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Apache Spark Vs Apache Storm - Head To Head Comparison

In today’s world, the need for real-time data streaming is growing exponentially due to the increase in real-time data. With streaming technologies leading the world of Big Data, it might be tough for the users to choose the appropriate real-time streaming platform. Two of the most popular real-time technologies that might consider for opting are Apache Spark and Apache Storm. One major key difference between the frameworks Spark and Storm is that Spark performs Data-Parallel computations, whereas Storm occupies Task-Parallel computations. Read along to know more differences between Apache Spark and Apache Storm, and understand which one is better to adopt on the basis of different features. Comparison Table: Apache Spark Vs. Apache StormSr. NoParameterApache SparkApache Storm1.Processing  ModelBatch ProcessingMicro-batch processing2.Programming LanguageSupports lesser languages like Java, Scala.Support smultiple languages, such as Scala, Java, Clojure.3.Stream SourcesHDFSSpout4.MessagingAkka, NettyZeroMQ, Netty5.Resource ManagementYarn and Meson are responsible.Yarn and Mesos are responsible.6.Low LatencyHigher latency as compared to SparkBetter latency with lesser constraints7.Stream PrimitivesDStreamTuple, Partition8.Development CostSame code can be used for batch and stream processing.Same code cannot be used for batch and stream processing.9.State ManagementSupports State ManagementSupports State Management as well10.Message Delivery GuaranteesSupports one message processing mode: ‘at least once’.Supports three message processing mode: ‘at least once’, ‘at most once’, ‘exactly once’.11.Fault ToleranceIf a process fails, Spark restarts workers via resource managers. (YARN, Mesos)If a process fails, the supervisor process starts automatically.12.Throughput100k records per node per second10k records per node per second13.PersistenceMapStatePer RDD14.ProvisioningBasic monitoring using GangliaApache Ambaripache Spark: Apache Spark is a general-purpose, lighting fast, cluster-computing technology framework, used for fast computation on large-scale data processing. It can manage both batch and real-time analytics and data processing workloads.  Spark was developed at UC Berkeley in the year 2009. Apache Storm:Apache Storm is an open-source, scalable fault-tolerant, and real-time stream processing computation system. It is a framework for real-time distributed data processing, which focuses on stream processing or event processing. It can be used with any programming language and can be integrated using any queuing or database technology.  Apache Storm was developed by a team led by Nathan Marz at BackType Labs. Apache Spark Vs. Apache Storm1. Processing Model: Apache Storm supports micro-batch processing, while Apache Spark supports batch processing. 2. Programming Language:Storm applications can be created using multiple languages like Java, Scala and Clojure, while Spark applications can be created using Java and Scala.3. Stream Sources:For Storm, the source of stream processing is Spout, while that for Spark is HDFS. 4. Messaging:Storm uses ZeroMQ and Netty as its messaging layer while Spark is using a combination of Nettu and Akka for distributing the messages throughout the executors. 5. Resource Management:Yarn and Meson are responsible for resource management in Spark, while Yarn and Mesos are responsible for resource management in Storm. 6. Low Latency: Spark provides higher latency as compared to Apache Storm, whereas Storm can provide better latency with fewer restrictions.7. Stream Primitives:Spark provides with stream transforming operators which transform DStream into another, while Storm provides with various primitives which perform tuple level of processing at the stream level (functions, filters). 8. Development Cost:It is possible for Spark to use the same code base for both stream processing and batch processing. Whereas for Storm, the same code base cannot be used for both stream processing and batch processing.  9. State Management: The changing and maintaining state in Apache Spark can be updated via UpdateStateByKey, but no pluggable strategy can be applied in the external system for the implementation of state. Whereas Storm does not provide any framework for the storage of any intervening bolt output as a state. Hence, each application has to create a state for itself whenever required. 10. Message Delivery Guarantees (Handling the message level failures):Apache Spark supports only one message processing mode, viz, ‘at least once’, whereas Storm supports three message processing modes, viz, ‘at least once’ (Tuples are processed at least one time, but can be processed more than once), ‘at most once’  and ‘exactly once’ (T^uples are processed at least once). Storm’s reliability mechanisms are scalable, distributed and fault-tolerant. 11. Fault-Tolerant:Apache Spark and Apache Storm, both are fault tolerant to nearly the same extent. If a process fails in Apache Storm, then the supervisor process will restart it automatically, as the state management is managed by Zookeeper, while Spark restarts its workers with the help of resource managers, who may be Mesos, YARN or its separate manager.12. Ease of Development: In the case of Storm, there are effective and easy to use APIs which show that the nature of topology is DAG. The Storm tuples are written dynamically. In the case of Spark, it consists of Java and Scala APIs with practical programming, making topology code a bit difficult to understand. But since the API documentation and samples are easily available for the developers, it is now easier. Summing Up: Apache Spark Vs Apache StormApache Storm and Apache Spark both offer great solutions to solve the transformation problems and streaming ingestions. Moreover, both can be a part of a Hadoop cluster to process data. While Storm acts as a solution for real-time stream processing, developers might find it to be quite complex to develop applications due to its limited resources. The industry is always on a lookout for a generalized solution, which has the ability to solve all types of problems, such as Batch processing, interactive processing, iterative processing and stream processing. Keeping all these points in mind, this is where Apache Spark steals the limelight as it is mostly considered as a general-purpose computation engine, making it a highly demanding tool by IT professionals. It can handle various types of problems and provides a flexible environment to in. Moreover, developers find it to be easy and are able to integrate it well with Hadoop. 
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Apache Spark Vs Apache Storm - Head To Head Comparison

