Spark vs Hadoop: Which is the Best Big Data Framework?

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Data Engineering

The exponential rate of growth of data must be followed up with tools that meet the expanded need for data analytics. It has been reported that the global volume of data doubles every two years. Due to the exponential growth of data, Big Data, just like AI, is one of the major tech trends.

In gaining insight from the large volume of data accumulated, organizations apply technologies like Big Data. The decision to upgrade to Big Data from other data analytics tools has been natural for a lot of organizations. Deciding on the right big data tool has been a hard decision.

As giants in Big Data, Apache Hadoop and Spark are two options commonly considered when organizations seek the best Big Data tools. It noteworthy that some of the biggest names rely on both these tools. Facebook, LinkedIn, Hulu and Spotify are some of the firms that rely on Hadoop. Spark is used by firms such as Shopify, Amazon, and Alibaba.

These two tools are placed side by side and compared in this article. Both tools are first introduced to offer a proper background before the features are compared.

Apache Hadoop

Hadoop is designed to allow the storage and processing of Big Data within a distributed environment. Hadoop is an open-source framework with two components, HDFS and YARN, based on Java.

HDFS, which has a master daemon and slave daemons, is the component of Hadoop that stores Big Data. YARN, on the other hand, is the component that is involved in all the processing that can occur with Hadoop. YARN also has a master daemon and slave daemons.

Apache Spark

Apache Spark is designed to achieve real-time data analytics within a distributed environment. Spark has a Resilient, Distributed Dataset Structure, which improves its speed of data processing. Components of Spark include Machine Learning Library, Spark Core, Spark SQL, Spark Learning and GraphX.

Factors to consider in choosing between Apache Spark and Hadoop

It is important to choose between these Big Data tools by noting different features that indicate their suitability for your project and organization. Although Hadoop and Spark could be described as similar, certain important features distinguish them. Proper consideration of the following features will reveal the best Big Data framework for your organization.

The features highlighted above are now compared between Apache Spark and Hadoop.

Spark vs Hadoop: Performance

Performance is a major feature to consider in comparing Spark and Hadoop. Spark allows in-memory processing, which notably enhances its processing speed. The fast processing speed of Spark is also attributed to the use of disks for data that are not compatible with memory. Spark allows the processing of data in real-time, a feature that makes it suitable for use in machine learning, security analytics, and credit card processing systems. This feature also distinguishes it from Hadoop.

Hadoop also has impressive speed, known to process terabytes of unstructured data in minutes, while processing petabytes of data in hours, based on its distribution system. However, Hadoop was not designed for real-time processing of data. On the other hand, Hadoop is suitable for storing and processing data from a range of sources.

Comparing the processing speed of Hadoop and Spark: it is noteworthy that when Spark runs in-memory, it is 100 times faster than Hadoop. When it runs on a disk, it is ten times faster than Hadoop. With fewer machines, up to 10 times fewer, Spark can process 100 TBs of data at three times the speed of Hadoop. This notable speed is attributed to the in-memory processing of Spark.

In terms of performance, Spark is faster than Hadoop because it processes data differently. Choosing between Spark and Hadoop, as regards processing, is thus a factor of the speed as well as the type of project which determines the suitable form of data processing.

Hadoop vs Spark: Type of data processing

The two major types of data processing applied in Big Data are batch processing and stream processing. As the name suggests, batch data processing is the processing of data that is initially collected and stored. With this type of data processing, data is collected over a period and then processed at a later time. This type of data processing is applied for large datasets that are static.

Stream data processing is a form of data processing that is aimed at real-time application. It is also the more current of the two types of data processing. Data is not stored and then processed. Instead, data is processed as it collected. This type of data processing is tailored to the needs of organizations to respond to changes quickly.

As regards Apache Hadoop and Spark, batch data processing is applied by Hadoop, while Spark applies stream data processing which makes it suitable for real-time processing of large datasets.

YARN, the processing component of Hadoop performs operations in a step-by-step manner, while GraphX allows users to view data in different forms in real-time.

When deciding between Hadoop and Spark-based on data processing, it is important to consider the peculiarities of both types of data processing and their suitability for different kinds of projects. Although stream processing makes operations with Spark fast, stream processing is tailored toward real-time processing of data. Batch processing utilized by Hadoop is suitable for the storage and processing of large datasets collected over specific periods.

Spark vs Hadoop: Cost

Before tools applied in organizations are chosen, it is important to consider their costs. This also applies to Apache Spark and Hadoop, even though they are open-source tools. The cost implications of Hadoop and Spark is related to infrastructure involved in their use. Both tools use different commodity hardware in different ways.

With Hadoop, the storage and processing of data occur within a disk. Thus, Hadoop only requires a lot of disk space. It is also noteworthy that Hadoop requires standard memory to function optimally. Hadoop also requires multiple systems applied in the distribution of the I/O of the disk. Thus, a major expenditure when using Hadoop is on disks, with a focus on high-quality disks.

