What is Spark and who is it for?
Spark is an open-source distributed computing platform designed for fast, in-memory data processing. It enables users to quickly analyze large datasets and make decisions based on the results. Spark can be used for a variety of tasks such as machine learning, streaming analytics, graph processing, and interactive SQL queries.
Apache Spark is a powerful open-source data processing engine. It provides many features, including:
•DataFrames and SQL support for structured data processing
•MLlib machine learning library with algorithms such as linear regression, k-means clustering, decision trees and more
•GraphX graph analytics library to analyze connected datasets
•Spark Streaming for real-time stream analysis of live data streams from sources like Kafka or Flume
•RDDs (Resilient Distributed Datasets) which can be used to store large amounts of distributed immutable objects in memory across multiple nodes in the cluster
•PySpark API that allows developers to write applications using Python language instead of Scala or Java
Spark is a software platform designed for data analytics, machine learning, and large-scale data processing. It can be used by developers, analysts, and data scientists to build powerful applications that process massive amounts of information quickly and efficiently.
What is Hadoop and who is it for?
Hadoop is an open-source software framework for distributed storage and processing of large data sets on computer clusters built from commodity hardware. It provides a reliable, fault tolerant platform to store and process massive amounts of data in parallel across multiple nodes. Hadoop can be used to analyze structured as well as unstructured data such as log files, images or videos.
Hadoop is an open-source software framework for distributed storage and processing of large data sets. It has a number of features, including:
1) Distributed Storage – HDFS (Hadoop Distributed File System): This feature allows you to store large amounts of data across multiple machines in a cluster.
2) Resource Management – YARN (Yet Another Resource Negotiator): YARN manages resources within the cluster and allocates them to applications as needed.
3) Fault Tolerance – MapReduce: MapReduce provides fault tolerance by replicating tasks on different nodes in case one node fails or experiences slow performance.
4) Data Processing – Hive & Pig Latin : These are two high-level languages used for querying and analyzing big datasets stored in HDFS using SQL like syntaxes .
5) Security – Sentry & Knox : These provide authentication, authorization, auditing capabilities with pluggable architecture that supports various security models such as Kerberos etc., so that only authorized users can access the system’s resources securely
Hadoop is an open-source software framework designed for distributed storage and processing of large datasets on clusters of commodity hardware. It is primarily used by organizations that need to store, process, and analyze huge amounts of data in a cost-effective manner. This includes companies from various industries such as finance, retail, healthcare, media & entertainment etc.
What are the benefits & downsides of Spark and what say users about it?
1. Fast performance – Spark is up to 100x faster than traditional Hadoop MapReduce for certain applications, making it a great choice for data processing and analytics tasks that require quick turnaround times.
2. Easy scalability – Spark can be easily scaled out across multiple nodes in a cluster, allowing you to process larger datasets more quickly and efficiently without having to invest in additional hardware resources or infrastructure changes.
3. High-level APIs – With its high-level APIs, developers can write code much more quickly using languages like Java, Python, Scala or R instead of writing complex MapReduce jobs from scratch every time they need to run an analysis job on their data sets. This makes the development process significantly easier and faster as well as less error prone due to fewer lines of code being written overall compared with other frameworks such as Hadoop Map Reduce which requires manual coding of mapreduce jobs each time one needs them executed on large datasets .
4. In memory computing – By storing intermediate results in memory rather than disk storage , spark offers significant speed advantages over traditional systems when running iterative algorithms (such as machine learning) by avoiding costly I/O operations between iterations .
Users generally have positive reviews of the software Spark. They praise its ease of use, powerful features, and scalability. Many users also appreciate that it is open source and free to use. Some even consider it one of the best big data processing tools available today.
What are the benefits & downsides of Hadoop and what say users about it?
– Hadoop is an open source framework, meaning it can be used without having to pay any licensing fees. This makes it a cost effective solution for companies looking to process large amounts of data.
– It allows you to store and analyze massive volumes of structured and unstructured data quickly. Its distributed computing model enables parallel processing across multiple nodes in a cluster, which helps speed up the analysis process significantly.
– With its scalability features, Hadoop can easily accommodate increasing amounts of data as your business grows over time with minimal effort on your part.
– The MapReduce programming model provides efficient fault tolerance capabilities that help protect against hardware or software failures during the analysis process by automatically restarting failed tasks from where they left off instead of starting them all over again from scratch.
– Setting up and managing a Hadoop system requires technical expertise so if you don’t have someone who knows how to use this technology then there could be some difficulty getting started with it initially (although many cloud providers offer managed solutions).
– As powerful as Hadoop is at analyzing big datasets, its performance tends to degrade when dealing with smaller
Users generally have positive things to say about Hadoop. Many users report that it is easy to use and provides a reliable platform for data storage, analysis, and processing. Additionally, many users appreciate the scalability of Hadoop as well as its ability to efficiently process large volumes of data in parallel across clusters of computers.
