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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.

Apache Spark Vs Hadoop - Head to Head Comparison

17K
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

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 Common
  • Hadoop Distributed File System (HDFS)
  • Hadoop YARN
  • Hadoop MapReduce

Hadoop’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 Spark

Overview of Apache Spark

An 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 Hadoop

Let 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 nutshell

Apache Spark
Parameters
Hadoop
Processes everything in memoryPerformance-wiseHadoop MapReduce uses batch processing
Has user-friendly APIs for multiple programming languagesEase of UseHas add-ons such as Hive and Pig
Spark systems cost moreCostsHadoop MapReduce systems cost lesser
Shares every Hadoop MapReduce compatibilityCompatibilityCompliments Apache Spark seamlessly
Has GraphX, its own graph computation libraryData ProcessingHadoop MapReduce operates in sequential steps
Spark uses Resilient Distributed Datasets (RDDs)Fault ToleranceUtilises TaskTrackers to keep the JobTracker ticking
Comparatively lesser scalabilityScalabilityLarge Scalability
Provides authentication via shared secret (password authentication)SecuritySupports Kerberos authentication

Performance-wise

Spark 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 Use

Spark 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 Scala
  • Java
  • Python
  • Spark SQL.

Ease of Use

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.

Costs

Apache 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 Compatibility

Both 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.

Apache Spark and Apache Hadoop Compatibility

Data Processing

Hadoop MapReduce is a batch-processing engine. So how does it work? Well, it works in sequential steps.

Step 1: Reads data from the cluster

Step 2: Performs its operation on the data

Step 3: Writes the results back to the cluster

Step 4: Reads updated data from the cluster

Step 5: Performs the next data operation

Step 6: Writes those results back to the cluster

Step 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 cluster

Step 2: Performs its operation on the data

Step 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 Tolerance

There 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:

  1. A list of partitions
  2. A function for computing each split
  3. A list of dependencies on other RDDs
  4. A Partitioner for key-value RDDs by choice (provided that the RDD is hash-partitioned)
  5. 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.

Scalability

In 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.

Security

Kerberos 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 Vs Hadoop

Apache 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.

KnowledgeHut

KnowledgeHut

Author

KnowledgeHut is an outcome-focused global ed-tech company. We help organizations and professionals unlock excellence through skills development. We offer training solutions under the people and process, data science, full-stack development, cybersecurity, future technologies and digital transformation verticals.
Website : https://www.knowledgehut.com

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2 comments

Shaker 20 Aug 2019

great article nice to stay here on this website.Thanks for sharing this information with us

Jayadeep Sai Kunchay 20 Aug 2019

Great article. Thank you for sharing this useful information.

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Since the amount of Big Data increases exponentially- more than 500 terabytes of data are uploaded to Facebook alone, in a single day- it represents a real problem in terms of analysis. Before we jump into the article, let's have a visual introduction on what is Big data and its types. (Structured Data, Semi-Structured & Unstructured Data) Types of Big Data: Classification is essential for the study of any subject. So Big Data is widely classified into three main types, which are- Structured Unstructured Semi-structured 1. Structured data Structured Data is used to refer to the data which is already stored in databases, in an ordered manner. It accounts for about 20% of the total existing data and is used the most in programming and computer-related activities. There are two sources of structured data- machines and humans. All the data received from sensors, weblogs, and financial systems are classified under machine-generated data. These include medical devices, GPS data, data of usage statistics captured by servers and applications and the huge amount of data that usually move through trading platforms, to name a few. Human-generated structured data mainly includes all the data a human input into a computer, such as his name and other personal details. When a person clicks a link on the internet, or even makes a move in a game, data is created- this can be used by companies to figure out their customer behavior and make the appropriate decisions and modifications. Let’s understand Structured data with an example. Top 3 players who have scored most runs in international T20 matches are as follows: Player Country Scores No of Matches played                Brendon McCullum New Zealand                                 2140                                           71                    Rohit Sharma India     2237          90 Virat Kohli  India      2167          65 2. Unstructured data While structured data resides in the traditional row-column databases, unstructured data is the opposite- they have no clear format in storage. The rest of the data created, about 80% of the total account for unstructured big data. Most of the data a person encounters belong to this category- and until recently, there was not much to do to it except storing it or analyzing it manually. Unstructured data is also classified based on its source, into machine-generated or human-generated. Machine-generated data accounts for all the satellite images, the scientific data from various experiments and radar data captured by various facets of technology. Human-generated unstructured data is found in abundance across the internet since it includes social media data, mobile data, and website content. This means that the pictures we upload to Facebook or Instagram handle, the videos we watch on YouTube and even the text messages we send all contribute to the gigantic heap that is unstructured data. Examples of unstructured data include text, video, audio, mobile activity, social media activity, satellite imagery, surveillance imagery – the list goes on and on. The following image will clearly help you to understand what exactly Unstructured data is The Unstructured data is further divided into – Captured User-Generated data a. Captured data: It is the data based on the user’s behavior. The best example to understand it is GPS via smartphones which help the user each and every moment and provides a real-time output. b. User-generated data: It is the kind of unstructured data where the user itself will put data on the internet every movement. For example, Tweets and Re-tweets, Likes, Shares, Comments, on Youtube, Facebook, etc. 3. Semi-structured data: The line between unstructured data and semi-structured data has always been unclear since most of the semi-structured data appear to be unstructured at a glance. Information that is not in the traditional database format as structured data, but contains some organizational properties which make it easier to process, are included in semi-structured data. For example, NoSQL documents are considered to be semi-structured, since they contain keywords that can be used to process the document easily. Big Data analysis has been found to have definite business value, as its analysis and processing can help a company achieve cost reductions and dramatic growth. So it is imperative that you do not wait too long to exploit the potential of this excellent business opportunity. Diagram showing Semi-structured data Difference between Structured, Semi-structured and Unstructured data       Factors      Structured data       Semi-structured data     Unstructured data Flexibility It is dependent and less flexible It is more flexible than structured data but less than flexible than unstructured data It is flexible in nature and there is an absence of a schema Transaction Management Matured transaction and various concurrency technique The transaction is adapted from DBMS not matured No transaction management and no concurrency Query performance Structured query allow complex joining Queries over anonymous nodes are possible An only textual query is possible Technology It is based on the relational database table It is based on RDF and XML This is based on character and library data Big data is indeed a revolution in the field of IT. The use of Data analytics is increasing every year. In spite of the demand, organizations are currently short of experts. To minimize this talent gap many training institutes are offering courses on Big data analytics which helps you to upgrade skills set needed to manage and analyze big data. If you are keen to take up data analytics as a career then taking up Big data training will be an added advantage .
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Types Of Big Data

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