6 Advantages and Disadvantages of Apache Spark | Limitations & Benefits of Apache Spark

Post Top Ad

6 Advantages and Disadvantages of Apache Spark | Limitations & Benefits of Apache Spark

Share This
6 Advantages and Disadvantages of Apache Spark | Limitations & Benefits of Apache Spark

6 Advantages and Disadvantages of Apache Spark | Limitations & Benefits of Apache Spark


Big data and machine learning are two applications for Apache Spark, an open source computing framework. Indeed, big data has advanced to a new level thanks to Apache Spark.

Since its launch, it has received widespread praise from critics for its proficiency in data processing, analytical reporting, and querying. Spark is preferred by many big data-dependent sectors because of their consistent processing performance. Data is combined with artificial intelligence (AI) to process the data. Apache Spark is used by numerous internet behemoths, including Netflix, eBay, and Yahoo. Spark offers support for programming languages like Scala, Python, and Java as well.

Despite being the most widely used alternative for big data solutions, Spark isn't perfect. Spark can be substituted with a variety of alternative technologies. Consequently, you must weigh the advantages and disadvantages of Apache Spark to determine if it is the ideal framework for the project you are working on.

I will be discussing the 6 Advantages and Disadvantages of Apache Spark | Limitations & Benefits of Apache Spark in this essay. You will learn about the pros and cons of Apache Spark from this post.

Now let's get started,

Advantages of Apache Spark

1. Speed

In contrast to other frameworks such as Hadoop, Apache Spark processes data without utilizing local memory space. It uses a computational system based on RAM. 


Their processing speed is therefore substantially faster. particularly in regard to large data. Spark can process workloads 100 times quicker than Hadoop on average. 


Spark is the recommended choice for processing petabytes of data on a huge scale, for this reason.

2. User Friendliness

Using APIs, Apache Spark has the capability to handle huge datasets. More than 100 operators that aim to convert semi-structured data are available in these APIs. In the end, the procedure of developing parallel applications is hassle-free.

3. Big Data Access

Apache Spark finds numerous ways to make huge data accessible, ensuring maximum availability. To leverage them, an increasing number of engineers and data scientists are learning about Spark.

4.  Machine learning & Data analysis

Apache Spark uses libraries to make machine learning and data analysis easier. For instance, Spark contains a framework for extracting and transforming data, including structured data.

5. Standard Libraries

Higher level standard libraries are included with Spark. Typically, the libraries offer support for SQL queries, graph processing, and machine learning. 


By utilizing these libraries, developers can ensure optimal efficiency. Additionally, Spark makes it simple to complete jobs while requiring complex work flows.

6. Career Demand

For individuals who are willing to pursue a profession in big data, Apache Spark will be a fantastic choice. 


Spark engineers will have a lot to gain from their jobs, both financially and in terms of their workload. 


Once they have gained sufficient expertise, there is a great demand for them in their field. Employers are eager to hire them if they can offer competitive compensation.

Disadvantages of Apache Spark

1. Cost

Another thing to think about with Apache Spark is cost effectiveness. Large data processing does not yield cost-effective results when data is allocated in memory. 


In general, massive amounts of memory are needed for in-memory processing. increased memory consumption will inevitably result in increased costs as well.

2. Small File Issue

Combining Apache Spark with Hadoop frequently results in problems with little files. The Hadoop Distributed File System (HDFS) is the file system that Hadoop utilizes internally. 


Instead of supporting a high number of small files, they can only support a small number of huge files under usual circumstances.

3. Lack of Real-Time Processing

Batch processing is used to separate the data that is arriving in real time. The term Resilient Distributed Database (RDD) is frequently used to describe these batches. 


These batches are processed to finish other tasks after they arrive. 


They will eventually be divided into batches once more. Micro Batch Processing is the term for this procedure. As a result, it cannot fully handle real-time data processing.

4. No File Management System

File management is not something Apache Spark can handle by itself. It is dependent on additional external systems. 


It must be utilized in conjunction with a cloud-based data platform or in conjunction with the Hadoop Distributed File System (HDFS). Because of this, Spark is less effective than other platforms.

5. Manual Optimization

A recent development in the field of technology is automation. Today's most widely used platforms favor automation. 


Apache Spark does not have an automatic code optimization method. Every code must be manually optimized.

6. Pressure Control

Data Buffer is a situation that affects Apache Spark. In this instance, the buffer fills up entirely, preventing data flow. 


All subsequent data will align at that point. Until the buffer is cleared, none of this built-up data can be sent. Spark is unable to manage this data buffer back pressure.

No comments:

Post a Comment