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Apache Spark Vs Apache Storm - Head To Head Comparison

In today’s world, the need for real-time data streaming is growing exponentially due to the increase in real-time data. With streaming technologies leading the world of Big Data, it might be tough for the users to choose the appropriate real-time streaming platform. Two of the most popular real-time technologies that might consider for opting are Apache Spark and Apache Storm. 

One major key difference between the frameworks Spark and Storm is that Spark performs Data-Parallel computations, whereas Storm occupies Task-Parallel computations. Read along to know more differences between Apache Spark and Apache Storm, and understand which one is better to adopt on the basis of different features. 

Comparison Table: Apache Spark Vs. Apache Storm

Sr. NoParameterApache SparkApache Storm
1.Processing  ModelBatch ProcessingMicro-batch processing
2.Programming LanguageSupports lesser languages like Java, Scala.Support smultiple languages, such as Scala, Java, Clojure.
3.Stream SourcesHDFSSpout
4.MessagingAkka, NettyZeroMQ, Netty
5.Resource ManagementYarn and Meson are responsible.Yarn and Mesos are responsible.
6.Low LatencyHigher latency as compared to SparkBetter latency with lesser constraints
7.Stream PrimitivesDStreamTuple, Partition
8.Development CostSame code can be used for batch and stream processing.Same code cannot be used for batch and stream processing.
9.State ManagementSupports State ManagementSupports State Management as well
10.Message Delivery GuaranteesSupports one message processing mode: ‘at least once’.Supports three message processing mode: ‘at least once’, ‘at most once’, ‘exactly once’.
11.Fault ToleranceIf a process fails, Spark restarts workers via resource managers. (YARN, Mesos)If a process fails, the supervisor process starts automatically.
12.Throughput100k records per node per second10k records per node per second
13.PersistenceMapStatePer RDD
14.ProvisioningBasic monitoring using GangliaApache Ambari

pache Spark: 

Apache Spark is a general-purpose, lighting fast, cluster-computing technology framework, used for fast computation on large-scale data processing. It can manage both batch and real-time analytics and data processing workloads.  Spark was developed at UC Berkeley in the year 2009. 

Apache Storm:

Apache Storm is an open-source, scalable fault-tolerant, and real-time stream processing computation system. It is a framework for real-time distributed data processing, which focuses on stream processing or event processing. It can be used with any programming language and can be integrated using any queuing or database technology.  Apache Storm was developed by a team led by Nathan Marz at BackType Labs. 

Apache Spark Vs. Apache Storm

1. Processing Model: 

Apache Storm supports micro-batch processing, while Apache Spark supports batch processing. 

2. Programming Language:

Storm applications can be created using multiple languages like Java, Scala and Clojure, while Spark applications can be created using Java and Scala.

3. Stream Sources:

For Storm, the source of stream processing is Spout, while that for Spark is HDFS. 

4. Messaging:

Storm uses ZeroMQ and Netty as its messaging layer while Spark is using a combination of Nettu and Akka for distributing the messages throughout the executors. 

5. Resource Management:

Yarn and Meson are responsible for resource management in Spark, while Yarn and Mesos are responsible for resource management in Storm. 

6. Low Latency: 

Spark provides higher latency as compared to Apache Storm, whereas Storm can provide better latency with fewer restrictions.

