Main Big Data Tools Hadoop vs Spark

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Most Big Data processing is done on one of two popular platforms, Hadoop or Spark. They allow you to manage massive datasets in any format, from Excel tables to website comments to media files. But which famous person should you entrust with your most sensitive data?

We need to break this huge problem down into manageable chunks if we want to find the right solution:

  • Please explain what Hadoop is.
  • What is the procedure?
  • Exactly where does the Hadoop ecosystem step in to remedy its flaws, if at all?
  • Whence came the requirement for Spark?
  • Is there a specific set of Big Data problems that Spark excels at resolving?
  • Can you tell me about the disadvantages of Spark?

Detailed solutions are provided in this article. If you happen to be familiar with a few of them, that’s fine. Proceed to the next paragraph to further your education. Comparison of the two systems’ primary distinguishing features is provided in the table below.

What is Hadoop

Apache Hadoop is a Java-based, free framework for storing and processing massive datasets in a distributed fashion. Since the data quantities are too great to be stored and analyzed by a single computer, the key word here is distributed.

The framework allows for the partitioning of a large data set into manageable pieces, which can then be distributed across the many nodes that make up a Hadoop cluster. As a result, a Big Data analytics task is broken down into smaller tasks that can be executed in parallel on different machines. However, to the end-user, it appears as though the pieces make up a whole.

Hadoop simplifies distributed computing by providing an abstracted API for direct access to the system’s features and benefits.

  • scalability. From a single proof-of-concept machine, the cluster can easily scale up to hundreds of machines by adding new nodes. Hadoop imposes almost no constraints on data storage.
  • versatility. By utilizing Hadoop, you can combine and analyze information from a wide variety of structured and unstructured data stores. Loading information does not necessitate backups or cleaning.
  • cost-effectiveness. Hadoop is supported by inexpensive, standard hardware, reducing the overall cost of ownership.
  • fail-safe design. Information is automatically replicated within the system to ensure data integrity in the event of a node failure.

Familiarity with Hadoop’s architecture and core components is necessary for a complete comprehension of the mechanism.

The structure of Hadoop, or how it operates

Main Big Data Tools Hadoop vs Spark

Hadoop can be set up in either a single-node or a multi-node cluster. The former is preferable during the evaluation or test phase because it involves setting up the framework on a single virtual machine.

Each node in the latter, more typical case operates as a virtual machine unto itself. Hundreds, if not thousands, of computers are required for Big Data processing. Consequently, a multi-node deployment strategy is mentioned in the references.

Hadoop nodes: masters and slaves

Hadoop clusters contain a variety of different nodes. Which of the three categories they belong to is dependent on their function.

Master Nodes manage and coordinate Hadoop’s two primary features, its data storage and its ability to process data in parallel. Specifically, they need cutting-edge hardware.

Worker or Slave Nodes are the vast majority of nodes in a system, and they serve to store information and carry out computations in response to commands from a central processing unit.

A Client Node, also known as a Gateway Node or an Edge Node, connects a Hadoop cluster to the wider Internet. Loading data into the cluster, describing how the data must be processed, and retrieving the output are not tasks that fall under the purview of the master-slave paradigm.

There are three levels of functionality in every Hadoop cluster:

  • HDFS, Hadoop’s Distributed File System, is the storage layer.
  • the resource management layer represented by YARN, and
  • the processing layer called MapReduce.

All Hadoop layers incorporate both master and slave nodes to facilitate communication between them. Let’s take a closer look at how they function.

HDFS: a storage layer

Hadoop Distributed File System (HDFS for short) is the framework’s central component for storing and managing data that has been partitioned across a cluster of computers. Hadoop utilizes 128MB blocks by default, but this can be modified in the config file.

HDFS was developed with the idea that data could be written once and read by many.A file in the system cannot be changed, but it can be analyzed in various ways over and over again. As a result, the tool prioritizes the speed with which the entire dataset is retrieved over the speed with which individual records are stored or retrieved.

