In this new world of Big Data, you can never fall short of data. Marketers these days have access to more data than ever. They can use this data to learn about their customer and fine tune their marketing to appeal them strongly. Data is collected from every interaction on the web. For instance, an e-commerce website would collect data about the products viewed, or pages viewed frequently, popular products, product reviews, cart abandonments, page shares, etc. This data can be used to answer questions like, “Which is the most popular product?”, or “what product recommendations should be displayed?”. This kind of data-driven marketing approach enables Marketers to reach the right customers making their marketing more relevant.
Digital channels have become a major source of data collection. Data collection is not a challenge anymore. However, the challenge lies in data storage. Traditional storage methods have become insufficient and are an expensive means to store huge data. To handle such large amounts of data, Hadoop provides a file system called as the HDFS or Hadoop Distributed File System. The design of HDFS is based on the Google File System (GFS), which was described in the paper published by Google.
The major features of the HDFS are as follows [Tweet this] –
- Its a distributed file system which stores large amounts of data.
- It uses the less-expensive commodity machines connected in parallel to store the data.
- It is highly scalable. This is achieved by simply adding more machines to the cluster.
- It provides a high-throughput access to data.
- It replicates data on multiple machines i.e. same data is replicated or stored on multiple machines which makes it fault-tolerant. In the event of a failure of a single machine, same data can be accessed from another machine where the data was replicated. This ensures that there is no data loss.
- Provides high availability of data.
- Data can be accessed by multiple clients at the same time.
- Data can be stored in any format (structured or unstructured).
- The underlying Hadoop platform takes care of handling failures and the automatic distribution of data. Unlike the traditional systems, users or developers need not bother about how to make the data available in parallel or to implement any error handling mechanisms.
- HDFS is a write once, read many model.
Data storage is the backbone of any data-centric system. With the current trend of integrating Big Data with Marketing, it is important that we have an optimized, scalable, reliable and an efficient data storage system. And, Hadoop provides just what we need!