5 Invincible Reasons to use Spark for Big Data Analytics?
For few years until Spark came in a big way, Hadoop was the most dominant engine for big data processing. In many respects Hadoop excelled. First of all, Hadoop Distributed File System or HDFS has been considered as the right Big Data storage platform. Secondly, as resource allocation framework YARN has been the best choice in Big Data environment. Lastly, MapReduce offered quite a powerful analytics technology except a few negative aspects.
Enterprises using Hadoop and heavily relying on it for their analytical infrastructures were satisfied with it though there has always been interest for a breakthrough technology that can give them a better performance. More advanced capabilities continued to become crucial for businesses but they found Hadoop not fully capable to address all these evolving requirements. From data preparation to descriptive analysis and search to predictive analysis to machine learning and graph processing, businesses were in mood for an advanced engine that can handle all these and even more. Thus when Spark arrived, enterprises all of a sudden found their dream solution to all the requirements mentioned above. Spark is the single data processing framework that just fits all the criteria earlier were unthinkable with other frameworks and processing engines. Let us have a look at the 5 most invincible reasons to embrace Spark as the most advanced data processing technology so far.
- Advanced analytics
- Extreme ease of use
- Support to several programming languages
- Scalability and acceleration with Spark
- Spark is vendor neutral
Innovation is driving companies to search for more advanced analytics capability. Capability for deploying advanced analytics across the business has now become the criterion to beat competitiveness. Simple preparation of data and basic analytics will no longer be enough to stand in the league.
Spark offers an advanced analytics framework that is equipped with all attributes to guarantee optimum outcome. From offering a tool for accelerated queries to making machine learning library available to boosting decision making with a graph processing engine to offering the real time advantage through streaming analytics engine, Spark offers all that one needs for advanced analytics. While implementing all these analytics through MapReduce is akin to impossible, Spark not only facilitates analytics driven towards these outcomes but it also makes it easier and faster with prebuilt libraries. By machine learning capability Spark can take care of many analytical tasks like preparing data and quality control. Naturally, while advanced analytical outcome is ensured data scientists can be much relieved.
Hadoop for a longtime has been criticized for its complexity and lack of ease in several aspects. Though Hadoop continued to evolve as more simple and powerful with every update, the criticism on complexity is still there. It has been a consistent requirement for Hadoop users to understand an array of complexities like Java and MapReduce programming languages. In this respect Spark is quite different. It has been designed in such a way that it can easily be accessed by anyone with only an understanding of databases and some amount of scripting knowledge. Thus it offers a very low learning curve and makes it immensely easier for businesses to deploy this analytics engine.
Support for several developer friendly programming languages is a great attribute of Spark. Spark allows developers to quickly write applications in several programming languages including Java, Scala or Python. This not only allows developing and running apps in familiar languages but this wider language support also offers the advantage of rapid development as well. Moreover, for the sake of querying data interactively just within the shell there is a built-in set of 80 high level operators.
With the business growth and scalable needs of analytics and real time needs, faster acceleration of analytical framework seems crucial. By providing parallel in-memory processing Spark accelerates the process even faster than so called sequential approach that can only take a job when the earlier is finished. Instant results provided by Spark not only eliminate delays making analytics faster but can also make resource utilization better. By leveraging Spark to build advanced data applications an array of dramatic improvements are being observed in the analyst workflow. The acceleration in the turnaround time for answers to queries indicates to the iterative process undergoing o make answers more precise and complete. Thus Spark let the analysts find not only fast answer but better answers as well.
Spark works just the same irrespective of the Hadoop vendor used for it. It is basically a vendor-neutral data engine that offers optimum flexibility as far as choice of providers is concerned. Being open source project it allows businesses to create Spark-based analytics infrastructure. But they do not need to be concerned on the Hadoop vendor compatibility.
We must agree equivocally that the wide spread popularity, acclamation and huge developer support Spark has achieved in such a short time tells the potential of it as an era changing data processing technology. While the coinage of such terms like the “age of Spark” has already been popular, how businesses can deploy Spark to take the advantage of robust analytical potential of it is still to e seen.
