Spark For Python Developers
Even better, the amazing developers behind Jupyter have done all the heavy lifting for you. They publish a Dockerfile that includes all the PySpark dependencies along with Jupyter. So, you can experiment directly in a Jupyter notebook!
Spark for Python Developers
There are a number of ways to execute PySpark programs, depending on whether you prefer a command-line or a more visual interface. For a command-line interface, you can use the spark-submit command, the standard Python shell, or the specialized PySpark shell.
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_OC_InitNavbar("child_node":["title":"My library","url":" =114584440181414684107\u0026source=gbs_lp_bookshelf_list","id":"my_library","collapsed":true,"title":"My History","url":"","id":"my_history","collapsed":true,"title":"Books on Google Play","url":" ","id":"ebookstore","collapsed":true],"highlighted_node_id":"");Spark for Python DevelopersAmit NandiPackt Publishing, 24 Dec 2015 - Python (Computer program language) - 206 pages 0 ReviewsReviews aren't verified, but Google checks for and removes fake content when it's identifiedA concise guide to implementing Spark Big Data analytics for Python developers, and building a real-time and insightful trend tracker data intensive appAbout This Book- Set up real-time streaming and batch data intensive infrastructure using Spark and Python- Deliver insightful visualizations in a web app using Spark (PySpark)- Inject live data using Spark Streaming with real-time eventsWho This Book Is ForThis book is for data scientists and software developers with a focus on Python who want to work with the Spark engine, and it will also benefit Enterprise Architects. All you need to have is a good background of Python and an inclination to work with Spark.What You Will Learn- Create a Python development environment powered by Spark (PySpark), Blaze, and Bookeh- Build a real-time trend tracker data intensive app- Visualize the trends and insights gained from data using Bookeh- Generate insights from data using machine learning through Spark MLLIB- Juggle with data using Blaze- Create training data sets and train the Machine Learning models- Test the machine learning models on test datasets- Deploy the machine learning algorithms and models and scale it for real-time eventsIn DetailLooking for a cluster computing system that provides high-level APIs? Apache Spark is your answer-an open source, fast, and general purpose cluster computing system. Spark's multi-stage memory primitives provide performance up to 100 times faster than Hadoop, and it is also well-suited for machine learning algorithms.Are you a Python developer inclined to work with Spark engine? If so, this book will be your companion as you create data-intensive app using Spark as a processing engine, Python visualization libraries, and web frameworks such as Flask.To begin with, you will learn the most effective way to install the Python development environment powered by Spark, Blaze, and Bookeh. You will then find out how to connect with data stores such as MySQL, MongoDB, Cassandra, and Hadoop.You'll expand your skills throughout, getting familiarized with the various data sources (Github, Twitter, Meetup, and Blogs), their data structures, and solutions to effectively tackle complexities. You'll explore datasets using iPython Notebook and will discover how to optimize the data models and pipeline. Finally, you'll get to know how to create training datasets and train the machine learning models.By the end of the book, you will have created a real-time and insightful trend tracker data-intensive app with Spark.Style and approach This is a comprehensive guide packed with easy-to-follow examples that will take your skills to the next level and will get you up and running with Spark. What people are saying - Write a reviewWe haven't found any reviews in the usual places.About the author (2015)Amit Nandi studied physics at the Free University of Brussels in Belgium, where he did his research on computer generated holograms. Computer generated holograms are the key components of an optical computer, which is powered by photons running at the speed of light. He then worked with the university Cray supercomputer, sending batch jobs of programs written in Fortran. This gave him a taste for computing, which kept growing. He has worked extensively on large business reengineering initiatives, using SAP as the main enabler. He focused for the last 15 years on start-ups in the data space, pioneering new areas of the information technology landscape. He is currently focusing on large-scale data-intensive applications as an enterprise architect, data engineer, and software developer. He understands and speaks seven human languages. Although Python is his computer language of choice, he aims to be able to write fluently in seven computer languages too.
