Is Spark Streaming Notoriously Difficult?

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Apache Spark is modern, high-tech and simple tool for big data processing. The tool stepped into the market a few days back and since then, it has created a huge impact in the big data industry. It is much more than just a general-purpose data processing model. Rather, it known worldwide for its superbly high speed. Also, it is known as a very strong open source tool which is known for cluster computing. There are tons of reasons that make Apache Spark Implementation a preferred choice of the world. It is known across the globe because of its unique data processing feature.

However, amongst all the positives and advantages of analyzing data, there are certain shortcomings or disadvantages related to Apache Spark as well. And, there have been a few discussions surrounding the difficulty of data streaming as well. Though, the tool has gained a lot of appreciation for its superb processing function. But, there were some rumors containing information that the streaming of the data is not as perfect as it should have been. Rather, there are some drawbacks or shortcomings related to the Apache Spark streaming. And, there are a few other disadvantages of using Apache Spark as well, and a few of them are highlighted below:

Real time processing is not well supported

The data which is poured in the system is segregated in different batches of pre-defined interval. And, every each lot of the data is considered as a RDDs or Apache Spark’s Resilient Distributed Database. Later on this Apache Spark’s Resilient Distributed Databases are processed with the help of various tasks like join, map etc. And, the outcome of all of the functions is received back, but in different batches. Therefore, we can say that Spark aids the micro batch processing of the data.

Writing stream processing tasks is not a cakewalk

Apache Spark Streaming normally provides restricted libraries of streaming operations. These activities are called programmatically with the aim to perform aggregation and computations on the data which is pouring in. Therefore, the users will have to write a code with the help of a programming language like Java, in order to create a streaming analytics app by implementing Apache Spark. As, Java is not as compressed as SQL, thus, when it comes to the processing of data streams, it is all the more time consuming. As, simply, more amount of coding is required. Additionally, there is a lot of memory space which is taken by these languages as well.

The 100x quicker in memory or 10x faster on disk projects than Hadoop is what Apache Spark lets you run. The other significant facet of Deep Learning with Apache Spark is the intelligent shell which it gives out-of-the box.

Apart from the possible streaming issues, there are a few of the other issues related to Apache Spark as well, and one of them is the absence of a decent file management system. As, Spark does not have a dedicated, FMS, therefore, it has to automatically rely on some of the other data processing platforms or any other cloud-based tool. Sometimes, they are even dependent on Hadoop. And, this is definitely considered as one of the issues with Spark. However, the platform is still one of the best in the business as it offers tons of advantages and there is no doubt about the fact that the chances of its success are very high.

The in-memory process utilization by Apache Spark provides quick and simple way to run intelligent analytics on massive datasets. The Apache Spark Implementation proficiency helps government and research organizations precisely to search, analyze, visualize, and process enormous datasets. The Spark Streaming supports continuous processing of data streaming like creation of web server log records (e.g. Apache Flume and HDFS/S3). Under the hood, the information streams will be received by Spark Streaming and splits the data into clusters.

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