Search

Apache Spark Pros and Cons

Apache Spark:  The New ‘King’ of Big DataApache Spark is a lightning-fast unified analytics engine for big data and machine learning. It is the largest open-source project in data processing. Since its release, it has met the enterprise’s expectations in a better way in regards to querying, data processing and moreover generating analytics reports in a better and faster way. Internet substations like Yahoo, Netflix, and eBay, etc have used Spark at large scale. Apache Spark is considered as the future of Big Data Platform.Pros and Cons of Apache SparkApache SparkAdvantagesDisadvantagesSpeedNo automatic optimization processEase of UseFile Management SystemAdvanced AnalyticsFewer AlgorithmsDynamic in NatureSmall Files IssueMultilingualWindow CriteriaApache Spark is powerfulDoesn’t suit for a multi-user environmentIncreased access to Big data-Demand for Spark Developers-Apache Spark has transformed the world of Big Data. It is the most active big data tool reshaping the big data market. This open-source distributed computing platform offers more powerful advantages than any other proprietary solutions. The diverse advantages of Apache Spark make it a very attractive big data framework. Apache Spark has huge potential to contribute to the big data-related business in the industry. Let’s now have a look at some of the common benefits of Apache Spark:Benefits of Apache Spark:SpeedEase of UseAdvanced AnalyticsDynamic in NatureMultilingualApache Spark is powerfulIncreased access to Big dataDemand for Spark DevelopersOpen-source community1. Speed:When comes to Big Data, processing speed always matters. Apache Spark is wildly popular with data scientists because of its speed. Spark is 100x faster than Hadoop for large scale data processing. Apache Spark uses in-memory(RAM) computing system whereas Hadoop uses local memory space to store data. Spark can handle multiple petabytes of clustered data of more than 8000 nodes at a time. 2. Ease of Use:Apache Spark carries easy-to-use APIs for operating on large datasets. It offers over 80 high-level operators that make it easy to build parallel apps.The below pictorial representation will help you understand the importance of Apache Spark.3. Advanced Analytics:Spark not only supports ‘MAP’ and ‘reduce’. It also supports Machine learning (ML), Graph algorithms, Streaming data, SQL queries, etc.4. Dynamic in Nature:With Apache Spark, you can easily develop parallel applications. Spark offers you over 80 high-level operators.5. Multilingual:Apache Spark supports many languages for code writing such as Python, Java, Scala, etc.6. Apache Spark is powerful:Apache Spark can handle many analytics challenges because of its low-latency in-memory data processing capability. It has well-built libraries for graph analytics algorithms and machine learning.7. Increased access to Big data:Apache Spark is opening up various opportunities for big data and making As per the recent survey conducted by IBM’s announced that it will educate more than 1 million data engineers and data scientists on Apache Spark. 8. Demand for Spark Developers:Apache Spark not only benefits your organization but you as well. Spark developers are so in-demand that companies offering attractive benefits and providing flexible work timings just to hire experts skilled in Apache Spark. As per PayScale the average salary for  Data Engineer with Apache Spark skills is $100,362. For people who want to make a career in the big data, technology can learn Apache Spark. You will find various ways to bridge the skills gap for getting data-related jobs, but the best way is to take formal training which will provide you hands-on work experience and also learn through hands-on projects.9. Open-source community:The best thing about Apache Spark is, it has a massive Open-source community behind it. Apache Spark is Great, but it’s not perfect - How?Apache Spark is a lightning-fast cluster computer computing technology designed for fast computation and also being widely used by industries. But on the other side, it also has some ugly aspects. Here are some challenges related to Apache Spark that developers face when working on Big data with Apache Spark.Let’s read out the following limitations of Apache Spark in detail so that you can make an informed decision whether this platform will be the right choice for your upcoming big data project.No automatic optimization processFile Management SystemFewer AlgorithmsSmall Files IssueWindow CriteriaDoesn’t suit for a multi-user environment1. No automatic optimization process:In the case of Apache Spark, you need to optimize the code manually since it doesn’t have any automatic code optimization process. This will turn into a disadvantage when all the other technologies and platforms are moving towards automation.2. File Management System:Apache Spark doesn’t come with its own file management system. It depends on some other platforms like Hadoop or other cloud-based platforms.3. Fewer Algorithms:There are fewer algorithms present in the case of Apache Spark Machine Learning Spark MLlib. It lags behind in terms of a number of available algorithms.4. Small Files Issue:One more reason to blame Apache Spark is the issue with small files. Developers come across issues of small files when using Apache Spark along with Hadoop. Hadoop Distributed File System (HDFS) provides a limited number of large files instead of a large number of small files.5. Window Criteria:Data in Apache Spark divides into small batches of a predefined time interval. So Apache won't support record-based window criteria. Rather, it offers time-based window criteria.6. Doesn’t suit for a multi-user environment:Yes, Apache Spark doesn’t fit for a multi-user environment. It is not capable of handling more users concurrency.Conclusion:To sum up, in light of the good, the bad and the ugly, Spark is a conquering tool when we view it from outside. We have seen a drastic change in the performance and decrease in the failures across various projects executed in Spark. Many applications are being moved to Spark for the efficiency it offers to developers. Using Apache Spark can give any business a boost and help foster its growth. It is sure that you will also have a bright future!

