5 Reasons Why Adoption of Apache Spark Is Inevitable in The World of Big Data

5 Reasons Why Adoption of Apache Spark Is Inevitable in Big Data

In the times when big data is the single biggest asset of any business, enterprises are now trying to get more from the data. As the expectations from the data steadily grow, so are the requirements out of big data tools and frameworks.

Enterprises aren’t just satisfied with the usual analysis of data that gives direct insights into why and how the incident happened. Instead of understanding the reasons behind the past information, enterprises are looking forward more about knowing what’s in store for the future and the only way to predict it is through big data analysis.

Hadoop was one of the popular software for big data analytics at the beginning but now Apache Spark has won over it in terms of processing speed, predictive analysis and quick handling of data. Apache Spark is considered to be the go-to choice for big data analysis by many top companies in e-commerce, gaming industries, financial services, and online service providers. Some experts speculate that there is much potential in developments for Spark users in the near-future even during the situation where Spark is already leading the big data revolution.

There are several reasons why Apache Spark is the forerunner in big data analytics and the why likes of Amazon, Alibaba, eBay, Yandex, Yahoo!, Baidu, Hitachi Solutions and several other global enterprises are adopting it. Here are some of the top few.

Capture Insights from Perishable Data Fast

Not all data we collect can be analyzed slowly and systemically in the order they come in. There are specific data which loses its value in a short time. To save such collected data from going futile, it is crucial to analyze it fast and get the maximum usable insights out of it. This is one area where the Resilient Distributed Datasets (RDD) of Apache Spark makes all the difference.

RDD is a generalized data structure that is suited for in-memory clustering. One can make partitions across RDD and use different partitions for storing separate data to ensure that the memory is not crowded. This gives way to an optimal and fast memory processing speed of Spark that helps data scientists to get the insights they require at the shortest possible time.

Combining Machine Learning with Big Data

Machine learning and big data go hand-in-hand and using two tools for handling both separately screams inaccuracy. Apache Spark has MLlib, the machine learning library, comprising of several toolsets that can be used to create and run a machine learning operation.

Massive Machine Learning Automation (MMLA) is the future of the combination of machine learning and big data and Apache Spark has the required resources to set its precedent. Instead of going through the separate processes of coding, processing and preparing the data for analysis, this can ensure that the entire process is automated from the beginning.

Simplify and Streamline Workflows

Apache Spark helps in creating an analytical workflow that can be run in a separate batch or in real-time. The coding for the workflow can be written in any of the three programming languages – Java, Python or Scala. Spark can easily access the data from the Hadoop and other Apache counterparts and incorporate it into the workflow seamlessly swiftly with minimum consumption of resources.

Spark has various resources that help to streamline the workflow process. For example, Spark Streaming is a Spark API extension that can integrate real-time data from events.

Valuable Customer Insights Through Advanced Analytics

Unarguably, providing excellent customer experience is at the top of every enterprise’s goal. Apache Spark helps in learning the characteristics and behavior of individual customer segment, identify patterns in the usage, detect the requirements and the desires of the customers and help in creating a wholesome customer experience in both online and offline businesses.

Apache Spark enables to obtain a comprehensive overview of everything a business needs to provide better service to its customers. From performance analysis to prescriptive analysis, Spark discovers insights that are worth investing for.

The Growing Community of Spark

As Apache Spark is an open source framework for big data, it naturally has an online community where various developers and data scientists collaborate and keep improving the platform’s usage and applications. The open community of Apache Spark is exponentially growing in the past few years and this has become an integral part of the development of the tools and libraries of Spark. Many new algorithms are continuously being developed by the members of Spark community which further takes the platform a step forward in the race of growing big data and the capturing of useful insights.

How Big Data Analytics Helps In Delivering More Value to Your Customers

With Apache Spark supported in all sides and combined with the enormous volumes of data collected, it is unavoidable for companies to rely on it for its scalability and advanced big data analysis. With the current impressive results that Spark provides, enterprises are hoping to get more such developments to support the pace of the data growth.

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