Real-time stream processing by Apache Spark: Apache Spark was basically developed to surpass the limitations that some of the other data processing programs or tools had. It is regarded as one of the best data processing solutions in the world at present. It is developed with a lot of strategy and planning.
Basically, a lot of planning was done in order to make sure that Spark solves the existing data processing problems. There are a host of big sized companies that are using Apache Spark Solutions at the moment, like Netflix, Amazon and a lot of other multinational firms. It is regarded as one of the most advanced real-time data analytic tool which is almost indispensable.
A few of the top reasons why Apache Spark is adopted include the fact that it is used for much more than just disk-based processing. Also, programming languages other than java can also be used in order to build the apps, whereas, some of the other top data processing tools, like Hadoop MapReduce, only use Java.
At the same time, the security benefits that Apache Spark offers are also huge, thus, cyber-attackers can’t exploit it that easily. However, the most astounding advantage of using Apache Spark is that it is used for real-time stream processing.
Let’s explore stream processing
Big Data is used almost everywhere, and there is no doubt about the fact on a constant basis, latest techniques and tools are being introduced in order to make data processing and analysis all the more convenient.
One of the latest inventions in the world of big data processing is stream processing. Basically, stream processing is all about processing the data which is in motion. Stream processing is the technique of computing on data straightaway, exactly when it is generated or received.
There is a host of data which is generated in continuous streams, for example, the data which is generated by the sensor events. Also, the data which is generated by the user behavior on a website is also generated in continuous streams.
At the same time, data which is generated by the financial trades are also in series. However, streaming is the second step. The first step is to store the data in a safe location. After the data is stored then only streaming can be done.
Stream processing is known by various names, including, intricate event processing, real-time analytics, event processing, streaming analytics etc. With the help of stream processing, valuable insights can be generated from big data.
Stream processing is meant to aim make the most of the insights. Basically, with the help of stream processing, the companies can get the insights a lot quicker. At the same time, the insights can be generated in a more structured manner.
Apache Spark and stream processing
Apache Spark is known for stream processing. Data streaming is nothing but the limitless sequence of data which is generated or received on a consistent basis. Apache Spark ensures that the stream processing segregates the consistently flowing data into distinct units, in order to enable data processing.
Stream processing makes the process of analyzing the data and using insights a lot more convenient. Basically, Apache Spark is known widely for Spark Streaming. Spark enhances the scalability and it also leads to high-throughput. At the same time, with the help of stream processing, the speed of data processing is increased along with the efficiency of the processing.
With the help of Spark streaming, data can be streamed in different batches. And, almost each and every batch consists of a pool of events which have been received over the batch phase. And, this is irrespective of the fact that when the data was actually created. Apache Spark makes time-series analytics possible.
Apache Spark is used extensively by a lot of companies across the world. Basically, the main reason why Apache Spark is used is that the solution offers much more advanced data processing solution.
Apache Spark is known for stream processing, which is the process of streaming continuous streams of data. Thus, the overall process of data streaming is boosted and also the efficiency and the rate of the operations are increased because of stream processing. Apache Spark is stepping up the popularity curve, especially because of stream processing, which is definitely the best feature offered by this big data solution.