Apache Spark Pros and Cons

9K
Apache Spark Pros and Cons

Apache Spark:  The New ‘King’ of Big Data

Apache Spark is a lightning-fast unified analytics engine for big data and machine learning. It is the largest open-source project in data processing. Since its release, it has met the enterprise’s expectations in a better way in regards to querying, data processing and moreover generating analytics reports in a better and faster way. Internet substations like Yahoo, Netflix, and eBay, etc have used Spark at large scale. Apache Spark is considered as the future of Big Data Platform.

Pros and Cons of Apache Spark

Apache Spark
AdvantagesDisadvantages
SpeedNo automatic optimization process
Ease of UseFile Management System
Advanced AnalyticsFewer Algorithms
Dynamic in NatureSmall Files Issue
MultilingualWindow Criteria
Apache Spark is powerfulDoesn’t suit for a multi-user environment
Increased access to Big data-
Demand for Spark Developers-

Apache Spark Pros & Cons

Apache Spark has transformed the world of Big Data. It is the most active big data tool reshaping the big data market. This open-source distributed computing platform offers more powerful advantages than any other proprietary solutions. The diverse advantages of Apache Spark make it a very attractive big data framework. 

Apache Spark has huge potential to contribute to the big data-related business in the industry. Let’s now have a look at some of the common benefits of Apache Spark:

Benefits of Apache Spark:

  1. Speed
  2. Ease of Use
  3. Advanced Analytics
  4. Dynamic in Nature
  5. Multilingual
  6. Apache Spark is powerful
  7. Increased access to Big data
  8. Demand for Spark Developers
  9. Open-source community

1. Speed:

When comes to Big Data, processing speed always matters. Apache Spark is wildly popular with data scientists because of its speed. Spark is 100x faster than Hadoop for large scale data processing. Apache Spark uses in-memory(RAM) computing system whereas Hadoop uses local memory space to store data. Spark can handle multiple petabytes of clustered data of more than 8000 nodes at a time. 

2. Ease of Use:

Apache Spark carries easy-to-use APIs for operating on large datasets. It offers over 80 high-level operators that make it easy to build parallel apps.

The below pictorial representation will help you understand the importance of Apache Spark.

Popularity of Apache Spark

3. Advanced Analytics:

Spark not only supports ‘MAP’ and ‘reduce’. It also supports Machine learning (ML), Graph algorithms, Streaming data, SQL queries, etc.

4. Dynamic in Nature:

With Apache Spark, you can easily develop parallel applications. Spark offers you over 80 high-level operators.

5. Multilingual:

Apache Spark supports many languages for code writing such as Python, Java, Scala, etc.

6. Apache Spark is powerful:

Apache Spark can handle many analytics challenges because of its low-latency in-memory data processing capability. It has well-built libraries for graph analytics algorithms and machine learning.

7. Increased access to Big data:

Apache Spark is opening up various opportunities for big data and making As per the recent survey conducted by IBM’s announced that it will educate more than 1 million data engineers and data scientists on Apache Spark. 

