by Matt Bornstein / Martin Casado / Jennifer Li from a16z.com

Five years ago, if you were building a system, it was a result of the code you wrote. Now, it’s built around the data that is fed into that system. And a new class of tools and technologies have emerged to process data for both analytics and operational AI/ ML.

by Satish Chandra Gupta from satishchandragupta.com

For deploying big-data analytics, data science, and machine learning (ML) applications in real-world, analytics-tuning and model-training is only around 25% of the work. Approximately 50% of the effort goes into making data ready for analytics and ML. This article gives an introduction to the data pipeline and an overview of big data architecture alternatives

from databricks.com

The official page for common terms used in the Spark ecosystem

by Max Woolf from minimaxir.com

An example notebook using Spark and R to process and analyze Product Reviews on Amazon

by Ion Stoica / Vida Ha from databricks.com

Databricks provides a powerful platform to process, analyze, and visualize Josh Duffney big and small data in one place. In this blog, we will illustrate how to analyze access logs of an Apache HTTP web server using Notebooks. Notebooks allow users to write and run arbitrary Apache Spark code and interactively visualize the results. Currently, notebooks support three languages: Scala, Python, and SQL. In this blog, we will be using Python for illustration.

from sparkbyexamples.com

In this Apache Spark Tutorial, you will learn Spark with Scala code examples and every sample example explained here is available at Spark Examples Github Project for reference. All Spark examples provided in this Apache Spark Tutorials are basic, simple, easy to practice for beginners who are enthusiastic to learn Spark, and these sample examples were tested in our development environment.