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.
|Speed||No automatic optimization process|
|Ease of Use||File Management System|
|Advanced Analytics||Fewer Algorithms|
|Dynamic in Nature||Small Files Issue|
|Apache Spark is powerful||Doesn’t suit for a multi-user environment|
|Increased 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:
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.
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.
Spark not only supports ‘MAP’ and ‘reduce’. It also supports Machine learning (ML), Graph algorithms, Streaming data, SQL queries, etc.
With Apache Spark, you can easily develop parallel applications. Spark offers you over 80 high-level operators.
Apache Spark supports many languages for code writing such as Python, Java, Scala, etc.
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.
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.
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.
The best thing about Apache Spark is, it has a massive Open-source community behind it.
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.
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.
Apache Spark doesn’t come with its own file management system. It depends on some other platforms like Hadoop or other cloud-based platforms.
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.
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.
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.
Yes, Apache Spark doesn’t fit for a multi-user environment. It is not capable of handling more users concurrency.
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!
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