 51m • COURSE
You'll learn Apache Spark skills with these experts
Lynn Langit is a cloud architect who works with Amazon Web Services and Google Cloud Platform.
Lynn specializes in big data projects. She has worked with AWS Athena, Aurora, Redshift, Kinesis, and the IoT. She has also done production work with Databricks for Apache Spark and Google Cloud Dataproc, Bigtable, BigQuery, and Cloud Spanner.
Lynn is also the cofounder of Teaching Kids Programming. She has spoken on data and cloud technologies in North and South America, Europe, Africa, Asia, and Australia.
As a lifelong data geek, Ben Sullins dedicates his time to helping others use data wisely.
Ben makes information meaningful and has fun doing it. His background affords him a unique set of knowledge that sets him apart in the data community. Since the late 1990s, he has consulted many high-tech companies, including Facebook, Microsoft, LinkedIn, Cisco, Mozilla, Pluralsight, and Genentech, on democratizing data in their organizations. Moreover, Ben spent three months leading the charge at Facebook to grow its data culture by demonstrating proper tool implementation and data visualization techniques using Tableau. And with this expertise, Ben aims to provide exceptional service to his customers by enriching their lives with impactful smart data.
Kumaran Ponnambalam has been working with data for more than 20 years.
He has built enterprise and cloud applications that ingest data to produce meaningful insights for its consumers. Data has always intrigued Kumaran and he has always searched for ways to mine, manage, and master it. Using analytics to solve business problems is his key interest domain. Of late, he has taken a keen interest in building quality courses for people to understand and use data. Big data analytics is fast growing, but quality education, especially in application areas, is lacking and he wants to contribute to it.
Dan Sullivan, PhD, is an enterprise architect and big data expert.
Dan specializes in data architecture, analytics, data mining, statistics, data modeling, big data, and cloud computing. In addition, he holds a PhD in genetics, bioinformatics, and computational biology. Dan works regularly with Spark, Oracle, NoSQL, MongoDB, Redis, R, and Python. He has extensive writing experience in topics including cloud computing, big data, Hadoop, and security.