What are the differences between Spark and Hadoop and in which case should you use either of them?
Spark and Hadoop are both open-source software frameworks for distributed computing. However, there are some key differences between them.
Hadoop is a batch processing system that uses the MapReduce algorithm to process large data sets across clusters of computers in parallel. It is optimized for throughput rather than latency, meaning it can take longer to get results from queries but can handle larger amounts of data more efficiently than other systems.
Spark on the other hand is an engine designed specifically for real-time analytics and interactive applications such as machine learning or streaming analytics with low latency requirements (in seconds). It also provides APIs in Java, Python, R and Scala which makes it easier to use compared to Hadoop’s Java only API set up. Furthermore unlike Hadoop which stores all its data on disk while running computations against this stored information; Spark keeps most of its intermediate datasets in memory making it much faster when dealing with iterative algorithms like those used by machine learning programs
Spark should be chosen over Hadoop when you need to process large amounts of data quickly, as it is faster than Hadoop due to its in-memory processing capabilities. Additionally, Spark supports a wide range of applications such as machine learning and streaming analytics which are not supported by Hadoop.
Hadoop should be chosen over Spark when dealing with large volumes of data that require batch processing. Hadoop is better suited for this type of task due to its ability to store and process massive amounts of unstructured data in a distributed computing environment. On the other hand, Spark is more suitable for real-time analytics or iterative computations on smaller datasets.
Feature Overview Spark vs. Hadoop MapReduce
|Features||Apache Spark||Hadoop MapReduce|
|Easy to manage||Yes||Yes|
Description of features
Speed. Processor speed measures (in megahertz or gigahertz; MHz or GHz) the number of instructions per second the computer executes. The need for speed is most evident in schools that offer advanced computing classes including web design, animation, and graphic design.
Difficulty. The difficulty level is an adjustable setting that controls how challenging a game is to play. It may affect the aggression of computer-controlled enemies, the amount of health or lives the player has, time limits of objectives, and other variables that challenge the player. The difficulty level setting only applies to local or single-player games where the player does not compete against other players.
Easy to manage. Spark can perform batch, interactive Machine Learning, and Streaming all in the same cluster. As a result, it makes it a complete data analytics engine. Thus, there is no need to manage different components for each need. Installing Spark on a cluster will be enough to handle all the requirements. MapReduce only provides the batch engine. Hence, we are dependent on different engines. For example- Storm, Giraph, Impala, etc. for other requirements. So, it is exceedingly difficult to manage many components.
It can process real-time data i.e. data coming from the real-time event streams at the rate of millions of events per second, e.g. Twitter data for instance, or Facebook sharing/posting. Spark’s strength is the ability to process live streams efficiently. MapReduce fails when it comes to the real-time data processing as it was designed to perform batch processing on voluminous amounts of data.
Latency. Spark provides low-latency computing. MapReduce is a high-latency computing framework.
Interactive mode. Spark can process data interactively. MapReduce doesn’t have an interactive mode.
Streaming. Spark can process real-time data through Spark Streaming. With MapReduce, you can only process data in batch mode.
Recovery. RDDs allow recovery of partitions on failed nodes by re-computation of the DAG while also supporting a more similar recovery style to Hadoop by way of checkpointing, to reduce the dependencies of RDDs. MapReduce is naturally resilient to system faults or failures. So, it is a highly fault-tolerant system.
Scheduler. Due to in-memory computation spark acts as its flow scheduler. MapReduce needs an external job scheduler, for example, Oozie to schedule complex flows.
Fault Tolerance. As a result, there is no need to restart the application from scratch in case of any failure. Like Apache Spark, MapReduce is also fault-tolerant, so there is no need to restart the application from scratch in case of any failure.
Security. Spark is a little less secure in comparison to MapReduce because it supports only authentication through shared secret password authentication. Apache Hadoop MapReduce is more secure because of Kerberos and it also supports Access Control Lists (ACLs) which are a traditional file permission model.
Cost. As spark requires a lot of RAM to run in-memory. Thus, increases the cluster, and its cost. MapReduce is a cheaper option available when comparing it in terms of cost.
Language Developed. Spark is developed in Scala. Hadoop MapReduce is developed in Java.
Category. It is a data analytics engine. Hence, it is a choice for Data Scientists. It is a basic data processing engine.
License. Apache License 2.
Scalability. Spark is highly scalable. Thus, we can add n number of nodes in the cluster. Also, the largest known Spark Cluster is eight thousand nodes. MapReduce is also highly scalable. We can keep adding n number of nodes in the cluster. Also, the largest known Hadoop cluster is 14000 nodes.
Machine Learning. Scala, Java, Python, R, SQL. Primarily Java, other languages like C, C++, Ruby, Groovy, Perl, and Python are also supported using Hadoop streaming.
Caching. Spark can cache data in memory for further iterations. As a result, it enhances system performance. MapReduce cannot cache the data in memory for future requirements. So, the processing speed is not as high as that of Spark.