Big data processing and analytics at speed and scale using command line tools
Key Features
- Perform string processing, numerical computations and more using command line tools
- Explore, understand and manage data science tasks using essential commands
- Know in and out of the development workflow for data science using this practical guide
Book Description
Command Line has been in existence on UNIX based OS and Windows platform in the form of Bash shell for over 3 decades. However, very little is known to developers, how Command line tools can be OSEMN (pronounced as awesome and stands for Obtaining, Scrubbing, Exploring, Modeling, and iNterpreting data)to carry simple-to-advanced data science tasks at speed.
This book will start with the required concepts and installation to carry out data science tasks using the command line. You will learn to create a data pipeline to solve the problem of working with small to medium-sized files on a single machine. You will understand the power of the command line, and learn how to edit files using a text-based and an in-line editor, call API endpoints. You will not only learn how to automate jobs and scripts but also learn how to visualize data using the command line.
By the end of this book, you will learn how to speed up the process and perform automated tasks using command line tools.
What you will learn
- Learn to setup the command line for data science
- Use AWK programming language commands to quickly search in large data sets.
- Work with files and API's using the command line
- Automate jobs and scripts
- Send and receive data from the command line
- Visualize data using the command line
- Uncover the machine level programming practices with a modern approach for Data Science.
Who This Book Is For
This book is for data scientists and data analysts with little to no knowledge of the command line but has an understanding of data science. Perform everyday data science tasks using the power of command line tools.
Jason Morris is a systems and research engineer with over 18 years of experience in system architecture, research engineering, and large data analysis. His primary focus is machine learning with TensorFlow, CUDA, and Apache Spark.
Jason is also a speaker and a consultant for designing large-scale architectures, implementing best security practices on the cloud, creating near real-time image detection analytics with deep learning, and developing serverless architectures to aid in ETL. His most recent roles include solution architect, big data engineer, big data specialist, and instructor at Amazon Web Services. He is currently the Chief Technology Officer of Next Rev Technologies and his favorite command line program is netcat.
Chris McCubbin is a data scientist and software developer with 20 years experience in developing complex systems and analytics. He co-founded the successful big data security startup Sqrrl, since acquired by Amazon. He has also developed smart swarming systems for drones, social network analysis systems in MapReduce and big data security analytic platforms using the Apache projects Accumulo and Spark. He has been using the Unix command line starting on IRIX platforms in college and his favorite command line program is find.