7. Stream Primitives:

Spark provides with stream transforming operators which transform DStream into another, while Storm provides with various primitives which perform tuple level of processing at the stream level (functions, filters). 

8. Development Cost:

It is possible for Spark to use the same code base for both stream processing and batch processing. Whereas for Storm, the same code base cannot be used for both stream processing and batch processing.  

9. State Management: 

The changing and maintaining state in Apache Spark can be updated via UpdateStateByKey, but no pluggable strategy can be applied in the external system for the implementation of state. Whereas Storm does not provide any framework for the storage of any intervening bolt output as a state. Hence, each application has to create a state for itself whenever required. 

10. Message Delivery Guarantees (Handling the message level failures):

Apache Spark supports only one message processing mode, viz, ‘at least once’, whereas Storm supports three message processing modes, viz, ‘at least once’ (Tuples are processed at least one time, but can be processed more than once), ‘at most once’  and ‘exactly once’ (T^uples are processed at least once). Storm’s reliability mechanisms are scalable, distributed and fault-tolerant. 

11. Fault-Tolerant:

Apache Spark and Apache Storm, both are fault tolerant to nearly the same extent. If a process fails in Apache Storm, then the supervisor process will restart it automatically, as the state management is managed by Zookeeper, while Spark restarts its workers with the help of resource managers, who may be Mesos, YARN or its separate manager.

12. Ease of Development: 

In the case of Storm, there are effective and easy to use APIs which show that the nature of topology is DAG. The Storm tuples are written dynamically. In the case of Spark, it consists of Java and Scala APIs with practical programming, making topology code a bit difficult to understand. But since the API documentation and samples are easily available for the developers, it is now easier. 

Summing Up: Apache Spark Vs Apache Storm

Apache Storm and Apache Spark both offer great solutions to solve the transformation problems and streaming ingestions. Moreover, both can be a part of a Hadoop cluster to process data. While Storm acts as a solution for real-time stream processing, developers might find it to be quite complex to develop applications due to its limited resources. 

The industry is always on a lookout for a generalized solution, which has the ability to solve all types of problems, such as Batch processing, interactive processing, iterative processing and stream processing. Keeping all these points in mind, this is where Apache Spark steals the limelight as it is mostly considered as a general-purpose computation engine, making it a highly demanding tool by IT professionals. It can handle various types of problems and provides a flexible environment to in. Moreover, developers find it to be easy and are able to integrate it well with Hadoop. 

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2 comments

priyanka 05 Aug 2019

The comparison between the apache spark Vs. apache storm is very clear loved it thanks for the article...

Vihaan 16 Aug 2019

This is one of the best article I have ever seen. Crystal clear. Thanks for this post.

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Apache Kafka Vs Apache Spark: Know the Differences

