In this chapter, we will cover building a Data Lake with the help of Hadoop. As we have learned in previous chapters, Hadoop offers low storage costs per terabyte of data compared to traditional data warehouse management systems, which makes it an alternative technology or a complementary technology for traditional data warehouse systems. Data Lake and data warehouse are both designed to store data, but a data lake can store a much larger volume of data than a data warehouse.
Data warehouses typically store clean data in pre-defined and structured relational tables. The tables are designed to hold the data in response to specific questions that the stakeholders ask of the data. In this process, the information contained in the data that has no direct value for the question that is being asked is purged when the data is loaded in the data warehouse. Once the information has been purged, there is no way to answer new questions that require the purged information...