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Spark’s Role in Big Data Ecosystem in 2017

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Is Spark going to be the new revolution in the field of data analysis? Though the answer won’t be a simple, it needs a proper research on the topic and a thorough understanding of the subject. Spark alone can’t be the future of something so let’s turn the question in a better direction. The question should be how much important can Spark big data analytics be in the future of data analysis? At the beginning of 2014 when he was first released it was thought of an extension of Hadoop, but as days passed by now, people realized that it could also be used as a standalone framework. And now it has become one of the preferred big data platforms for many enterprises.

The most important components of Spark big data are:

1. Core: The main processing engine 2. Streaming: Can provide real-time analytics in high-speed GraphX: Can show the analysis in graph form for better visualization of the data MLib: It’s the library for machine learning and is faster than other Apache ML frameworks.

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Let’s see why Spark database has a bright capability in the field of data analysis.

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1: Scalable and Open Source

If someone says, Spark has no future then ask them to show an alternative to that one can use Presto for SOL, for streaming Storm and Giraph for graph-related problems. But using these things all at a time and connecting them is a tough task. Thus, use Spark for data wrangling.

2: Small data chunks

Most of the data scientist love to work with small datasets so that they can fit those into a single machine all at once. It’s like asking someone to write my paper for me – too easy. The main dataset can be in gigabytes or terabytes, but the analysis datasets are small. Not all of the data warehouses are clean and well organized so at some time sooner or later you have to get your hands dirty. And if you need to write the best algorithm for your machine learning than serially running test can take years. Thus, distribute them in Spark cluster, and it will radically reduce the complexities making it optimal.

3: Ever increasing features

Spark is evolving daily as the developers are working every minute to make it more efficient to compete with the traditional frameworks. They already have included the support for SOL and streaming capabilities. Spark now also supports Python and many API of R.

4: More cool stuff

The core Spark team is working on many major projects which will increase the boundaries of Spark evermore. First one is CLIPPER which will have Spark ML and some Python functions and MLLib libraries. Second is Drizzle which is a group scheduling algorithm to reduce high latency and repetitive operations. Other ones are Opaque and Ray. They also have three Apache projects named Apache Hivemall, Apache PredictionI0, and Apache SystemML going on

5: Better workload

Spark can handle better-sophisticated workload models than its competitors. Hence it has lowered the need for data governance.  Spark has enterprise-level security architecture.

In the survey of data architects, most of the percentage nearly 70% of them chose Spark over Hadoop MapReduce. The main reason was Map Reduce is batch-oriented and doesn’t support real-time processing. Whereas Spark is better than that regarding user-friendliness and integration. Let’s have a look at the pros and cons of it.

Pros: Compatibility with Java, Scala, R, Python means you can choose any platform you want. It is fast as it operates on RAM Vast and flexible API It is compatible with Hadoop YARN and can use Hadoop functions. Open source so not expensive like others.

Cons: The main con till now is it doesn’t have a real layer for storage. So for that, you have to use other cloud storage platform.

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Though Apache Spark is a new kid in the field of data analysis with its growth rate, it is hard to predict future. It is not only an easier way out for data analysis but also allows a diverse range of ways to solve complex problems from structured graphs to queries computation. Spark allows developers and data scientists to concentrate more on their logic and less on the speed and integration of APIs. Spark is going to make the life of developers easier in future, and there is no doubt about that.

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