A new breed of ‘Fast Data’ architectures has evolved to be stream-oriented, where data is processed as it arrives, providing businesses with a competitive advantage. - Dean Wampler (Renowned author of many big data technology-related books)Dean Wampler makes an important point in one of his webinars. The demand for stream processing is increasing every day in today’s era. The main reason behind it is, processing only volumes of data is not sufficient but processing data at faster rates and making insights out of it in real time is very essential so that organization can react to changing business conditions in real time.And hence, there is a need to understand the concept “stream processing “and technology behind it. So, what is Stream Processing?Think of streaming as an unbounded, continuous real-time flow of records and processing these records in similar timeframe is stream processing. AWS (Amazon Web Services) defines “Streaming Data” is data that is generated continuously by thousands of data sources, which typically send in the data records simultaneously, and in small sizes (order of Kilobytes). This data needs to be processed sequentially and incrementally on a record-by-record basis or over sliding time windows and used for a wide variety of analytics including correlations, aggregations, filtering, and sampling.In stream processing method, continuous computation happens as the data flows through the system.Stream processing is highly beneficial if the events you wish to track are happening frequently and close together in time. It is also best to utilize if the event needs to be detected right away and responded to quickly.There is a subtle difference between stream processing, real-time processing (Rear real-time) and complex event processing (CEP). Let’s quickly look at the examples to understand the difference. Stream Processing: Stream processing is useful for tasks like fraud detection and cybersecurity. If transaction data is stream-processed, fraudulent transactions can be identified and stopped before they are even complete.Real-time Processing: If event time is very relevant and latencies in the second's range are completely unacceptable then it’s called Real-time (Rear real-time) processing. For ex. flight control system for space programsComplex Event Processing (CEP): CEP utilizes event-by-event processing and aggregation (for example, on potentially out-of-order events from a variety of sources, often with large numbers of rules or business logic).We have multiple tools available to accomplish above-mentioned Stream, Realtime or Complex event Processing. Spark Streaming, Kafka Stream, Flink, Storm, Akka, Structured streaming are to name a few. We will try to understand Spark streaming and Kafka stream in depth further in this article. As historically, these are occupying significant market share. Apache Kafka Stream: Kafka is actually a message broker with a really good performance so that all your data can flow through it before being redistributed to applications. Kafka works as a data pipeline.Typically, Kafka Stream supports per-second stream processing with millisecond latency.  Kafka Streams is a client library for processing and analyzing data stored in Kafka. Kafka streams can process data in 2 ways. Kafka -> Kafka: When Kafka Streams performs aggregations, filtering etc. and writes back the data to Kafka, it achieves amazing scalability, high availability, high throughput etc.  if configured correctly. It also does not do mini batching, which is “real streaming”.Kafka -> External Systems (‘Kafka -> Database’ or ‘Kafka -> Data science model’): Typically, any streaming library (Spark, Flink, NiFi etc) uses Kafka for a message broker. It would read the messages from Kafka and then break it into mini time windows to process it further. Representative view of Kafka streaming: Note:Sources here could be event logs, webpage events etc. etc. DB/Models would be accessed via any other streaming application, which in turn is using Kafka streams here. Kafka Streams is built upon important stream processing concepts such as properly distinguishing between event time and processing time, windowing support, and simple (yet efficient) management of application state. It is based on many concepts already contained in Kafka, such as scaling by partitioning.Also, for this reason, it comes as a lightweight library that can be integrated into an application.The application can then be operated as desired, as mentioned below: Standalone, in an application serverAs a Docker container, or Directly, via a resource manager such as Mesos.Why one will love using dedicated Apache Kafka Streams?Elastic, highly scalable, fault-tolerantDeploy to containers, VMs, bare metal, cloudEqually viable for small, medium, & large use casesFully integrated with Kafka securityWrite standard Java and Scala applicationsExactly-once processing semanticsNo separate processing cluster requiredDevelop on Mac, Linux, WindowsApache Spark Streaming:Spark Streaming receives live input data streams, it collects data for some time, builds RDD, divides