8. Demand for Spark Developers:

Apache Spark not only benefits your organization but you as well. Spark developers are so in-demand that companies offering attractive benefits and providing flexible work timings just to hire experts skilled in Apache Spark. As per PayScale the average salary for  Data Engineer with Apache Spark skills is $100,362. For people who want to make a career in the big data, technology can learn Apache Spark. You will find various ways to bridge the skills gap for getting data-related jobs, but the best way is to take formal training which will provide you hands-on work experience and also learn through hands-on projects.

9. Open-source community:

The best thing about Apache Spark is, it has a massive Open-source community behind it. 

Apache Spark is Great, but it’s not perfect - How?

Apache Spark is a lightning-fast cluster computer computing technology designed for fast computation and also being widely used by industries. But on the other side, it also has some ugly aspects. Here are some challenges related to Apache Spark that developers face when working on Big data with Apache Spark.

Let’s read out the following limitations of Apache Spark in detail so that you can make an informed decision whether this platform will be the right choice for your upcoming big data project.

  1. No automatic optimization process
  2. File Management System
  3. Fewer Algorithms
  4. Small Files Issue
  5. Window Criteria
  6. Doesn’t suit for a multi-user environment

1. No automatic optimization process:

In the case of Apache Spark, you need to optimize the code manually since it doesn’t have any automatic code optimization process. This will turn into a disadvantage when all the other technologies and platforms are moving towards automation.

2. File Management System:

Apache Spark doesn’t come with its own file management system. It depends on some other platforms like Hadoop or other cloud-based platforms.

3. Fewer Algorithms:

There are fewer algorithms present in the case of Apache Spark Machine Learning Spark MLlib. It lags behind in terms of a number of available algorithms.

4. Small Files Issue:

One more reason to blame Apache Spark is the issue with small files. Developers come across issues of small files when using Apache Spark along with Hadoop. Hadoop Distributed File System (HDFS) provides a limited number of large files instead of a large number of small files.

5. Window Criteria:

Data in Apache Spark divides into small batches of a predefined time interval. So Apache won't support record-based window criteria. Rather, it offers time-based window criteria.

6. Doesn’t suit for a multi-user environment:

Yes, Apache Spark doesn’t fit for a multi-user environment. It is not capable of handling more users concurrency.

Conclusion:

To sum up, in light of the good, the bad and the ugly, Spark is a conquering tool when we view it from outside. We have seen a drastic change in the performance and decrease in the failures across various projects executed in Spark. Many applications are being moved to Spark for the efficiency it offers to developers. Using Apache Spark can give any business a boost and help foster its growth. It is sure that you will also have a bright future!

