Application of Spark in Python Programming
Spark is an open source application that has gained popularity in the processing of big data. It is able to process large volumes of data significantly faster compared to the MapReduce app because data is present in the spark’s memory.
Apache spark provides high-level APIs for use in Python, Java, and Scala environments.
This open source program has built-in modules to stream SQL queries, machine learning and also process graphs.
It is characterized by its ease of use, processing speed, and its own ability to run in virtually in every machine.
Spark is used as data analytic tool to carry out data analysis, data extractions, supervised learning and also for carrying out module evaluation.
Learning Spark with Python is suitable for people with less programming experience. It is more reliable in terms of its learning curve and the ease of use compared to Scala open source program.
Spark development utilizes in-memory data structures and increases the processing speed of large volumes of data over the Hadoop environment.
Use of Resilient Distributed Datasets (RDD’s) in Sparks’ Framework
Spark uses RDD as a core data structure. RDD is distributed across the various machines memory. RDD object consists of elements used to hold tuples, data dictionaries, and lists. This requires you to load the dataset into the RDD framework and run any method accessed through the object.
The Spark Python API (PySpark) is commonly used in python programming. Spark is usually written in Scala language and it compiles down into bytecode for the JVM environment.
Through spark open source community, PySpark toolkit is developed. The PySpark acts as an interface between the RDD and the Python.
The current PySpark version does not support lookup and non-input text files found in the Scala framework.
With the Py4j library, Python can interface JVM objects to enable the performance of PySpark.
In python program development, the RDDs is used to support different objects types.
Features of Spark in Python development framework
- Swift processing speed
All the application in Hadoop cluster can run up to 100 times faster when stored in memory and also 10 times faster when running from a hard disk.
Spark open source application can support multiple languages. Due to its dynamic nature, you can easily Install the application in your machine and use it to write programs using Java, Python, Scala, and R programming languages.
- Compatibility with the Hadoop system
Spark is compatible with the Hadoop ecosystem and can also run independently.
- Uses in-memory computing
Due to its ability to access data faster, data is kept in the server’s RAM. In-memory data storage enables iterative machine learning algorithm since it reduces the round trips of data read and write operations from the disk. The sparks execution engine provides in-memory computation which results in high-speed data processing.
- An active, progressive and an open source community
Spark is an open source community developed by a wide set of developers. The framework has an active mailing state and uses JIRA to track components. It used as one of the most active components in the Apache repository environment.
- Provides real-time processing
Spark streaming provides a real-time data processing along with other frameworks. The sparks’ data streaming ability is easy, integrated and can withstand any faults.
- Analytic tool
Spark environment supports data analysis tools like SQL queries, machine learning, Streaming data, use of graph algorithms, and using MapReduce feature.
- Lazy evaluation
This is one of the outstanding features in the Spark development framework. It acts as a call by need or memorization. It works by waiting for instructions (through a call to action) before delivering the end results. This helps save a lot of time.
Advantages of using Spark in Python compared to Scala development frameworks
- Provides efficient interactive queries and iterative algorithms for data analysis.
- The resilient distributed datasets, RDD provides data abstraction and makes spark fault-tolerance.
- Consist of several in-built machine learning libraries.
- It provides spark streaming, a processing platform used for streaming data in real time.
- Provides a reliable and high-speed memory processing.
- Uses graphx libraries on top of a spark core to provide graphical data reporting
SparkContext is an object used to manage cluster connection and coordinate all processes running in the clusters. The cluster manager connected to SparkContext manage the actual program executors as shown below.
Essentials of Apache spark in Python
With large volumes of data streaming into the organization from multiple sources like the social media network and web content, companies should be able to stream and analyze data in real time. Spark streaming enables faster data processing using a single development framework to accommodate all the processing workload.
This Spark use case provides an integrated framework to carry out advanced analytics on large volumes of data. It enables the analyst to perform simultaneous queries on various data sets and come up with machine learning algorithms to analyze and predict data patterns.
MLlib component is used to carry out predictive intelligence to forecast future marketing and customer trends.
Observation and inspection of real-time data packets through machine learning can help trace any malicious activity in the big data processing.
Apache Spark uses analytic tools like MapReduce to handle any batch processing task within the organization. Other analytic tools like SQL-on-Hadoop engine provides an interactive analysis by allowing users to structure SQL queries on datasets.
Complex data sets can be analyzed using visualization tools combined with spark library component. Interactive queries can analyze live data boosting web analytics.
Using Spark in real-world applications
Online Marketers and companies like Netflix leverage on Spark analysis of big data to gain insights on the market trends.
Analyst and data engineers use Apache Spark community to stream data, use different machine learning library to get insights of data and analyze both structural and unstructured data including web traffic, and social media.