the data into micro-batches, which are then processed by the Spark engine to generate the final stream of results in micro-batches. Following data flow diagram explains the working of Spark streaming. Spark Streaming provides a high-level abstraction called discretized stream or DStream, which represents a continuous stream of data. DStreams can be created either from input data streams from sources such as Kafka, Flume, and Kinesis, or by applying high-level operations on other DStreams. Internally, a DStream is represented as a sequence of RDDs. Think about RDD as the underlying concept for distributing data over a cluster of computers. Why one will love using Apache Spark Streaming?It makes it very easy for developers to use a single framework to satisfy all the processing needs. They can use MLib (Spark's machine learning library) to train models offline and directly use them online for scoring live data in Spark Streaming. In fact, some models perform continuous, online learning, and scoring.Not all real-life use-cases need data to be processed at real real-time, few seconds delay is tolerated over having a unified framework like Spark Streaming and volumes of data processing. It provides a range of capabilities by integrating with other spark tools to do a variety of data processing.  Spark Streaming Vs Kafka StreamNow that we have understood high level what these tools mean, it’s obvious to have curiosity around differences between both the tools. Following table briefly explain you, key differences between the two. Sr.NoSpark streamingKafka Streams1Data received form live input data streams is Divided into Micro-batched for processing.processes per data stream(real real-time)2Separated processing Cluster is requriedNo separated processing cluster is requried.3Needs re-configuration for Scaling Scales easily by just adding java processes, No reconfiguration requried.4At least one semanticsExactly one semantics5Spark streaming is better at processing group of rows(groups,by,ml,window functions etc.)Kafka streams provides true a-record-at-a-time processing capabilities. it's better for functions like rows parsing, data cleansing etc.6Spark streaming is standalone framework.Kafka stream can be used as part of microservice,as it's just a library.Kafka streams Use-cases:Following are a couple of many industry Use cases where Kafka stream is being used: The New York Times: The New York Times uses Apache Kafka and Kafka Streams to store and distribute, in real-time, published content to the various applications and systems that make it available to the readers.Pinterest: Pinterest uses Apache Kafka and the Kafka Streams at large scale to power the real-time, predictive budgeting system of their advertising infrastructure. With Kafka Streams, spend predictions are more accurate than ever.Zalando: As the leading online fashion retailer in Europe, Zalando uses Kafka as an ESB (Enterprise Service Bus), which helps us in transitioning from a monolithic to a micro services architecture. Using Kafka for processing event streams enables our technical team to do near-real time business intelligence.Trivago: Trivago is a global hotel search platform. We are focused on reshaping the way travellers search for and compare hotels while enabling hotel advertisers to grow their businesses by providing access to a broad audience of travellers via our websites and apps. As of 2017, we offer access to approximately 1.8 million hotels and other accommodations in over 190 countries. We use Kafka, Kafka Connect, and Kafka Streams to enable our developers to access data freely in the company. 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It also enables them to share ad metrics with advertisers in a timelier fashion.Spark Streaming’s ever-growing user base consists of household names like Uber, Netflix, and Pinterest.Broadly, spark streaming is suitable for requirements with batch processing for massive datasets, for bulk processing and have use-cases more than just data streaming. Dean Wampler explains factors to evaluation for tool basis Use-cases beautifully, as mentioned below: Sr.NoEvaluation CharacteristicResponse Time windowTypical Use Case Requirement1.Latency tolerancePico to Microseconds (Real Real time)Flight control system for space programs etc.Latency tolerance< 100 MicrosecondsRegular stock trading market transactions, Medical diagnostic equipment outputLatency tolerance< 10 millisecondsCredit cards verification window when consumer buy stuff onlineLatency tolerance< 100 millisecondshuman attention required Dashboards, Machine learning modelsLatency tolerance< 1 second to minutesMachine learning model trainingLatency tolerance1 minute and abovePeriodic short jobs(typical ETL applications)2.Evaluation CharacteristicTransaction/events frequencyTypical Use Case RequirementVelocity1M per secondNest Thermostat, Big spikes during specific time period.3Evaluation CharacteristicTypes of data processingNAData Processing Requirement1. 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Apache Spark Vs Hadoop - Head to Head Comparison

