GLADE: big data analytics made easy

Abstract

We present GLADE, a scalable distributed system for large scale data analytics. GLADE takes analytical functions expressed through the User-Defined Aggregate (UDA) interface and executes them efficiently on the input data. The entire computation is encapsulated in a single class which requires the definition of four methods. The runtime takes the user code and executes it right near the data by taking full advantage of the parallelism available inside a single machine as well as across a cluster of computing nodes. The demonstration has two goals. First, it presents the architecture of GLADE and how processing is done by using a series of analytical functions. Second, it compares GLADE with two different classes of systems for data analytics: a relational database (PostgreSQL) enhanced with UDAs and Map-Reduce (Hadoop). We show how the analytical functions are coded into each of these systems (for Map-Reduce, we use both Java code as well as Pig Latin) and compare their expressiveness, scalability, and running time efficiency.