Frequent itemset mining presents one of the fundamental building blocks in data mining. However, despite the crucial recent advances that have been made in data mining literature, few of both standard and improved solutions scale. This is particularly the case when i) the quantity of data tends to be very large and/or ii) the minimum support is very low. In this paper, we address the problem of parallel frequent itemset mining (PFIM) in very large databases, and study the impact and effectiveness of using specific data placement strategies in a massively distributed environment. By offering a clever data placement and an optimal organization of the extraction algorithms, we show that the arrangement of both the data and the different processes can make the global job either completely inoperative or very effective. In this setting , we propose two different highly scalable, PFIM algorithms, namely P2S (Parallel-2-Steps) and PATD (Parallel Absolute Top Down). P2S algorithm allows discovering itemsets from large databases in two simple , yet efficient parallel jobs, while PATD renders the mining process of very large databases more simple and compact. Its mining process is made up of only one parallel job, which dramatically reduces the mining runtime, the communication cost and the energy power consumption overhead in a distributed computational platform. Our different proposed approaches have been extensively evaluated on massive real-world data sets. The experimental results confirm the effectiveness and scala-bility of our proposals by the important scale-up obtained with very low minimum supports compared to other alternatives.