When it comes to big data analytics the most sought after platforms and technologies are Apache Spark and Apache Hadoop which are often pitted against each other as competitors in the Big Data space. However, for the companies performing and managing big data analytics there could be a lingering question so as to which one they should opt for. If you have had doubts concerning the which one of these should you pursue to get your Hadoop Certification then you have landed on the right page. In this article we try to ease your task by comparing the two big names helping you choose the right framework as per your requirement.
Even though you may be tempted to choose Spark Training over Hadoop Training, keep in mind, both are known for individual tasks and the way they perform data management, they are actually mutually exclusive in their way of operation. Hadoop has an infrastructure of distributed data which is basically responsible for distributing within a cluster of commodity servers huge data collections across multiple nodes. This means that you can safely rule out the need to purchase and maintain custom hardware which can be very expensive. The fact that Hadoop also indexes and maintains a track of that data it actually enables big data analysis and processing more effectively than it could be done previously.
On the other hand, students seeking a Spark Training which is a data processing tool operating on distributed data collections and not sporting the distributed storage framework. Spark endorses the reuse of the data on distributed collections in an arrangement of application. While Hadoop stores the data on a disk, the data storage in Spark is in-memory, its primary concept being RDDs or Resilient Distributed Datasets which offer efficient and fault-tolerant disaster and recovery management mechanism across a range of clusters.
However, answering the dominant question about which Big data tool should be used for better business and organizational processes and it cannot be answered without addressing a few key technological disparities between the two frameworks and why they need to be considered by aspirants looking a certification. If you expect the difference and comparison to reveal which one is better than the other then you might as well be disappointed with the discussion which is about to follow because rather than helping you choose one over the other it will actually help you select one that best suits your requirement.
Both Hadoop and Spark are good when used together but they can also be used separately. It is the MapReduce that Hadoop possesses HDFs, also known as the heart of Hadoop, which is responsible for performing all the necessary computations across its cluster. MapReduce also has data processing in its hands which means that for data computations enterprises can go without introducing Spark to its framework. Correspondingly, you can also implement Spark without opting for HDFs and MapReduce. Even though it lacks a built-in system of data management, Spark would still manage to operate without Hadoop. And if at all required, it can totally utilize the other cloud-computing platforms.
Compared to MapReduce, the capability of Spark for handling tasks of data processing including machine learning and real time data streaming is a lot faster and speedier. Hence, as a student of the Spark Training if you are wondering what is the reason for the upsurge it is definitely the in-memory data operations coupled with impeccable fast speed. The moment the data is captured it is fueled into an application for analysis and for further actions the user is then provided with the valuable information via dashboard. The fact that Spark at once performs all data analytics single-handedly is a huge advantage with this framework.
On the other side, you have Hadoop Training which has MapReduce writing all the data back to its physical storage disk every time a data operation is completed. This could no doubt make the process seem a lot lengthier comparatively as it is undeniably time consuming. With Spark you may be getting the data processing done at a speed which is ten or hundred times faster, it is still the Hadoop which is widely considered as the best big data platform to opt for where the requirements for information and data are static since it conducts the most productive and cost-effective data processes. However, if you are dealing with dynamic business and data requirements then it is best to go for Spark.
Again, another considerable advantage Hadoop provides over Spark’s speed is where the data size exceeds the memory in which case Spark would be incapable of extracting its cache to the extent that the processing on Spark gets slower than batch processing. In conclusion it can be said, that the comparison between the big data tools points towards something more than competition and indicates that they are capable of complimenting each other. It has often been observed though that the advanced processing power and reliability of Hadoop is often chosen over the speedier Spark.
Now, if are looking for a Spark or Hadoop Certification, then there are number of courses you can choose from. The certificate basically validates an individual’s capacity to deal with big data and validates your ability to handle the components of a framework which are a perfect fit for understanding big data processing lifecycle. The training prepares you to handle various components of big data platforms and there are options for self-study, video lectures and instructor-led training. You can avail a course online or the ones offered by different organizations which even have the option of conducting on-site training. The course is increasingly becoming a lucrative option for individuals today as more companies and even governments are attempting to strengthen their processes and understand ways and means to make life easier for the end-user, by identifying patterns and behaviors, through analysing big data. All major businesses from banking, insurance, telecommunication, retail, food industry, e-commerce, shipping, logistics, etc., utilise big data framework to improve processes, reduce losses and increase overall profits.