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, the utmost lively Apache project at the moment across the world with a flourishing open-source community known for its ‘lightning-fast cluster computing,’ has surpassed Hadoop by running with 100 times faster speed in memory and 10 times faster speed in disks.
Check out the video on PySpark Course to learn more about its basics:
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 and secure apps on Hadoop. Spark comes with a Machine Learning library, 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 much lesser in Spark. Here’s the example:
.flatmap(line => line.split(“ “))
Use Cases of Apache Spark
For every new arrival of technology, the innovation done should be clear for the test cases in the marketplace. There must be proper approach and analysis on how the new product would hit the market and at what time it should with fewer alternatives.
Now when you think about Spark, you should know why it is deployed, where it would stand in the crowded marketplace, and whether it would be able to differentiate itself from its competitors?
With these questions in mind, go on with the chief deployment modules that illustrate the uses cases of Apache Spark.
Want to explore more? Read this extensive Spark Tutorial!
Apache Spark is easy to use and brings up a language-integrated API to stream processing. It is also fault-tolerant, i.e., it helps semantics without extra work and recovers data easily.
This technology is used to process the streaming data. Spark streaming has the potential to handle additional workloads. Among all, the common ways used in businesses are:
- Streaming ETL
- Data enrichment
- Trigger event detection
- Complex session analysis
Read our blog to avoid errors during Spark application development on Mistakes to Avoid While Writing Apache Spark Applications.
There are three techniques in Machine Learning:
- Classification: Gmail organizes or filters mails from labels which you provide and filters spam to another folder. This is how classification works.
- Clustering: Taking Google News as a sample, it categorizes news items based on the title and the content of the news.
- Collaborative filtering: Facebook uses this to show users ads or products as per their history, purchases, and location.
Spark with Machine Learning algorithms helps in performing advanced analytics which assists customers with their queries on sets of data. It is the Machine Learning Library (MLlib) that holds all these components.
Machine Learning capabilities further help you in securing your real-time data from any malicious activities.
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- Spark provides an easy way to study APIs, 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 considered to be slow. Hence, with Spark, it is fast to perform any identification queries against live data without sampling.
- Structured streaming is also a new feature that helps in web analytics by allowing customers to run a user-friendly query with web visitors.
- Fog computing runs a program 100 times faster in memory and 10 times faster in the disk than Hadoop. It helps write apps quickly in Java, Scala, Python, and R.
- It includes SQL, streaming, and hard analytics and can run anywhere (standalone/cloud, etc.).
- With the rise of Big Data Analytics, the concept that arises is IoT (Internet of Things). IoT implants objects and devices with small sensors that interact with each other, and users are making use of it in a revolutionary way.
- It is a decentralized computing infrastructure where data, compute, storage, and applications are located, somewhere between the data source and the cloud. It brings the advantages of the cloud closer to where data is created and acted upon, more or less the way edge computing does it.
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To summarize, Apache Spark helps calculate the processing of a large amount of real-time or archived data, both structured and unstructured, without anything being held or attached. It’s linking appropriate complex possibilities similar to graph algorithms and Machine Learning. Spark brings the processing of Big Data to a 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 in real-time interactions such as video streaming and many other user interfaces, thus, maintaining the constant smooth and high-quality customer experience.
To understand the fundamentals of PySpark, you can read our immersive PySpark Tutorial!
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