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

Join the Discussion

Your email address will not be published. Required fields are marked *

Suggested Blogs

Top Pros and Cons of Hadoop

Big Data is one of the major areas of focus in today’s digital world. There are tons of data generated and collected from the various processes carried out by the company. This data could contain patterns and methods as to how the company can improve its processes. The data also contains feedback from the customer. Needless to say, this data is vital to the company and should not be discarded. But, the entire set is also not useful, a certain amount of data is futile. This set should be differentiated from the useful part and discarded. To carry out this major process, various platforms are used. The most popular among these platforms is Hadoop. Hadoop can efficiently analyse the data and extract the useful information. It also comes with its own set of advantages and disadvantages such as: Pros 1. Range of data sources The data collected from various sources will be of structured or unstructured form. The sources can be social media, clickstream data or even email conversations. A lot of time would need to be allotted in order to convert all the collected data into a single format. Hadoop saves this time as it can derive valuable data from any form of data. It also has a variety of functions such as data warehousing, fraud detection, market campaign analysis etc. 2. Cost effective In conventional methods, companies had to spend a considerable amount of their benefits into storing large amounts of data. In certain cases they even had to delete large sets of raw data in order to make space for new data. There was a possibility of losing valuable information in such cases. By using Hadoop, this problem was completely solved. It is a cost-effective solution for data storage purposes. This helps in the long run because it stores the entire raw data generated by a company. If the company changes the direction of its processes in the future, it can easily refer to the raw data and take the necessary steps. This would not have been possible in the traditional approach because the raw data would have been deleted due to increase in expenses. 3. Speed Every organization uses a platform to get the work done at a faster rate. Hadoop enables the company to do just that with its data storage needs. It uses a storage system wherein the data is stored on a distributed file system. Since the tools used for the processing of data are located on same servers as the data, the processing operation is also carried out at a faster rate. Therefore, you can processes terabytes of data within minutes using Hadoop. 4. Multiple copies Hadoop automatically duplicates the data that is stored in it and creates multiple copies. This is done to ensure that in case there is a failure, data is not lost. Hadoop understands that the data stored by the company is important and should not be lost unless the company discards it. Cons 1. Lack of Preventive Measures When handling sensitive data collected by a company, it is mandatory to provide the necessary security measures. In Hadoop, the security measures are disabled by default. The person responsible for data analytics should be aware of this fact and take the required measures to secure the data. 2. Small Data Concerns There are a few big data platforms in the market that aren’t fit for small data functions. Hadoop is one such platform wherein only large business that generates big data can utilize its functions. It cannot efficiently perform in small data environments. 3. Risky Functioning Java is one of the most widely used programming languages. It has also been connected to various controversies because cyber criminals can easily exploit the frameworks that are built on Java. Hadoop is one such framework that is built entirely on Java. Therefore, the platform is vulnerable and can cause unforeseen damages. Every platform used in the digital world comes with its own set of advantages and disadvantages. These platforms serve a purpose that it vital to the company. Hence, it is necessary to check if the pros outweigh the cons. If they do, then utilize the pros and take preventive measures to guard yourself against the cons. To know more about Hadoop and pursue a career in it, enrol for a big data Hadoop certification. You can also gain better with big data Hadoop training online courses.
22354
Top Pros and Cons of Hadoop

Big Data is one of the major areas of focus in tod... Read More

Master Big Data With A Hadoop Certification

Ever wondered how your social media posts or online transaction details are always available? And not just yours, but anyone who uses the internet can access their data whenever they want. With the advent of technology and the internet, the amount of data generated online is humongous. According to a report, almost 90% of the data that we have today have been created over the last couple of years. Whether it’s networking sites or weather reports, data is being generated every second and it needs to be stored for various purposes. How does this happen? Surely, all of these information can’t be stored in physical storage devices like pen drives or hard disks, unless there’s a huge football field to accommodate them. This is where ‘Big Data’ plays a huge role. Let’s find out more about it. What Is Big Data? Big data is a technology that can collate and store huge amounts of data every day. The information stored is important as it allows companies to assess their customers and market their products. For example, when a product or service is advertised on Facebook, users like or comment on the post. This data is then used by companies to judge the popularity of their product and further promote or improve their marketing campaigns accordingly. Big data, therefore, is one of the most important technologies of the modern world. However, most of the data is unstructured, which means it can’t be used for analysis and data mining. This where you need a software that can sort the unstructured data and provide data security. How Does Hadoop Help Big Data? Hadoop is an open-source, Java-based programming framework that’s capable of storing and processing large amounts of data. Hadoop makes use of a distributed computing framework, wherein data is formatted and stored in clusters of commodity hardware. Simultaneously, it also processes data by using cheap computers. This software is available for free download and is run and maintained by developers from all around the world. However, nowadays, commercial Hadoop software are being made available to suit the various data processing and storage needs of organizations. What Are The Advantages Of Hadoop? Apart from the fact that Hadoop can process and store data quickly, there are many other reasons that makes it the most preferred data storage choice. Let’s take a look at some of them: ● Hadoop offers you the flexibility of storing data as you want. For example, in traditional databases, you would have to first process the data and then store it. But, in Hadoop, you can store anything and then analyze it later and this includes unstructured data like images, videos, and texts. ● When you’re using the Hadoop framework, you can be assured that data will not be lost due to hardware failure. If any one of the nodes become faulty, the data will automatically distributed amongst other nodes. Also, several copies of the data will be made and stored automatically. ● It’s a free open-source software that relies on commodity hardware to process and store data. You can scale it up as per your needs by adding nodes. Why Take A Big Data And Hadoop Certification? If you want to begin a career in the IT industry or would like to become a data analyst, then you can’t do without big data. It’s everywhere and every internet-based service relies on big data technology to store their information and analyze it. No matter which field you choose, right from social media to weather reports, big data plays a big role in keeping them up and running. Therefore, it only makes senses that you take a certification in big data and Hadoop to add another point to your resume and eventually land better jobs. How Will The Certification Help Me? When you’re preparing for the certifying exam, you can take up a training course to better acquaint yourself with the subject. During your training, you will be taught about the various aspects of Hadoop and how it’s used to store big data. Some of the things that you’ll be learning during the training are: ● A clear understanding of the Hadoop ecosystem that includes Flame and Apache oozie workflow scheduler ● Mastery over the basic and advanced concepts of Hadoop 2.7 framework ● Learning to write MapReduce programs ● Conduct detailed data analysis with the help of Pig and Hive Apart from these, you will also be given training on setting up configurations for Hadoop clusters. With big data becoming an integral part of most businesses, mastering the Hadoop technology will help you land well-paying jobs. If you’ve been on the lookout for big data analytics jobs or want to become a software developer and architect, then a Hadoop certification will open up a world of opportunities for you.
14448
Master Big Data With A Hadoop Certification

