The amount of data we are now generating is astonishing. Data has also evolved dramatically in recent years, in type, volume, and velocity. The emerging technologies like smartphones and sensors present opportunities for data exploitation, streaming and collecting from heterogeneous device every second. Analyzing these large datasets can unlock multiple behaviors previously unknown, a help optimizes approaches to many applications. However, collecting and handling of these massive datasets present challenges in how to perform optimized the large data. There are several frameworks available for handling the big data applications. The Lambda Architecture is data processing framework that can handle both batch and stream processing. The batch layer is implemented using Pig and hive, the streaming layer is built by using the Spark streaming and Spark SQL. This presents a need for developing the new framework for handling the big data applications particularly using public clouds to minimize cost, resource availability.