Apache Hadoop Development

 

Retail companies across the globe have focused on reaching out to audience and understanding their preferences. It is all about anticipating changes in customer behavior and developing consumer-centric solutions.

With the rapidly increasing competition and growth of diverse new channels, the retail industry is now focusing towards developing their distinctive identity by leveraging both physical and digital footprints. The focus is now towards creating an omnichannel experience for consistent user experience. Traditional retailers are now facing constant pressure to redefine the role of store and develop distinctive capabilities for continued growth.

Several factors such as emerging technologies, technological breakthroughs, social media, demographics and volatile economic environment continue to pose challenges for the retail industry. In such scenario when stakes are high, it is important for retailers to build consumer profiles based on customer’s behaviors using big data analytics.

'Big data' refers to data sets that are too large or too complex for traditional data processing applications. The data produced in the retail industry is growing at a phenomenal pace creating both opportunities and challenges for the retail industry.

Big data in retail is now driving new wave of change creating opportunities for personalized shopping and connecting brands, customers and retailers like never before.

Big Data's Role in the Retail Industry

The goal of Big Data Analytics is to help retailers analyze large data volumes from different sources ranging from website, social media, mobile calls and machine data transformed from connected devices using the Internet of Things (IoT).

Effective use of Big Data analytics can create new avenues of marketing, improve ROI, enhance customer services, improved operational efficiency, gain competitive advantage over rival organizations and other business benefits.

Big Data tools like Hadoop is helping identify user patterns, hidden trends, customer behaviors to find unknown correlations and much more. Let’s understand the importance of Apache Hadoop Development in the retail industry.

Defining Hadoop

Hadoop is an open source distributed processing framework that manages data processing and storage for big data applications running in clustered systems. Hadoop serves as the centerpoint of the rapidly growing ecosystem of big data technologies used to conduct advanced analytics functions and initiatives such as predictive analytics, data mining and machine learning applications.

With Hadoop, business organizations and enterprises can handle large, voluminous structured and unstructured data. This gives users flexibility to collect, process and analyze data than relational databases.

The Hadoop platform was designed to solve problems where you have a lot of data — perhaps a mixture of complex and structured data — and it doesn’t fit nicely into tables.

Hadoop framework stores data and runs applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs.

Components of Hadoop

MapReduce

Apache Hadoop MapReduce framework processes large data sets in parallel across a Hadoop cluster. The term "MapReduce" refers to distinct tasks that Hadoop programs perform. The first is the map job, which converts one set of data into another set of data and second is reduce which is designed to reduce the final form of the clusters’ results into one output

Hadoop Distributed File System (HDFS)

HDFS is one of the key tools in the Hadoop ecosystem as it provides support voluminous big data and support big data analytics applications. Supporting rapid transfer of data between compute nodes, HDFS provides access to data across highly scalable clusters.

Apache Hadoop YARN

Known as “Yet Another Resource Negotiator” however popularly referred to by the acronym YARN reduces dependence on MapReduce and opened up Hadoop to other processing engines and various applications besides batch jobs. It is a new cluster resource management and job scheduling technology that took over those functions from MapReduce.

Big Data Tools Associated with Hadoop

The ecosystem that has been built up around Hadoop includes a range of other open source technologies that can complement and extend its basic capabilities. The list of related big data tools includes:

Apache Flume: Distributed and reliable software to collect, aggregate, and move streaming data into HDFS.

Apache HBase: Open source NoSQL database providing real-time read/write access to large datasets.

Apache Hive: SQL-on-Hadoop tool provides data summarization, query and analysis.

Apache Oozie: Server-based workflow scheduling system to manage Hadoop job.

Apache Phoenix: SQL-based massively parallel processing (MPP) database engine that uses HBase as its data store.

Apache Pig: Platform to create programs that run on Hadoop clusters.

Apache ZooKeeper: Configuration, synchronization and naming registry service for large distributed systems.

How Apache Hadoop Development can Transform Retail Industry

Retail IT solutions leverage the power of analytics for repetitive tasks will be far more efficient and gain competitive advantage in the retail space. Hadoop is helping leverage technology to analyze more data. For instance, it gives the capability to stores receipts dating back to several years instead of just one or two years. This will help retailers get confidence in their analyses.

Hadoop platform can facilitate the handling and analysis of unstructured data from social media networking sites such as Facebook posts or Pintrest. Natural Language Processing (NLP) can extract information from social media, and machine learning can gauge insights giving the social enterprise an edge over the competition.

Tools like Hadoop can be instrumental in finding out buying patterns of users and narrow down on approach that works exactly for users in the real world.

Conclusion

Retailers are exploring Big Data technologies like Hadoop to understand customer insights as well as reduce costs and increase profitability. Hadoop can help gain insights that can help retailers to quickly meet market trends.


written by for Big Data Services, Retail IT Solutions section(s).