- 1. Objective
- 2. Apache Spark use cases
- 2.1. Some industry-specific Apache Spark use cases
- 2.2. Chief deployment modules that prove use cases of Apache Spark
- 3. When NOT to Use Spark
- 4. Conclusion
As we know Apache Spark is the fastest big data engine, it is widely used among several organizations in a myriad of ways. There are ample of Apache Spark use cases. In this article, we will study some of the best use cases of Spark.
However, we know Spark is versatile, Still, it’s not necessary that Apache Spark is the best fit for all use cases. Hence, we will also learn about the cases where we can not use Apache Spark.
2. Apache Spark use cases
2.1. Some industry-specific Apache Spark use cases
a. Spark use cases in the Finance Industry
Mostly, Banks are using the Hadoop alternative – Spark. It helps to access and analyze many of the parameters in Bank Sector. For Example, the social media profiles, emails, forum, call recordings and many more. Moreover, it gains insights that help to make right decisions for several zones. Like credit risk assessment targeted advertising and customer segmentation.
b. Apache Spark use cases in e-commerce Industry
In E-Commerce, it helps with Information about a real-time transaction. Those are passed to streaming clustering algorithms. Such as alternating least squares or K-means clustering algorithm. It also helps to enhance the recommendations to customers based on new trends. Some Real-time examples like Alibaba, eBay using Spark in e-commerce.
c. Apache Spark use cases in Healthcare
Healthcare sector is one of the most developing sectors nowadays. These people always look for ways, that will help to enhance the quality of healthcare. Hence, Spark is slowly becoming the part of many healthcare applications. One of the best use of Spark in Healthcare is the analysis of patient records along with past clinical data. It helps to identify which patients are likely to face health issues later on. This step prevents hospital re-admittance. Since it is possible to deploy home services to the identified patient now. Also, saves costs for both the hospitals and patients.
In addition, to reduce the processing time of genome data, Spark is used in genomic sequencing. Before Spark, it took several weeks to organize all the chemical compounds with genes. Now, with Spark, it takes few hours. Although it doesn’t come under real-time use of Spark. Since it is a benefit to researchers over the earlier implementation of genome data. One of the best examples of the company which is using Spark is MyFitnessPal.
d. Apache Spark use cases in Media & Entertainment Industry
In the gaming, we use Spark to identify patterns from the real-time in-game events. It helps to respond in order to harvest lucrative business opportunities. For Example targeted advertising, auto adjustment of gaming level complexity, player retention etc.
In addition, some video sharing websites using spark along with MongoDB. It helps to show relevant advertisements to its users based on the videos they view, share and browse. Some Real-time companies which are using Spark are Yahoo, NetFlix, Pinterest, Conviva etc.
e. Apache Spark use cases in Travel Industry
Travel Industries are using Apache Spark rapidly. It helps users to plan a perfect trip by speed up the personalized recommendations. They also use it to provide advice to travelers by comparing many websites to find the best hotel prices. Also, the Review process of the hotels in a readable format is done by using Spark.
In addition, some apps using Spark to provide us a platform for online reservation(real time). They are using Spark to manage ample of restaurants and dinner reservations at the same time. The speed achieved by them is only possible by using Apache Spark. Reduction in run time of machine learning from few weeks to few hours resulted in improved teamwork. Some of the best Examples in this section are TripAdvisor and OpenTable.
2.2. Chief deployment modules that prove use cases of Apache Spark
a. Data Streaming
Spark brings up language-integrated API to stream processing. That is is easy to use. Also, fault tolerant in nature. This feature helps semantics without extra work and recovers data out of the box. Basically, we use this technology to process streaming data. It has potential to handle the additional workload. Between all that, some common ways used in business are:
- Streaming ETL
- Data Enrichment
- Trigger event detection
- Complex session analysis
b. Machine Learning
Basically, there are three techniques for Machine Learning. They are:
1. Classification :
To understand classification, let’s take a real-time example, gmail. It bifurcates mails within labels that we provide. Also, filters spam to another folder. It is the process of Classification.
2. Clustering :
To understand clustering, let’s take an example of google news. It bifurcates on the basis of title and content of news.
3. Collaborative Filtering :
To understand Collaborative Filtering, let’s take Facebook as an Example. It shows users ads or products from their history, purchases, and location.
In addition, one of the good business parts of ML capabilities is Network security. It helps security providers to investigate in real-time. Even for any clue of malicious activity.
c. Interactive Analysis
For interactive data analysis, Spark provides an easy way to study API. Also turned as a strong tool. There are two programming languages in which it is available. Such as Python or Scala. There is a new feature known as Structured streaming. It helps in web analytics by allowing customers. Also runs a user-friendly query with web visitors
d. Fog Computing
In case of Memory, it runs program 100 times faster than Hadoop. Also, in case of disk, it runs 10 times faster. That helps to write apps quickly in several languages. Such as Java, Scala, Python, and R. It incorporates Streaming, SQL, hard analytics within that can run everywhere. At the rising time of Big Data Analytics, the new concept IoT(Internet of Things) arises. It implants devices with small sensors that interact with each other. Moreover, Users are making it revolutionary. It decentralized storage and data processing.
3. When NOT to Use Spark
However, we know Spark is versatile, still, it’s not necessary that Apache Spark is the best fit for all use cases. Moreover, we can say, Spark was not created as a multi-user environment. It is very important to know whether the memory they have access is sufficient for a dataset. Since it is possible that adding more users complicates to run projects concurrently. As spark is incapable to handle this type of concurrency. Hence, users consider an alternate engine for this. Such as Apache Hive, for large, batch projects. Learn more about limitations of Apache Spark.
As a result, we have seen all the top use cases of Apache Spark. Basically, Apache Spark is used in many notable business industries as mentioned above. Moreover, these companies gather terabytes of event data from users. Also, engage them in real-time interactions. For example, video streaming and many other user interfaces. Thus, it maintains the constant smooth and high-quality customer experience.
Furthermore, if you know any other top uses of Spark, feel free to share, in the comment section.