Have you ever wondered how to use big data to your advantage? If so, you’re not alone! Millions of people are turning to big data and its many uses for everything from discovering relationships to predicting market behavior. The promise of big data has created a new wave of entrepreneurs aiming to capitalize on the new technology. But how to make the most of big data and what tools should you use?
Big Data has been around for decades but only recently has it started to reach its full potential. Basically, big data describes large, aggregate data sets which can be studied together to reveal trends, patterns, and relationships. Although there’s no defined threshold that separates “big data” from traditional data analysis, big data is usually believed to be too complex and slower to be useful in most traditional business applications. New applications based on artificial intelligence and machine learning have been developed as a way to tap the benefits of this new architecture.
Apache Spark is one of the most popular machine learning frameworks and was originally developed at Google to help data analysts to exploit the capabilities of the Hadoop framework. Like Hadoop, Spark is designed to scale up using large data sets and run on larger machines than those used by traditional data analysis applications. This enables Spark to provide real-time results. This article will discuss some of the tools developers use to leverage Spark.
Apache Spark is designed for big data with the help of traditional Data Mining techniques. Traditional Data Mining techniques involve the use of complex algorithms to extract useful information from large amounts of unstructured data. With traditional Machine Learning, an analyst would need to code the machine learning algorithm itself which may not be feasible. Spark has the ability to leverage the parallel processing capability of the Hadoop framework and make it possible for analysts to code the same algorithm that can easily be executed on a cluster of machines. In the case of Hadoop, there are a number of high-level libraries that allow programmers to create applications leveraging on the Hadoop framework without the need to write the algorithm from scratch. Because of the parallelization capabilities of Spark, these developers can easily apply the same machine learning techniques to their applications running on clusters of machines.
While Hadoop is good at handling large volumes of unstructured data efficiently, there are limits with regards to the memory, storage, and bandwidth of the devices running Hadoop. Spark is an open source project based on the Hadoop framework that has been designed specifically to scale up from the small clusters originally used by big data analysts. By using the Spark streaming language, data analysts can now access the large structured data stored on the mainframe even when the machine is turned off. As a result, the performance of the machine continues to remain optimized while the analyst’s work on the real problem.
Data analysts can also benefit from Hadoop’s ability to enable easy ML decomposition and machine learning. Both of these processes, when combined with applications written in the Spark programming language, have the potential to dramatically reduce the time it takes researchers to conduct live experiments, such as those performed on social media systems. Spark is good at both analyzing and synthesizing large quantities of data, allowing it to quickly analyze and map the relationships between real entities. The speed of this process makes it ideal for data analysis on social media networks such as Twitter and Facebook, especially when it comes to exploring complex relationships involving a number of different people.
Another way how to use big data analytics is to build large-scale data warehouses. Data warehouses are constructed using multiple large data sources, allowing analysts to apply standard logical rules to the information within the data warehouse. This allows the data analysts to build business rules that will maximize the profitability of a business according to their specific needs. In contrast to analysts who analyze smaller sets of information, data analysts must analyze the complete set of data. Standard logical rules may not be enough to make sense of the data and many times the entire set of data may not be useful.
Using data to improve the performance of your business means being able to understand your target audience and providing them relevant content that they will find valuable. Social media is a great platform for content strategy because it allows you to reach your target audience directly. However, content that is highly promotional will only attract a small target audience. Hadoop, however, can help you build a large database of customers, along with providing you with insights into your target audience’s preferences. By combining Hadoop’s Map-reduce capacity with Spark’s horizontal logic capability, you can easily combine streaming data with analytical processing power to create the perfect storm of data that can improve the performance of your business.