Ever wondered how your social media posts or onlin... Read More

What Is Big Data and Why Use Hadoop?

What is Big Data and Why Use Hadoop? Big data specifies datasets that are very big. It’s a hoard of large datasets that cannot be processed by the traditional methods of computing. Big data is related to a complete subject rather than merely data that can be processed using various techniques, tools, and framework. Hadoop is an open-source frame, which is based on Java Programming and supports the storage and processing capabilities of extremely large datasets in a computing environment that is distributed across branches. Hadoop was developed by a team of computer scientists, which comprised of Mike Cafarella and Doug Cutting in 2005, to support the distribution capabilities of search engines. There are pros & cons in hadoop, but compared to pros, cons are negotiable. Benefits of Hadoop • Scalable: Hadoop is a storage platform that is highly scalable, as it can easily store and distribute very large datasets at a time on servers that could be operated in parallel. • Cost effective: Hadoop is very cost-effective compared to traditional database-management systems. • Fast: Hadoop manages data through clusters, thus providing a unique storage method based on distributed file systems. Hadoop’s unique feature of mapping data on the clusters provides a faster data processing. • Flexible: Hadoop enables enterprises to access and process data in a very easy way to generate the values required by the company, thereby providing the enterprises with the tools to get valuable insights from various types of data sources operating in parallel. • Failure resistant: One of the great advantages of Hadoop is its fault tolerance. This fault resistance is provided by replicating the data to another node in the cluster, thus in the event of a failure, the data from the replicated node can be used, thereby maintaining data consistency. Careers with Hadoop Big data with Hadoop training could make a great difference in getting your dream career. Employees with capabilities of handling big data are considered more valuable to the organisation. Hadoop skills are in great demand and thus it is very important for the IT professionals to keep up with the current trend, because the amount of data generated day by day is ever increasing. According to the Forbes magazine report of 2015, around 80% of the global organisations are reported to make high- or medium-level investments in big data analytics. They consider this investment to be very significant and so they plan to increase their investment in big data analytics. There are more job opportunities with Hadoop. Looking at the market forecast for Big Data, it looks like the need for Big Data engineers is going to increase. Big Data is here to stay, as the data is ever increasing and does not seem to slow down in the coming years. A research conducted by the Avendus Capital reported that the IT market in India for big data is hovering near $1.15 billion in the year 2015. Big data analytics contributed for about one-fifth of the nation’s KPO market, which is considered to be worth almost $5.6 billion. The Hindu also predicted that by the end of year 2018, India alone would be facing a shortage of almost quarter million Big Data scientists. Therefore, Big Data Analysis with Hadoop presents a great career and tremendous growth opportunity.
16348
What Is Big Data and Why Use Hadoop?

What is Big Data and Why Use Hadoop? Big data s... Read More