# A New Big Data Benchmark for OLAP Cube Design Using Data Pre-Aggregation Techniques
## 6 Conclusions and Future Work  
In this paper, we have proposed a new benchmark for Big Data OLAP, aimed at benchmarking OLAP cubes designed by taking advantage of data pre-aggregation techniques provided by any of the current Big Data OLAP appoaches [2; 3; 4; 5; 6; 7; 8]. As a result of our research in state-of-the-art technologies, and our experience implementing Big Data OLAP systems, we identified the main goals related to a successful OLAP cube design. In order to create a benchmark from these goals, we translated them into measures, thus being able to quantify their level of satisfaction for each benchmarked cube design.  
In order to show the application of our benchmark, we required to create a specific implementation for a particular technology. We chose Apache Kylin [2; 3] as our sample Big Data OLAP approach from the range of candidate technologies due to three reasons: (i) its ability to cope with up to tens of billions row-sized fact tables, (ii) the capability to deal with ultra high cardinality dimensions, and (iii) its support for both batch and real-time OLAP scenarios.  
Moreover, we have identified the principles for the design of Kylin cubes and then executed our proposed benchmark across eight different Kylin cube designs. Based on the results, we proposed a guideline to aid OLAP cube designers find the most optimal cube designs for their particular scenarios.  
Among the design goals proposed, we included OLAP cube building time. However, we must notice that our benchmark focuses only on the cube building time related to the OLAP engine itself. It is not aimed at including into the equation other measures covering the total amount of time taken since data are acquired, such as time required for data transformation, cleaning, etc. Given that a Big Data pipeline is often composed of many processes, all of them can be a limiting factor, and contribute to the overall time required to build the cube. Moreover, since there is a tradition for incremental loads after the first one, our benchmark metrics could be complemented or even combined with other benchmarks aimed at optimizing the Big Data pipeline. This would provide Big Data system designers an overall view of the suitability of their architected solution.  
As part of our future work, we are planning on implementing and comparing the results obtained across several Big Data OLAP approaches [2; 3; 4; 5; 6], such as Druid (standalone or integrated with Kylin) or Clickhouse. On the other hand, we will study the specifics of Big Data cloud services such as Google Big Query [7] or Azure Synapse with the aim to implement our benchmark on these systems. Furthermore, for these cloud approaches, we propose to carry out vertical and horizontal scalability tests to study their effects on the OLAP cube performance.  
Conceptualization, R.T.; Data curation, R.T.; Funding acquisition, J.T.; Methodology, R.T.; Project administration, A.M. and J.T.; Supervision, A.M. and J.T.; Validation, A.M. and J.T.; Visualization, R.T.; Writing--original draft, R.T.; Writing--review and editing, A.M. and J.T. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by the ECLIPSE project (RTI2018-094283-B-C32) from the Spanish Ministry of Science, Innovation and Universities.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SK & Surrogate Key \\ OLAP & On-Line Analytical Processing \\ OOM & Out Of Memory \\ HDFS & Hadoop Distributed File System \\ \end{tabular}