BigData processing and Machine Learning introduction with Apache Spark

BigData processing and Machine Learning introduction with Apache Spark

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Apache Spark is becoming the platform of choice for data scientists who want to design and run large scale machine learning applications in programming languages like Python, Scala, R or Java. Spark’s popularity is due the fact that it excels in in-memory computations, which is some cases is 10x-100x faster than Hadoop MapReduce.


The workshop is divided in two parts, the first part is mainly dedicated to Spark’s general concepts and the second part is an introduction into Machine Learning by using Spark. As a dataset for all our examples during the workshop offered by, from where we can build interesting examples and get meaningful insights from data.


Part 1

We will begin with an introduction to Apache Spark. Through examples we will understand how to build applications using one of the following programming languages: Java, Python.


What you will learn: 

● An introduction into Apache Spark 

● Installing and setting up Spark 

● Spark architecture 

● RDD fundamentals 

● DataFrames 

● Spark operations 

● SparkSQL 

● What’s new in Spark 2.0


Part 2

Wondering how to use machine learning at scale? Research datasets grow rapidly in size and complexity. This makes it difficult to process or edit them using standard applications and systems. Apache Spark offer an interesting alternative for data-intensive research.


What you will learn: 

● Machine Learning Basics 

● Spark ML – Basic concepts 

● Feature Engineering 

● Classification 

● Clustering 

● Train a model with Spark MLlib’s DataFrame 

● Evaluate model accuracy 

● Visualizing data


If you’d like to speak at one of future events, please get in touch


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