In order to guarantee fault tolerance, HDFS automatically replicates each block in multiple worker nodes. If a node containing critical information fails, a replica can be used instead. We advise keeping three copies of everything, with “no more than one copy on the same node and no more than two copies in the same rack.”

HDFS master-slave structure

NameNodes are HDFS Master Nodes responsible for storing metadata containing crucial information about system files (such as their names, locations, number of data blocks in the file, etc.) as well as monitoring storage space, data transfer volume, etc.

Large files are stored in chunks across several Worker Nodes called DataNodes. The workers signal their master every three seconds to let it know that everything is running smoothly and the data is ready to be accessed.

DataNodes are typically housed in racks with 40-50 nodes all linked to the same network switch.

YARN: a resource management layer

Yet Another Resource Negotiator (YARN) is a software layer that keeps tabs on how much processing power, memory, and disk space are being used by active applications, distributes those resources to those applications, and schedules tasks based on those applications’ needs.

YARN master-slave structure

A ResourceManager acts as the system’s Master Node, with complete control over all available resources.

Each virtual machine’s resources are monitored by multiple slaves, or NodeManagers, which then report back to a master, or ResourceManager.

MapReduce: a processing layer

For batch processing, in which a large number of files are processed in one go after being gathered over time, MapReduce is widely regarded as the most effective solution.

The whole process can be broken down into its component parts—map and reduce (hence the name.) The Map stage processes data by applying filters, sorting, and splitting operations, while the Reduce stage compiles and summarizes the data.

This method is best for businesses that are less concerned with obtaining immediate analytics results in real-time and more concerned with gaining in-depth insights from massive data volumes.

MapReduce master-slave structure

Client requests are received by a Master Node called a JobTracker, which then communicates with the NameNode to determine the location of the data, divides up the work amongst the slave nodes, and updates the client on the progress of the job.

JobTracker directs Slave Nodes, also known as TaskTrackers, to carry out map and reduce operations. They update their Master Node on execution status in real time, just like DataNodes do.

Hadoop limitations

While Apache Hadoop is a powerful Big Data tool, it is not sufficient on its own. There are numerous restrictions. We’ve compiled a list of Hadoop’s biggest drawbacks for you.

Small file problem. Hadoop was developed to handle large datasets, not a large number of files significantly smaller than Hadoop’s default of 128 MB. The NameNode is responsible for storing metadata like names, access rights, locations, etc., for each data unit. Obviously, millions of small files will take up too much memory in the Master Nodes and generate a great deal of work, which will slow down the processing.

High latency of data access. Throughput, or the system’s capacity to deliver large data batches, is guaranteed by Hadoop. However, this is at the expense of latency, the time it takes for a system to respond to an input from a user. To rephrase, it will take a considerable amount of time to locate and retrieve even a single record. Hadoop’s high latency makes it inappropriate for applications that need constant access to data.

No real-time data processing. However, MapReduce is not suited to real-time analytics or processing time-sensitive data, as it only supports batch processing.

Complex programming environment. Data engineers who are only familiar with SQL queries and relational database management systems will need training to use Hadoop effectively. For advanced Hadoop programming and making the most of the Hadoop features accessible through Java APIs, they will need to be familiar with Java. Learning Hadoop’s foundational concepts is also crucial.

Apache Hadoop ecosystem

Main Big Data Tools Hadoop vs Spark

To avoid these problems and address the full scope of the Big Data workflow, many complementary new services have emerged. They all work together to form the Hadoop ecosystem, a comprehensive set of programs that extend the capabilities of the framework and overcome its shortcomings.

Data storage options

Apache HBase, NoSQL databases built on top of HDFS can store tabular data with millions of rows and columns. Thanks to its in-memory processing engine, HDFS data can be accessed rapidly and in real-time.

Another member of the noSQL database family is Apache Cassandra, which can also be used as an alternative. Cassandra Query Language, its own SQL-like language, distinguishes it from HBase as an independent technology. Cassandra is highly effective at processing and analyzing real-time data.

Data access options

Common SQL queries can be translated into specialized HBase commands (scans) and executed in parallel with the help of Apache Phoenix, another popular tool used with HBase.

