Apache Spark has gained immense popularity over the years and is being implemented by many competing companies across the world. Many organizations such as eBay, Yahoo and Amazon are running this technology on their big data clusters.
‘Spark’, utmost lively Apache project at the moment across the world with flourishing open source community known for ‘lightning rapid cluster computing’. Spark has passed Hadoop by running 100 times faster speed in memory and 10 times faster speed in disks.
Before exploring the Spark use cases, we must learn what is Apache Spark all about?
Spark has originated as one of the strongest big data technologies in a very short span of time. As it is an open source substitute to MapReduce associated to build and run fast as secure apps on Hadoop. Spark comes with a library of machine learning and graph algorithms, and real-time streaming and SQL app, through Spark Streaming and Shark, respectively.
For instance, a simple program for printing “Hello World!” requires more lines of code in MapReduce but requires much lesser lines of code in Spark. Let’s have a look at it:
.flatmap(line => line.split(“ “))
Use Cases of Apache Spark
For every new arrival of technology, innovation done should be clear for the test cases in the marketplace. There must be a proper approach and analysis the new product hits the market at the right time with fewer alternatives.
Now when we think about spark, we get why it is deployed. Where will it stand in the crowded marketplace? Will it be able to differentiate from the competitors?
With these questions, here are the chief deployment modules that prove uses cases of Apache Spark:
Want to explore more? Read this extensive Spark Tutorial!
Apache Spark is easy to use which brings up with language-integrated API to stream processing. It is also fault tolerant i.e. helps semantics without extra work and recovers data out of the box.
This technology is used to process Streaming data. Spark streaming has the potential to handle the additional workload. Among all, the common ways used in business are:
- Streaming ETL
- Data Enrichment
- Trigger event detection
- Complex session analysis
There are three parts of techniques for machine learning:
- Classification : Gmail organizes or filters mail from labels which we provide and filters spam to another folder. This is how classification works.
- Clustering :Taking Google news as a sample, it categorizes based on title and content of news.
- Collaborative Filtering :Facebook uses this to show users ads or products from their history, purchases and location.
Spark with Linked framework for performing advanced analytics which helps customers repeated queries on sets of data, which results to processing machine learning algorithms. Components found are in Machine Learning Library (MLlib).
Network security is a good business part for ML capabilities. Utilizing these components, security providers can investigate on real-time data packets for any clue of malicious activity.
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- Spark provides easy way to study API and also it is a strong tool for interactive data analysis. It is available in Python or Scala.
- MapReduce is made to handle batch processing and SQl on Hadoop engines which are usually slow. Hence it is fast to perform any identification queries against live data without sampling and highly interactive.
- Structured streaming is also new feature that helps in web analytics by allowing customers run user-friendly query with web visitors.
- Fog computing runs program 100 times faster in memory than Hadoop or 10times in case of disk. It helps to write apps quickly in Java, Scala, Python and R.
- It includes SQL, Streaming and hard analytics and can run Everywhere(standalone/cloud, etc.).
- When this Big Data analytics rising, the concept that arises is IoT(Internet of Things). The IoT implants objects and devices with small sensors that interact with each other and users making it revolutionary.
- Fog computing decentralizes data processing and storage. It amounts to rise with low latency, extremely difficult graph analytics and large amount of together processing of machine learning.
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To summarize, Apache Spark helps to shorten the challenging and calculate intensive work of processing large amount of real-time or archived data, both structured and unstructured, without anything held or attached, it’s linking appropriate complex possibilities similar to graph algorithms and machine learning. Spark brings processing of Big Data to large quantity.
In real time, Apache Spark is used in many notable business industries such as Uber, Pinterest, etc. These companies gather terabytes of event data from users and engage them with real-time interactions such as video streaming and many other user interfaces. Thus, maintaining the constant smooth and high quality customer experience.
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