Over the past few years, data science has been one of the most sought-after multidisciplinary fields in the world today. It has established itself as an essential component of numerous industries such as marketing optimisation, risk management, marketing analytics. fraud detection, agriculture, etc. Understandably, this has lead to increasing demand for resorting to different approaches to data.When we talk about Apache Spark and Hadoop, it is really difficult to compare them with each other. We should be aware that both possess important features in the world of data science and big data. Hadoop excels over Apache Spark in some business applications, but when processing speed and ease of use is taken into account, Apache Spark has its own advantages that make it unique. The most important thing to note is, neither of these two can replace each other. However, since they are compatible with each other, they can be used together to produce very effective results for many big data applications.To analyse how important these two platforms are, there is a set of parameters with which we can discuss their efficiencies such as performance, ease of use, cost, data processing, compatibility, fault tolerance, scalability, and security. In this article, we will talk about Apache Spark and Hadoop individually for a bit, followed by stressing these parameters to better understand their significance in data science and big data.What is Hadoop?Hadoop, also known as Apache Hadoop, is a project formed by Apache.org that includes a software library and a framework that enables the usage of simple programming models to distributed processing of large data sets (big data) across computer clusters. Hadoop is quite efficient in scaling up from single computer systems to a lot of commodity system, offering substantial local storage. Due to this, Hadoop is considered as an omnipresent heavyweight in the big data analytics space. There are modules that work together to form the Hadoop framework. Here are the main Hadoop framework modules:Hadoop CommonHadoop Distributed File System (HDFS)Hadoop YARNHadoop MapReduceHadoop’s core is based on the above four modules followed by many others like Ambari, Avro, Cassandra, Hive, Pig, Oozie, Flume, and Sqoop. These are responsible for improving and extending Hadoop’s power to big data applications and large data set processing.Hadoop is utilised by numerous companies using big data sets and analytics and is the de facto model for big data applications. Initially, it was designed to take care of crawling and searching billions of web pages and collecting their information into a database, This resulted in Hadoop Distributed File System (HDFS), a distributed file system designed to run on commodity hardware and Hadoop MapReduce, a processing technique and a program model for distributed computing based on java.Hadoop comes handy when companies find data sets too large and complex to not being able to process the information in reasonably sufficient time. Since crawling and searching the web are text-based tasks, Hadoop MapReduce comes in handy as it is an exceptional text processing engine.An Overview of Apache SparkAn open-source distributed general-purpose cluster-computing framework, Apache Spark is considered as a fast and general engine for large-scale data processing. Compared to heavyweight Hadoop’s Big Data framework, Spark is very lightweight and faster by nearly 100 times. Although the facts say so, in fact, Spark runs up to 10 times faster on disk. Apart from that, it can perform batch processing but it really is good at streaming workloads, interactive queries, and machine-based learning.✓Streaming workloads✓Interactive queries✓Machine-based learning.Spark engine’s real-time data processing capability has a clear edge over Hadoop MapReduce’s disk-bound, batch processing one. Not only is Spark compatible with Hadoop and its modules, but it is also listed as a module on Hadoop’s project page. And because Spark can run in Hadoop clusters through YARN (Yet Another Resource Negotiator), it has its own page and a standalone mode. It can run as a Hadoop module and as a standalone solution which makes it difficult to make direct comparisons.Despite these facts, Spark is expected to diverge and might even replace Hadoop, especially in terms of faster access to processed data. Spark’s cluster computing feature enables it to compete with only Hadoop MapReduce and not the entire Hadoop ecosystem. That is why it can use HDFS despite not having its own distributed file system. To be concise, Hadoop MapReduce uses persistent storage whereas Spark uses Resilient Distributed Datasets (RDDs). What is RDD? This will be stressed in the Fault Tolerance section.The differences between Apache Spark and HadoopLet us have a look at the parameters using which we can compare the features of Apache Spark with Hadoop.Apache Spark vs Hadoop in a nutshellApache SparkParametersHadoopProcesses everything in memoryPerformance-wiseHadoop MapReduce uses batch processingHas user-friendly APIs for multiple programming languagesEase of UseHas add-ons such as Hive and PigSpark systems cost moreCostsHadoop MapReduce systems cost lesserShares every Hadoop MapReduce compatibilityCompatibilityCompliments Apache Spark seamlesslyHas GraphX, its own graph computation libraryData ProcessingHadoop MapReduce operates in sequential stepsSpark uses Resilient Distributed Datasets (RDDs)Fault ToleranceUtilises TaskTrackers to keep the JobTracker tickingComparatively lesser scalabilityScalabilityLarge ScalabilityProvides authentication via shared secret (password authentication)SecuritySupports Kerberos authenticationPerformance-wiseSpark is definitely faster when compared to Hadoop MapReduce. However, they cannot be compared because they perform processing in different styles. Spark is way faster because it processes everything in memory, even using disk for data that does not all fit into memory. The in-memory processing of Spark performs near real-time analytics for data from machine learning, log monitoring, marketing campaigns, Internet of Things sensors, security analytics, and social media sites. Hadoop MapReduce, on the other hand, utilises the batch-processing method so it understandably was never created for mesmerising speed. As a matter of fact, it was initially created to continuously gather information from