- Agile Data Science Tutorial
- Agile Data Science - Home
- Agile Data Science - Introduction
- Methodology Concepts
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- Agile Tools & Installation
- Data Processing in Agile
- SQL versus NoSQL
- NoSQL & Dataflow programming
- Collecting & Displaying Records
- Data Visualization
- Data Enrichment
- Working with Reports
- Role of Predictions
- Extracting features with PySpark
- Building a Regression Model
- Deploying a predictive system
- Agile Data Science - SparkML
- Fixing Prediction Problem
- Improving Prediction Performance
- Creating better scene with agile & data science
- Implementation of Agile
- Agile Data Science Useful Resources
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- Agile Data Science - Discussion
Extracting features with PySpark
In this chapter, we will learn about the application of the extracting features with PySpark in Agile Data Science.
Overview of Spark
Apache Spark can be defined as a fast real-time processing framework. It does computations to analyze data in real time. Apache Spark is introduced as stream processing system in real-time and can also take care of batch processing. Apache Spark supports interactive queries and iterative algorithms.
Spark is written in “Scala programming language”.
PySpark can be considered as a combination of Python with Spark. PySpark offers PySpark shell, which links Python API to the Spark core and initializes the Spark context. Most of the data scientists use PySpark for tracking features as discussed in the previous chapter.
In this example, we will focus on the transformations to build a dataset called counts and save it to a particular file.
text_file = sc.textFile("hdfs://...") counts = text_file.flatMap(lambda line: line.split(" ")) \ .map(lambda word: (word, 1)) \ .reduceByKey(lambda a, b: a + b) counts.saveAsTextFile("hdfs://...")
Using PySpark, a user can work with RDDs in python programming language. The inbuilt library, which covers the basics of Data Driven documents and components, helps in this.
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