This course is designed for software engineers and architects who are willing to design and develop big data engineering projects using Apache Spark. It is also designed for programmers and developers who are aspiring to grow and learn data engineering using Apache Spark.
Python is the leading language preferred by the data science community. Even with in Spark community, python API has seen tremendous upsurge in last few years. According to databricks, company behind the Apache Spark, 60% of the commands written on their notebook is python compared to 23% of them in Scala.
Spark has excellent support for python with Pyspark project. Pyspark allows developers to access all different parts of spark like SQL,ML etc using python language.Still it has not yet reached wider python community. The reason is majority of python data developers prefer Pandas API.
Koalas allows python developers to write pandas API code on top spark dataframe which gives best of both worlds. Now developers can write code in pandas API and get all the performance benefits of spark.
From spark 3.2, pandas API will be added to mainline spark project. No more need of third party library. So pandas API going to be yet another API with Dataframe DSL and SQL API to manipulate data in spark.
Scala language has several syntactic sugars when programming with Apache Spark, so big data professionals need to be extremely cautious when learning Scala for Spark. Programmers might find the syntax of Scala for programming in Spark crazy hard at times. Few libraries in Scala makes it difficult to define random symbolic operators that can be understood by inexperienced programmers. While using Scala, developers need to focus on the readability of the code. Scala is a sophisticated language with flexible syntax when compared to Java or Python. There is an increasing demand for Scala developers because big data companies value developers who can master a productive and robust programming language for data analysis and processing in Apache Spark.
Refactoring the program code of a statically typed language like Scala is much easier and hassle-free than refactoring the code of dynamic language like Python. Developers often face difficulties after modifying Python program code as it creates more bugs than fixing the older ones. Typecheck in Python actually conquests the duck-typing philosophy of python. It is better to be slow and safe using Scala for Spark than being fast and dead using Python for Spark.
Scala and Python languages are equally expressive in the context of Spark so by using Scala or Python the desired functionality can be achieved. Either way the programmer creates a Spark content and calls functions on that. Python is a more user friendly language than Scala. Python is less verbose making it easy for developers to write a script in Python for Spark. Ease of use is a subjective factor because it comes down to the personal preference of the programmer.
Scala programming language has several existential types, macros and implicits. The arcane syntax of Scala might make it difficult to experiment with the advanced features which might be incomprehensible to the developers. However, the advantage of Scala comes with using these powerful features in important frameworks and libraries.
Apache Spark framework is written in Scala, so knowing Scala programming language helps big data developers dig into the source code with ease, if something does not function as expected. Using Python increases the probability for more issues and bugs because translation between 2 different languages is difficult. Using Scala for Spark provides access to the latest features of the Spark framework as they are first available in Scala and then ported to Python.
Deciding on Scala vs Python for Spark depends on the features that best fit the project needs as each one has its own pros and cons. Before choosing a language for programming with Apache Spark it is necessary that developers learn Scala and Python to familiarize with their features. Having learnt both Python and Scala, it should be pretty easy to make a decision on when to use Scala for Spark and when to use Python for Spark. Language choice for programming in Apache Spark purely depends on the problem to solve.
If not configured, dbt-spark will use the built-in defaults: the all-purpose cluster (based on cluster in your connection profile) without creating a notebook. The dbt-databricks adapter will default to the cluster configured in http_path. We encourage explicitly configuring the clusters for Python models in Databricks projects.
Today, Spark has become one of the most active projects in the Hadoop ecosystem, with many organizations adopting Spark alongside Hadoop to process big data. In 2017, Spark had 365,000 meetup members, which represents a 5x growth over two years. It has received contribution by more than 1,000 developers from over 200 organizations since 2009.
Apache Spark natively supports Java, Scala, R, and Python, giving you a variety of languages for building your applications. These APIs make it easy for your developers, because they hide the complexity of distributed processing behind simple, high-level operators that dramatically lowers the amount of code required. 041b061a72