websites during the times when data in or near real-time were not required.Ease of UseSpark does not only have a good reputation for its excellent performance, but it is also relatively easy to use along with providing additional support for languages like user-friendly APIs for Scala, Java, Python, and Spark SQL. Since Spark SQL is quite comparable to SQL 92, the user requires no additional knowledge to use it.Supported Languages:APIs for ScalaJavaPythonSpark SQL.Additionally, Spark is armed with an interactive mode to allow developers and users get instant feedback for questions and other actions. Hadoop MapReduce makes up for the lack of any interactive mode with add-ons like Hive and Pig, thus easing the workflow of Hadoop MapReduce.CostsApache Spark and Apache Hadoop MapReduce are both free open-source software.However, because Hadoop MapReduce’s processing is disk-based, it utilises standard volumes of memory. This results in companies buying faster disks with a lot of disk space to run Hadoop MapReduce. In stark contrast to this, Spark requires a lot of memory but compensates by settling with a standard amount of disk space running at standard speeds.Apache Spark and Apache Hadoop CompatibilityBoth Spark and Hadoop MapReduce are compatible with each other. Moreover, Spark shares every Hadoop MapReduce compatibility for data sources, file formats, and business intelligence tools via JDBC and ODBC.Data ProcessingHadoop MapReduce is a batch-processing engine. So how does it work? Well, it works in sequential steps.Step 1: Reads data from the clusterStep 2: Performs its operation on the dataStep 3: Writes the results back to the clusterStep 4: Reads updated data from the clusterStep 5: Performs the next data operationStep 6: Writes those results back to the clusterStep 7: Repeat.Spark performs in a similar manner, but the process doesn’t go on. It includes a single step and then to memory.Step 1: Reads data from the clusterStep 2: Performs its operation on the dataStep 3: Writes it back to the cluster.Moreover, Spark has GraphX, its own graph computation library. GraphX presents the same data as graphs and collections. Users have the option to use Resilient Distributed Datasets (RDDs) to transform and join graphs. This will be further addressed below in the Fault Tolerance section.Fault ToleranceThere are two different ways in which Hadoop MapReduce and Spark resolve the fault tolerance issue. Hadoop MapReduce utilises nodes like TaskTrackers to keep the JobTracker ticking. On the process being interrupted, the JobTracker reassigns every pending and in-progress operation to another TaskTracker. Although this process effectively provides fault tolerance, the completion times might get majorly affected even for operations having just a single failure.Spark, in this case, applies Resilient Distributed Datasets (RDDs), fault-tolerant collections of elements that can be operated side by side. References can be provided by RDDs in the form of datasets in an external storage system like shared filesystems, HDFS, HBase, or whatever available data source. This results in allowing a Hadoop InputFormat and Spark can create RDDs from every storage source that is backed by Hadoop. That covers local filesystems or one of those listed earlier.Below-mentioned is five main properties that an RDD possesses:A list of partitionsA function for computing each splitA list of dependencies on other RDDsA Partitioner for key-value RDDs by choice (provided that the RDD is hash-partitioned)Optionally, a list of preferred locations to compute each split on (e.g. block locations for an HDFS file)The persistence of RDDs to cache a dataset in memory across operations enables the speeding up of future actions by possibly ten folds. The cache of Spark is fault-tolerant, it will recomputed automatically by making use of the original transformations provided any partition of an RDD is lost.ScalabilityIn terms of scaling up, both Hadoop MapReduce and Spark are on equal terms in using the HDFS. Reports say that Yahoo holds a 42,000 node Hadoop cluster with no bounds while the most comprehensive Spark cluster holds 8,000 nodes. However, in order to support output expectations, the cluster sizes are expected to grow along with that of big data.SecurityKerberos authentication, considered to be quite hectic to manage is supported by Hadoop. Nevertheless, companies have been assisted by third-party vendors to leverage Active Directory Kerberos and LDAP for authentication and also allow data encrypt for in-flight and data at rest. Access control lists (ACLs) a traditional file permissions model are supported by Hadoop while it provides Service Level Authorization for user control in job submission, resulting in clients having the right permissions without any fail.For Spark though, it presently offers somewhat inadequate security as it provides authentication via shared secret (password authentication). However, if the user runs Spark on HDFS, then it can utilise HDFS ACLs and file-level permissions. Moreover, running Spark on YARN will enable the latter to have the capacity of using Kerberos authentication. That is the security takeaway from using Spark.  ConclusionApache Spark and Apache Hadoop form the perfect combination for business applications. Where Hadoop MapReduce has been a revelation in the big data market for businesses requiring huge datasets to be brought under control by commodity systems, Apache Spark’s speed and comparative ease of use compliments the low-cost operation involving Hadoop MapReduce.Like we discussed at the beginning of this article that neither of these two can replace one another, Spark and Hadoop form a lethal and effective symbiotic partnership. While Hadoop has features like a distributed file system that Spark does not have, the latter presents real-time, in-memory processing for the required data sets. Both Hadoop and Spark form the perfect combination for the ideal big data scenario. Rest assured, in this situation, both working in the same team is what goes in favour of big data professionals.You would be interested to know that Knowledgehut offers world-class training for Apache Spark and Hadoop. Feel free to check these courses to enhance your knowledge about both Apache Spark and Hadoop.
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Apache Spark Vs Hadoop - Head to Head Comparison