For data specialists who are already fluent in SQL, Hadoop and HBase can be used with the help of other tools such as Apache Pig and Apache Hive.

Pig, created by Yahoo, offers a scripting language called Pig Latin (not to be confused with the children’s game of the same name) that is similar to SQL and is used to express data flows. It can process any kind of data, whether it’s completely freeform or has some structure to it. A MapReduce operation can be described in just 10 lines of Pig Latin code, as opposed to 200 lines in Java.

Facebook’s Hive is a query engine that operates on the back of the SQL-like query processing language Hive QL. Data analysts working with structured data in HDFS or HBase are Hive’s primary users.

Data management and monitoring options

The solutions listed here are some of the many that can help with data management.

  • Apache Sqoop, which makes it easier to move data between Hadoop and traditional databases;
  • Apache ZooKeeper for HBase metadata coordination and management;
  • Apache Oozie arranging Hadoop-based tasks;
  • Apache Flume in order to collect large amounts of log data and transfer them to HDFS for further examination; and
  • Apache Ambari, providing a dynamic interface for managing and monitoring all Hadoop cluster applications in real time.

Processing options

On top of MapReduce, Apache Mahout executes machine learning algorithms for clustering, classification, and other Hadoop-based tasks.

It’s obvious that this doesn’t include the entirety of Hadoop’s supporting infrastructure. Its most sought-after satellite, Apache Spark, is a data processing engine. The need for significantly faster data processing than MapReduce gave rise to this Big Data superstar.

Explaining Apache Spark’s salient features and advantages over Hadoop

Spark was developed as a direct successor to MapReduce and works in a similar fashion to it in that it processes data in batches while distributing the associated workloads across a network of interconnected servers.

The engine, like its forerunner, is compatible with both master-slave and multi-node deployments. There is one master node, or driver, in a Spark cluster that oversees all of the other nodes, or slaves, that do the actual work. And that’s about it in terms of similarities.

Hadoop and Spark are essentially two different data processing frameworks.

MapReduce writes partial computations to local disks so that they can be retrieved and used in subsequent operations. Spark, on the other hand, stores information in the system’s random access memory (Random Access Memory.)

The fastest disk read times still can’t keep up with RAM speeds. Therefore, it should come as no great surprise that, assuming all data can be stored in RAM, Spark can complete tasks one hundred times faster than MapReduce. Although Spark is ten times slower than the Hadoop engine, it still provides better performance than Hadoop when datasets are so large or queries are so complex that they must be saved to disc.

Down below, we’ll delve into Apache Spark’s fundamentals and examine its other distinguishing features besides in-memory data processing as they relate to Hadoop.

Data structures in Spark’s core and across nodes

The Spark Core computational engine runs the framework. It’s to blame for

  • distributed data processing,
  • memory management,
  • task scheduling,
  • fault recovery, and
  • interaction with a remote cluster administrator and data store.

Spark Core uses Resilient Distributed Dataset as its primary data format (RDD.) It’s a set of records that can be read and processed in parallel without exposing the partitioning to the user. Both structured and unstructured data are easily processed by RDD.

Information in the named columns can also be organized in a different schema called DataFrames, which is analogous to tables in relational databases.

Its Datasets add-on combines the best features of the two earlier models. Data structures like RDD are fully supported, and SQL queries can be executed, albeit more slowly than with DataFrames.

There isn’t a predefined database or resource manager

Spark, in contrast to Hadoop, which combines storage, processing, and resource management, is designed solely for data analysis and has no built-in mechanism for storing intermediate results. It is instead capable of reading and writing data to and from a wide variety of sources, such as HDFS, HBase, and Apache Cassandra. It works with a wide variety of data storage systems beyond the Hadoop ecosystem, such as Amazon S3.

Despite its popularity, Spark had limitations when it came to managing resources, especially central processing unit (CPU) and memory usage, when processing data across multiple servers. A cluster or resource manager is required for this purpose. There are currently four available choices within the framework:

  • An independent, pre-made cluster manager that is both simple and straightforward;
  • Hadoop’s YARN, its most popular framework for Spark;
  • Apache Mesos, which is used to manage the infrastructure of massive data centers and intensive services; and
  • Kubernetes is a system for managing containers.

