## 1 Introduction  
The storage cost and data acquisition cost have fallen sharply due to technological advances, which has created the rise of big data in this era. Meanwhile, the growth of data volume in various industries around the world is rising rapidly, for example, in social networks [1]. Also, since obtaining data is no longer the biggest difficulty in scientific research, how to \"store\" and \"dig\" massive data and successfully \"communicate\" the results of analysis has become a new bottleneck and research focus. In big data analytics, the characteristics of data processing are high volume, high velocity, high variety, high veracity, high value, and high visualization (abbreviated as 6 V), and hence data processing is facing an expanding number of incredible challenges. Many issues are emerging in big data manipulation such as network traffic flow, high volume storage, remote backup, resources management, computing performance, package compatibility, data security, service level agreement, disaster recovery, and other abilities. Many practical applications concerning big data have to find the proper way for dealing with huge amounts of data containing a complex structure. Unfortunately, traditional tools cannot solve the problem as mentioned above and thus it is worthwhile to continue to develop emerging tools or tools to tackle the issues in big data environment. Business intelligence (BI) is defined as a kind of technology and application that is made up of a data warehouse (or data mart), query report, data analysis, data mining, data backup, and recovery to help enterprises make decisions [2]. Today, enterprises treat business intelligence as a tool that transforms existing data in the enterprise into knowledge and helps companies make informed business decisions. The data discussed here includes orders from the enterprise business system, inventory, transaction accounts, customer and supplier profiles, and data from the industry and competitors, as well as various data from other external environments in which the business is located. Business decisions that business intelligence can assist with can be either at the operation level or at the management and policy level. People always like to deliver a query to an easy-to-use business intelligence and analytics that can support a full range of SQL-like commands capabilities such as SAS PROC SQL, Microsoft SQL Server, and Tableau SQL. Today's companies need the ability to turn on BI using SQL commands that scale across big data in BI. Advanced big data analytics requests and reshapes BI with the ability to turn complex data to insight and action for better business decision-making. Furthermore, the technique of in-memory computing is an increasingly viable option; a number of companies are using in-memory computing for many analytical applications [3]. That is why in-memory computing for big data analytics has become a core function of BI to run smarter, more agile, and higher-efficiency data retrieval and analysis. The cost of the establishment and maintenance of big data BI are vital concerns because small-to-medium enterprises may not be able to afford it. A change in attitude towards open-source solutions to create an opportunity for business intelligence is expected. This paper is the first to introduce the open-source solutions to multiple big data tools using (1) Apache Hive [4], (2) Apache Impala [5], and (3) Apache SparkSQL [6] that fully support SQL-like commands. Next, the integration of these tools, as shown in Figure 1, is applied to a data warehouse and they work over underlying supported platforms like Apache Hadoop [7] and/ or Apache Spark [8] that commonly share the distributed file system Hadoop Distributed File System(HDFS) [9]. This paper has built three integrated open source SQL-handling tools for the data warehouse part in a commercial business intelligence system, as shown in Figure 2, where a block is surrounded by a dotted square, and thus it is suitable for traditional SQL query interaction.