Spark applies in-memory processing. Thus, there is less focus on hard disks, in comparison with Hadoop. Although Spark applies standard disk space, data processing with Spark does not require disks. Instead, Spark requires a lot of RAM in the data processing.

The difference infrastructure makes Spark is a costlier option than Hadoop. The infrastructure that makes Spark expensive is responsible for the in-memory processing for which it is known. When choosing between Hadoop and Spark-based on cost, the type of project should be considered too, since the cost of the use of Spark could be reduced when it is mainly used for real-time data analytics.

Hadoop vs Spark: Ease of use

The ease of use of a Big Data tool determines how well the tech team at an organization will be able to adapt to its use, as well as its compatibility with existing tools.

A major score for Spark as regards ease of use is its user-friendly APIs. These APIs are available for Python, Scala and Java. Spark SQL, which is similar to SQL, is another indication of its user-friendliness since it can be easily learned by developers that are already familiar with SQL, a common find. It is also noteworthy that Spark has a shell that allows users to get immediate results for queries and other actions. This interactive platform helps users run commands with significant ease.

The multilingual support offered by Spark is especially noteworthy, as regards its ease of use. It is important to mention that Spark codes can be applied for batch processing, in addition to stream processing.

Indications of the user-friendliness of Hadoop include the ease of data ingestion. Data can be ingested with a shell. Users can also integrate Hadoop with tools such as Flume to ingest data. To process data with YARN, Hadoop can also be integrated with tools such as Hive and Pig. Although running programs with Hadoop can be tasking, because there is no interaction, tools like Pig make it easier to run.

Spark vs Hadoop: Fault tolerance

The fault tolerance ability of these tools is their ability to complete operation after an error occurs. Both Big Data tools have fault-tolerance features. Hadoop and Spark effect fault tolerance in different ways, and it is important to consider them.

Hadoop effects fault tolerance in two ways, through the qualitative control function of the master daemons, as well as with commodity hardware.

Community hardware is applied by Hadoop in replicating data when failures occur. The master daemons of the two components of Hadoop monitor the operation of the slave daemons. When a slave daemon fails, its tasks are assigned to another slave daemon that is functional.

Both processes applied in fault tolerance by Hadoop increase the time for carrying out an operation. Thus, there are measures for checking failures with Hadoop; when failures occur, the operation time could be significantly increased.

Resilient Distributed Datasets (RDDs) which are the basic unit for Spark are applied in fault tolerance. RDDs check failures by referring to datasets shared in external storage systems. Thus, RDDs can keep datasets accessible, in memory, across operations. RDDs can also be recomputed when they are lost.

Since RDDs are involved in fault tolerance in Spark when failures occur, minimal downtime is experienced, and operation time is not significantly lengthened.

As regards fault tolerance, Spark and Hadoop have measures of fault tolerance that are effective. The operation time becomes longer with Hadoop than with Spark.

Hadoop vs Spark: Security

Hadoop and Spark have security measures implemented to keep operations away from unauthorized parties. These security measures differ, and it is important to examine them to choose the most suitable option for your project or organization.

Authentication is carried out with Kerberos and third-party tools on Hadoop. The third-party authentication options for Hadoop include Lightweight Directory Access Protocol. Security measures also apply to the components of Hadoop. For HDFS, for example, access control lists, as well as traditional file permissions, are applied.

Security measures implemented to keep the operations of users of Spark safe include the file-level permissions and access control lists of HDFS since Spark and HDFS can be integrated. Since Spark can also run on YARN, it can apply Kerberos as a security measure. Shared secrets are also applied to securing Spark.

In terms of security, it can be stated that Hadoop is more secure than Spark because of the different security measures and tools applied. The security of Spark could be described as still evolving. However, since Spark and Hadoop can be integrated, the security features of Hadoop can be applied by Spark.

Spark vs Hadoop: Type of project and market scope

The type of project should ultimately guide the choice of Big Data tools. All the factors listed above should be considered against the type of project. The features of Hadoop indicate that it is most suitable for projects that involve collecting and processing large datasets. Such projects should also not require real-time data analytics.

Spark is the Big Data tool designed for projects that require real-time data analytics, with minimal focus on the storage of large datasets.

Although Spark is more expensive to use than Hadoop, the details of projects could be modified to fit a wide range of budgets.

Spark and Hadoop are tools trusted by some of the biggest names in the tech space because of their suitability for different kinds of projects. When the market scope of both tools is compared, Hadoop covers a wider market scope.

There are predictions that Hadoop will experience a CAGR growth of about 65% in the period from 2018 to 2025. In this period, Spark will experience a growth of about 39%, in terms of CAGR.

Hadoop and Spark are Big Data tools with features that indicate their suitability for specific projects. These features should be properly considered in choosing the most appropriate tool for a project. The peculiarities of both tools could also be combined and applied for projects within organizations.