About this course
- Hands-on PySpark
- Spark MLIB
- Moving from RDD to dataframe API
- Clustering with pyspark
- Music data case studies
Get Spark: PySpark course completion certificate from Great learning which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.
Frequently Asked Questions
Pyspark is a great language for data scientists to learn since it enables scalable analysis and machine learning pipelines. Pyspark is majorly used for processing structured and semi-structured datasets and pyspark also provides optimized APIs which can read the data from various data formats containing various or different file formats and then with pyspark we can also process the data by making use of SQL as well as HIVEQL.
If we know the basic knowledge of python or some other programming languages like java learning pyspark is not difficult since spark provides java, python and Scala APIs. Pyspark realizes the potential of bringing together big data and machine learning, it is a bit difficult to get started with spark but the spark has some attribute features like high speed, easy access and also applied for streaming analytics. In addition to all this, the framework of spark and python helps pyspark access and process the big data easily. Thus, pyspark can be easily learnt if we possess some basic knowledge of python, java and other programming languages.
Scratch uses event-driven programming and this can be used as an introductory language because this creates interesting programs which are relatively easy and this skill can be applied to learn other programming languages like python, java. Scratch supports one-dimensional arrays, lists floating-point scalars and strings but only with limited string manipulation. Hence, scratch has nothing to do with pyspark since this block-based visual or graphics programming language. Since pyspark is an API developed for python and apache spark. Scratch can only be used to learn python.
To learn pyspark we need to learn both apache spark and python. So, in order to learn apache-spark, it takes approximately one or two weeks just to learn the basics of spark and it takes more time, maybe one month to get perfect with the language or even more. This actually depends on the level of complexity of the problems that we try to solve. It actually takes 2-3 weeks to get comfortable with the framework. And to learn python it takes almost 8 weeks to learn to get comfortable with the syntax and like if we know the programming languages like java, c, c++ then python becomes easier to learn, it does not take so long to learn. And when we know both these languages i.e., apache-spark and python we need at least 25-30 hours to learn pyspark and if we are good with both the languages i.e., python and spark, it takes at most 50 hours to get practical hands-on with the pyspark. It takes at least five weeks to learn pyspark approximately
Pyspark is very well used in many sectors and organizations. Pyspark is used in the data sciences and machine learning community as these are widely used data science libraries written in python programming languages including its libraries like NumPy, pandas, TensorFlow, etc. pyspark brings both robust and cost-effective ways to run machine learning applications on billions or trillions of data distributed clusters which are 100 times faster than other python applications. Pyspark has been used in organizations like amazon, Walmart etc. and also used in different sectors like health, financials, education, entertainment, utilities, e-commerce and many other sectors.
Pyspark is the collaboration of apache spark and python. Python is used for general-purpose, is a high-level programming language, which acts as python API for spark. Python helps us to interface with resilient distribution databases (RDDs) in apache spark and python programming language. Python is known as an interpreted programming language whereas pyspark is the tool that supports python on spark which is specially used in big data whereas python is used in artificial intelligence, machine learning, big data etc. Basic knowledge of other programming languages will be a greater advantage for a programmer to learn python whereas pyspark needs the language of python and spark, this uses a library called py4j an API written in python which is created and licensed under apache-spark foundation. Python supports functionalities like databases, automation, text processing, scientific computing etc. and licensed under python.
Yes, we can use pandas in pyspark, we need a spark 2.3 runtime version and python 3 for the pandas UDFs functionality. The data types used in pyspark in the spark data frame, these objects can be a table distributed across a cluster and have functionality that is similar to the data frames in R and pandas. It is possible to use panda’s data frames when we are using spark, by calling the topandas () function on spark data frames, this returns the panda’s object. This function should be avoided generally except when we are working with the small data frames because this pulls the entire object into the memory into a single node. The main key difference between pandas and spark data frames is eager and lazy execution. In pyspark operations are delayed until the result is actually needed in the pipeline whereas while using panda’s data frame, everything is pulled inside the memory and also pandas’ operations are immediately approved. Operations on the pyspark data frames run parallel on different modes but this is not possible in the case of pandas. But still, panda’s API is more powerful than the spark. Because of this parallel execution on all the cores, pyspark is faster than the pandas. Hence, we can use pandas in pyspark.
Pyspark is the API developed and released by the apache spark foundation. Pyspark is the tool that supports python on spark whereas spark is the data computational framework that handles big data, this is written in Scala apache core is the main component, whereas pyspark is supported by a library known as PY4J which is originally written in a python programming language, this is developed in order to support python in spark. Knowledge of python and understanding of big data and spark are the prerequisites for spark. Spark works well with other languages such as java, python, R etc. programming knowledge of Scala and databases are the prerequisites for spark. Spark is a fast and general processing engine compatible with Hadoop data. Pyspark can be classified as a tool in the data science tools category while apache-spark is grouped under big data tools. This is the open-source cluster-computing framework built around speed, ease of use and streaming analytics, whereas pyspark is the collaboration of apache spark and python in order to support python with the spark.
No, we cannot use pyspark without spark, because pyspark is mainly upon the apache spark and python. Pyspark is built to make python work with spark. Pyspark is an API that is built for python and apache spark. pyspark is installed by pip is the subfolder of spark. It is a python API for using spark which is a parallel and distributed engine for running big data applications. To use pyspark we need to know both the apache spark framework and python programming language. Using this pyspark we can work with resilient distributed datasets RDDs in the python programming language, this library function is known as PYLAB function. This also offers a shell known as pyspark shell which links python API to the spark core, which initializes the spark context. This can be learnt if we have basic knowledge of both apache spark framework and python and other programming languages like java, c etc. so it is necessary to use pyspark with spark.
There are many ways or techniques to optimize the pyspark code (pyspark example code). Let us see what these techniques are deeper. Apache spark helps with in-memory data computations. One of the techniques is serialization, which plays a major role in the performance of any distributed applications, by default pyspark uses a java serializer and also uses another serializer known as the kryo serializer for better performance. One more technique is API selection, there are three types of API work on RDDDs, a data frame, a dataset that helps in code optimization. Advance variables which are of two types broadcast and the accumulator is also one of the techniques for code optimization. Some other techniques can be used for code optimization or cache and persist, by key operation, file format selection, garbage collection tuning, level of parallelism.