Found 10 Articles for Apache Spark

In today's time, when we have high volume and velocities of data flowing, Apache Spark, an open source big data processing framework, is a common choice as it allows parallel and distributed processing of data. Cleaning of such data is an important step and Apache Spark provides us with a variety of tools and methods for the cleaning of data. In this method, we are going to be seeing how to clean data with Apache Spark in Python and the steps to do so are as follows: Loading the data into a Spark DataFrame − The SparkSession.read method allows ... Read More

Introduction Big Data has become a buzzword in the technology industry over the past decade. With vast amounts of data being generated every second, it's essential to manage and process it efficiently. That’s where Hadoop and Spark come into play. Both are powerful big data processing frameworks that can handle large datasets at scale. Hadoop Overview History and Development Hadoop was created by Doug Cutting and Mike Cafarella in 2005 while they were working at Yahoo. The project was named after a toy elephant that belonged to Cutting's son. Initially designed to handle large amounts of unstructured data, Hadoop has ... Read More

Apache Spark is a complex computing system. It provides high-level APIs in programming languages namely Python, Scala, and Java. It is easy to write parallel jobs in Spark. It offers general and quicker processing of data. It is written in Scala and it is faster than others. It is used to process a large number of datasets. It is now the most prominently engaged Apache Project. Its key feature is in-memory complex computing that extends the speed of the data process. It possesses some main features that are Multiple Language Support, Platform-Independent, High Speed, Modern Analytics, and General Purpose. Now, ... Read More

In world of big data processing, Apache Storm and Apache Spark are two popular distributed computing systems that have gained traction in recent years. Both of these systems are designed to process massive amounts of data, but they have different strengths and weaknesses. In this article, we will do a side-by-side comparison of Apache Storm and Apache Spark and explore their similarities, differences, and use cases. What is Apache Storm? Apache Storm is an open-source distributed computing system that is used for real-time stream processing. It was developed by Nathan Marz and his team at BackType, which was later acquired ... Read More

PySpark is a data processing framework built on top of Apache Spark, which is widely used for large-scale data processing tasks. It provides an efficient way to work with big data; it has data processing capabilities. A PySpark dataFrame is a distributed collection of data organized into named columns. It is similar to a table in a relational database, with columns representing the features and rows representing the observations. A dataFrame can be created from various data sources, such as CSV, JSON, Parquet files, and existing RDDs (Resilient Distributed Datasets). However, sometimes it may be required to create an ... Read More

In era of digitalization, data has become most valuable asset for businesses. Organizations today generate an enormous amount of data on a daily basis. This data can be anything, from customer interactions to financial transactions, product information, and more. Managing and storing this massive amount of data requires a robust and efficient infrastructure, which is where big data servers come in. Big data servers are a type of server infrastructure designed to store, process and manage large volumes of data. In this article, we will delve deeper into what big data servers are, how they work, and some popular examples. ... Read More

Introduction Big Data is a term that has been making rounds in the world of technology and business for quite some time now. It refers to the massive volume of structured and unstructured data that is generated every day. With the rise of digitalization and the internet, the amount of data being generated has increased exponentially. This data, when analyzed correctly, can provide valuable insights that can help organizations make better decisions and improve their operations. In this article, we will delve into the characteristics of Big Data and the different types that exist. We will also provide real-life examples ... Read More

The full name of the RDD is a distributed database. Spark performance is based on this ambiguous set, enabling it to consistently cope with major data processing conditions, including MapReduce, streaming, SQL, machine learning, graphs, etc. Spark supports many programming languages, including Scala, Python, and R. RDD also supports the maintenance of material in these languages. How to create RDD Spark supports RDDS architecture in many areas, including local file systems, HDFS file systems, memory, and HBase. For the local file system, we can create RDD through the following way − val distFile = sc.textFile("file:///user/root/rddData.txt") By default, Spark takes ... Read More

Both MapReduce and Spark are examples of so-called frameworks because they make it possible to construct flagship products in the field of big data analytics. The Apache Software Foundation is responsible for maintaining these frameworks as open-source projects.MapReduce, also known as Hadoop MapReduce, is a framework that enables application writing, which in turn enables the processing of vast amounts of data on clusters in a distributed form while maintaining fault tolerance and reliability. The MapReduce model is constructed by separating the term "MapReduce" into its component parts, "Map, " which refers to the activity that must come first in the ... Read More

Let us understand the concepts of BigDL and Caffe before learning the differences between them.BigDLIt is a distributed deep learning framework for Apache Spark, launched by Jason Dai in the year 2016 at Intel. By using BigDL, users write deep learning applications as standard Spark programs that can directly run on top of existing Spark or Hadoop clusters.FeaturesThe features of BigDL are as follows −Rich deep learning supportEfficiently scale-outExtremely high performanceprovides plenty of deep learning modulesLayersOptimizationAdvantagesThe advantages of BigDL are as follows −SpeedEase of useDynamic natureMultilingualAdvanced analyticsDemand for spark developers.DisadvantagesThe disadvantages of BigDL are as follows −No automatic optimization processFile ... Read More