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Best ways to learn Apache Spark

If you ask any industry expert what language should you learn for Big Data? You will get an obvious reply to learn Apache Spark. Apache Spark is widely considered as the future of the Big Data industry. Since Apache Spark has stepped into Big data market, it has gained a lot of recognition for itself. Today, most of the cutting-edge companies like Apple, Facebook, Netflix, and Uber, etc. have deployed Spark at massive scale. In this blog post, we will understand why one should learn Apache Spark? And several ways to learn it. Apache Spark is a powerful open-source framework for the processing of large datasets. It is the most successful projects in the Apache software foundation. Apache Spark basically designed for fast computation, also which runs faster than Hadoop. Apache Spark can collectively process huge amount of data present in clusters over multiple nodes. The main feature of Apache Spark is its in-memory cluster computing that increases the processing speed of an application.Why You Should Learn Apache SparkApache Spark has become the most popular unified analytics engine for Big Data and Machine Learning. Enterprises are widely utilizing Spark which in turn is increasing demand for Apache Spark developers. Apache Spark developers are the ones earning the highest salary. IT professionals can leverage this upcoming skill set gap by pursuing a certification in Apache Spark. A developer with expertise in Apache Spark skills can earn an average salary of $78K as per Payscale. It is the right time for you to learn Apache Spark as there is a very high demand for Spark developers chances of getting a job is high.Here are the reasons why you should learn Apache Spark today:In order to go with the growing demand for Apache SparkTo fulfill the demands for Spark developersTo get benefits of existing big data investmentsResources to learn ReactTo learn Spark, you can refer to Spark’s website. There are multiple resources you will find to learn Apache Spark, from books, blogs, online videos, courses, tutorials, etc. With these multiple resources available today, you might be in the dilemma of choosing the best resource, especially in this fast-paced and swiftly evolving industry.BooksCertificationsVideosTutorials, Blogs, and TalksHands-on Exercises 1. BooksWhen was the last time you read a book? Do you have reading habits? If not, it’s the time to read the books. Reading has a significant number of benefits. Those aren’t fans of books might miss out the importance of Apache Spark. To learn Apache Spark, you can skim through the best Apache Spark books given below.Apache Spark in 24 hours is a perfect book for beginners which comprises 592 pages covering various topics. An excellent book to learn in a very short span of time. Apart from this, there are also books which will help you master.Here is the list of top books to learn Apache Spark:Learning Spark by Matei Zaharia, Patrick Wendell, Andy Konwinski, Holden KarauAdvanced Analytics with Spark by Sandy Ryza, Uri Laserson, Sean Owen and Josh WillsMastering Apache Spark by Mike FramptonSpark: The Definitive Guide – Big Data Processing Made SimpleSpark GraphX in ActionBig Data Analytics with SparkThese are the various Apache Spark books meant for you to learn. These books include for beginners and others for the advanced level professionals.2. Apache Spark Training and CertificationsOne more way to learn Apache Spark is through taking up training. Apache Spark Training will boost your knowledge and also help you learn from experience. You will be certified once you are done with training. Getting this certification will help you stand out of the crowd. You will also gain hands-on skills and knowledge in developing Spark applications through industry-based real-time projects.3. Videos:Videos are really good resources to help you learn Apache Spark. Following are the few videos will help you understand Apache Spark.Overview of SparkIntro to Spark - Brian ClapperAdvanced Spark Analytics - Sameer FarooquiSpark Summit VideosVideos from Spark Summit 2014, San Francisco, June 30 - July 2, 2013Full agenda with links to all videos and slidesTraining videos and slidesVideos from Spark Summit 2013, San Francisco, Dec 2-3-2013Full agenda with links to all videos and slidesYouTube playist of all KeynotesYouTube playist of Track A (Spark Applications)YouTube playist of Track B (Spark Deployment, Scheduling & Perf, Related projects)YouTube playist of the Training Day (i.e. the 2nd day of the summit)You can learn more on Apache Spark YouTube Channel for videos from Spark events. 4. Tutorials, Blogs, and TalksUsing Parquet and Scrooge with Spark — Scala-friendly Parquet and Avro usage tutorial from Ooyala's Evan ChanUsing Spark with MongoDB — by Sampo Niskanen from WellmoSpark Summit 2013 — contained 30 talks about Spark use cases, available as slides and videosA Powerful Big Data Trio: Spark, Parquet and Avro — Using Parquet in Spark by Matt MassieReal-time Analytics with Cassandra, Spark, and Shark — Presentation by Evan Chan from Ooyala at 2013 Cassandra SummitRun Spark and Shark on Amazon Elastic MapReduce — Article by Amazon Elastic MapReduce team member Parviz DeyhimSpark, an alternative for fast data analytics — IBM Developer Works article by M. Tim Jones 5. Hands-on ExercisesHands-on exercises from Spark Summit 2014 - These exercises will guide you to install Spark on your laptop and learn basic concepts.Hands-on exercises from Spark Summit 2013 - These exercises will help you launch a small EC2 cluster, load a dataset, and query it with Spark, Spark Streaming, and MLlib.So these were the best resources to learn Apache Spark. Hope you found what you were looking for. Wish you a Happy Learning!
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Best ways to learn Apache Spark

If you ask any industry expert what language shoul... Read More