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What can Spark do? Spark application field

Spark is the king of data computing and processing in big data's technology. It can process PB-level data at a time, distributed in thousands of collaborative physical or virtual server clusters. It has an extensive developer library and API, and supports languages such as Java,Python,R and Scala. Its flexible features are suitable for a variety of environments. Here are the two most common Spark application scenarios:

Offline scenarios: we can use time as a dimension, a dataset of several years, or a business as a dimension, a large dataset of a certain domain, and so on. This kind of data is generally called offline data, or cold data.

Real-time scenarios: buried sites on the website, real-time data transmitted from the front-end pages, real-time data transmitted from business systems or physical hardware, hardware signals or image data, etc., need to be processed in real time and return the resulting data.

Spark is designed for data science, and data scientists incorporate Spark into their applications that can handle interactive queries across large data sets, streaming data from sensors or financial systems, and machine learning tasks, and Spark's ability to cache data sets in memory greatly speeds up iterative data processing, making Spark an ideal processing engine for iterative MapReduce algorithms.

Spark is designed for engineer big data. In the face of strong computing power and excellent architecture design, data engineers can safely choose to use Spark in both offline scenarios and real-time business requirements.

Spark is developing rapidly because of its own advantages. At present, almost all one-stop big data platforms have integrated Spark, and many industries are using Spark to improve their business. Here are the specific uses of Spark in some industries:

Insurance industry: optimize the claim reimbursement process by using Spark's machine learning capabilities to process and analyze all claims.

Healthcare: use Spark Core,Streaming and SQL to build a patient care system.

Retail: use Spark to analyze point-of-sale data and coupon usage.

Internet: use Spark's ML feature to identify fake profiles and enhance the product matches they present to customers.

Banking: machine learning models are used to predict the retail data of some financial products.

Government: analysis of geography, time and fiscal expenditure.

Scientific research: predicting future events by analyzing seismic events through time, depth, and geography.

Investment banking: analyze intraday stock prices to predict future price movements.

Geospatial analysis: Uber travel is analyzed by time and geography to predict future demand and pricing.

Twitter emotion analysis: analyze a large number of tweets to determine the positive, negative or neutral emotions of a particular organization and product.

Airlines: establish models to predict air travel delays.

Equipment: predict the possibility that the building will exceed the critical temperature.

Spark is an important framework technology in big data's technology stack. It is a fast and general computing engine specially designed for large-scale data processing. It can support iterations on distributed data sets and supplement Hadoop. It is an ecosystem with rapid development and wide application.

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