If your organization is considering migrating your entire technology stack to a cloud-native architecture, running Spark on Kubernetes is a logical next step.

This includes MLlib and GraphX in addition to Spark Streaming and Spark SQL.

Spark includes a stack of four libraries that permit the development of multiple analytics apps on top of a common platform while making use of an external cluster manager and data repository.

Spark Streaming enables the core engine with near-real-time processing capabilities and simplifies the development of streaming analytics merchandise. As micro-batches, the module can take in real-time data streams from places like Apache Kafka, Apache Flume, Amazon Kinesis, and Twitter.

For the record, the following organizations use Spark Streaming and Kafka:

  • Analytical telematics services similar to Uber;
  • Pinterest for conducting international research on consumer habits, and
  • Netflix, for movie suggestions almost as fast as they come out.

Spark SQL builds a bridge for sharing information between RDDs and SQL databases. It makes it easier for data scientists to query data structures within Spark applications.

GraphX provides a library of operators and algorithms for analyzing graph data.

MLlib contains algorithms for various machine learning tasks, including classification, clustering, and regression, and is designed for scalability. Statistics, ML pipeline development, model evaluation, and other tools are all included.

APIs that are easy to use and available in multiple languages

APIs are provided for access to Spark’s core engine, data structures, and libraries. The framework is written in Scala, but it works with Java, Python, and R as well. It’s therefore accessible to a wide variety of specialists who already possess fluency in the aforementioned languages. Therefore, businesses can draw from a larger talent pool than they could with Java-centric Hadoop.

Spark limitations

In comparison to Hadoop’s MapReduce engine, Spark clearly excels. However, there are some drawbacks to keep in mind.

Pricey hardware. However, because RAM costs more than the hard discs used by MapReduce, running Spark operations is more expensive.

Near, but not truly real-time processing. Quick data analysis is now possible with Spark Streaming and in-memory caching. However, it still won’t be in real-time because the module processes data in “micro-batches,” or small groups of events collected over a time period. In fact, true real-time processing tools perform their operations on data streams in the very moment they are created.

This means Spark isn’t an ideal fit for IoT applications. The Apache portfolio has superior tools for real-time analytics. For instance, Apache Flink was created with real-time data processing in mind. Storm, when run atop HBase, is superior to Spark in its ability to process real-time data streams.

Issues with small files. The same is true of Spark; it struggles when presented with a large number of relatively small datasets. When there are more files included in a workload, there is also more metadata to parse and more tasks to schedule, both of which can significantly increase the processing time.

Choosing between Hadoop and Spark: What to Look for?

Main Big Data Tools Hadoop vs Spark

In a strict sense, the choice is not between Spark and Hadoop, but rather between two processing engines, given that Hadoop is more than just a processing engine.

MapReduce’s main benefit is its low overhead in handling massive, delay-tolerant processing jobs. It’s most efficient for data that can be stored and analyzed at a later time, such as late at night. Here are a few practical examples:

  • analysis of customer feedback on the web to learn how they feel about a product
  • Internet user sentiment analysis regarding a given product
  • examination of recorded activity to forestall security lapses.

When time is more important than money, however, Spark really shines. An obvious option for

  • The ability to identify and thwart fraud,
  • Market forecasting, especially for stocks,
  • technology that makes suggestions in a timely fashion, or
  • risk management.

However, other considerations, such as the accessibility of specialists, may prove decisive. In addition, Spark and Hadoop’s effectiveness is highly dependent on the tools used in conjunction with them.

Spark is independent of Hadoop, but it is typically used in conjunction with HDFS as the data store and YARN as the resource manager. Therefore, you will frequently switch back and forth between the two mediums. In addition, many businesses use both MapReduce and Spark Core for their Big Data processing needs. In contrast to the former, which handles larger operations at lower costs, the latter works with smaller data batches when immediate analytics results are essential.