ii. What is QlikView?<\/h4>\n
QlikView<\/strong> is a data analysis and data visualization platform. It is a data analysis and reporting tool which uses graphical aids like charts, graphs, and tables to represent data in different ways for the analysts to draw meaningful insights from.<\/p>\n Important business decisions are taken on the basis of insights drawn from the data reports.<\/p>\n Users have always been curious about the intersection or combination of these two tools as for Hadoop is efficient in fetching, storing and managing the massive amounts of data (big data<\/strong>) and QlikView is able to display the relations between data elements perform aggregations on it.<\/p>\n So, coming together of these two is for sure an interesting thing.<\/p>\n QlikView can work as a front-end interactive platform which shows the associations of facts or aggregation of data objects in the data silos distributed under the Hadoop Distributed File System (HDFS)<\/strong>.<\/p>\n But a major concern here is that QlikView is an in-memory data analysis tool which means that it keeps the data in memory.<\/p>\n Whereas, Hadoop is a tool which works with big data and accommodating as much data as the size of big data (several terabytes) is a tough task.<\/p>\n Qlik has been trying to make it work by breaking the big data into smaller chunks so that it could be used by the QlikView Engine. This is done by following the MapReduce<\/strong> model of Hadoop.<\/p>\n So, to put it differently, business users use the big data from Hadoop systems to make it meaningful information in QlikView. That is, big data flows from the source to the analysis tool.<\/p>\n Let us understand the flow of data from the storage silos to the analytics tool and understand the role of Hadoop and QlikView in this process. Initially, semi or unstructured data (raw) from various origins such as machine data, transaction data, cloud data, etc. is taken.<\/p>\n Such data is defined as big data as it has a high volume, high velocity, and high variety.<\/p>\n Because of the magnitude of data, it is copied to the Hadoop ecosystem<\/strong>, where it is distributed according to the HDFS deployed on commodity hardware and processed by data manipulation and aggregation operations. This marks the first-level of raw data processing.<\/p>\n The flow of Big data from Hadoop to QlikView<\/p><\/div>\n In the Hadoop ecosystem lies Apache Hive<\/strong> and Cloudera Impala which serves as open source platforms for external tools like QlikView to access and process data from Hadoop.<\/p>\n From the Hadoop cluster, data goes to the Enterprise Data Warehouse (EDW) which acts as the central repository storing and processing structured data for analysis. EDWs complement the Hadoop system very well because they not only stores data but are is also capable of conducting robust ETL (Extract, Transform and Load) process upon the structured data.<\/p>\n It is convenient for the analytical tools to fetch and process structured hence, more meaningful data from the EDWs. EDWs can fetch data either from the data source (raw data) or from the Hadoop ecosystem.<\/p>\n Thus, the structured and organized data from EDWs or the data from supporting tools such Hive and Impala<\/strong> is consumed by the analytical tools such as QlikView, Qlik Sense etc. for respective purposes.<\/p>\n From these tools, users create well structured and informative data reports and dashboards.<\/p>\n You can configure and integrate Hadoop with QlikView in two ways. Firstly, by loading data directly into a QlikView In-memory associative data store. Secondly by conducting direct data discovery on top of Hadoop.<\/p>\n In the first method, data is compressed and loaded into QlikView in-memory from Hadoop only at the time of requirement.<\/p>\n Another method is to build a hybrid QlikView solution which conducts direct data discovery on top of Hadoop. Which means that data resides in Hadoop and all the processing related queries are sent down to Hadoop from QlikView. To connect QlikView with Hadoop tools. Follow the procedure given below.<\/p>\n Download and install QlikView<\/strong> in your desktop.<\/p>\n Download a JDBC connector. Through the JDBC connector, you can connect with Hadoop tools such as, Hadoop HDFS, Hive, HiveServer2 (Cloudera, MapR, Amazon, EMR), Cloudera Impala, Apache HBase and Apache Cassandra<\/strong>.<\/p>\n Install the JDBC set up file on your device (extract the zip file).<\/p>\n Now, go to File > New File > Edit Script > Data<\/strong> and select JDBCConnector.dll option from the drop-down menu.<\/p>\n Step. 4 QlikView integration with Hadoop<\/p><\/div>\n We will connect Cloudera Hadoop Hive from this JDBC connector. You can download the latest version of Cloudera Hive JDBC drivers.<\/p>\n If the files are zipped, then extract them under the name Cloudera_HiveJDBC_2.5.4.1006.zip. Also, keep a few useful jar files as mentioned below.<\/p>\n Follow the given path and add the mentioned jar files in into the JDBC library. Path: Edit Script > Data > (JDBCConnector) Configuration > JDBC drivers > Add Library > Browse<\/strong> the jar files and add them to library.<\/p>\n Step. 7 QlikView integration with Hadoop<\/p><\/div>\n Next, we give the name to the JDBC driver class according to the Hive server you are using. If you are using Cloudera Hive Server 1 or Hive Server 2 then driver class name should be one of the below two class names given. It is possible that you find org.apache.hive.jdbc.HiveDriver as the default class name. You must change it.<\/p>\n Step.8 QlikView integration with Hadoop<\/p><\/div>\n Use the commands $ hive — service metastore & and $ hive\u2014service hiveserver & to initiate the services like Hive\u00a0metastore<\/strong> and hive server.<\/p>\n Provide the connection string details like URL, username, and password to connect to the Hadoop database.<\/p>\n Step. 10 QlikView integration with Hadoop<\/p><\/div>\n Select tables and columns from the selected Hadoop<\/strong> database.<\/p>\n Step. 11a QlikView integration with Hadoop<\/p><\/div>\n Step. 11b QlikView integration with Hadoop<\/p><\/div>\n Click on OK and execute the QlikView script<\/strong>.<\/p>\n Step. 12 QlikView integration with Hadoop<\/p><\/div>\n Once the connection is established. You can add all the files into QlikView by right-clicking on the sheet and clicking on Select fields. You can use the loaded data fields in the visualizations in QlikView.<\/p>\niii. The Relationship of Hadoop and QlikView<\/h4>\n
A Flow of Big data from Hadoop to QlikView<\/h3>\n
![\"Big](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/02\/Hadoop-and-Qlikview-1.png\")
<\/a>QlikView integration with Hadoop<\/h3>\n
Step. 1<\/h4>\n
Step. 2<\/h4>\n
Step. 3<\/h4>\n
Step. 4<\/h4>\n
<\/a>Step. 5<\/h4>\n
Step. 6<\/h4>\n
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Step. 7<\/h4>\n
<\/a>Step. 8<\/h4>\n
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<\/a>Step. 9<\/h4>\n
Step. 10<\/h4>\n
<\/a>Step. 11<\/h4>\n
<\/a><\/a>Step. 12<\/h4>\n
<\/a>Step. 13<\/h4>\n