GLADE: Big Data Analytics Made Easy

We present GLADE, a scalable distributed system for large scale data analytics. GLADE takes analytical functions ex-pressed through the User-Defined Aggregate (UDA) inter-face 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 tak-ing 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 ar-chitecture 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 func-tions 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