Optimizing Data Lakes for High-Performance Analytics in Big Data Ecosystems

This paper proposes an optimized framework for data lakes meant to enhance parameters like data rate, response time, and capacity in big data environments. Some common issues with traditional data lakes include issues of slow data access, and basic problem of scalability that becomes an issue as data sizes increase. These challenges are however countered by the proposed method through the use of distributed data structures of data, indexing quality, concurrent processing and in memory computing. The results obtained are 40% time saving in retrieving large data set and 50% improvement in query response time for heavy data. This in-memory processed system exhibited up to 60% gain in data throughput and achieved better scalability with shorter query response time as more parallel processing nodes were added. The evidences derived from the experiment clearly explain that the proposed method not only improves the performance but also offers cost efficient solution for the organizations those are dealing with big real time data analytics. Subsequent studies may involve the exploration of higher levels of machine learning to enhance the predictability of the analytical approaches used and optimal resource allocation to improved data lake underway in the cloud.