Monthly Archives: April, 2017

Which Ones Your Preferred Database From 2017?

Which-ones-your-preferred-Database-from-2017

The ‘trending’ topic this season is Data Processing. The statistics attached with this blog depicts that respondents have mainly voted for NoSQL and SQL databases. The opinions of the respondents have conferred the title of ‘most engaging’ to NoSQL database, confirming the second position with a 74.8%.

Also read: Drawing a Bigger Picture: FAQs about Data Analytics


The survey declares the PostgreSQL as the confirmed winner, where 25.3 % have proclaimed it to be ‘very interesting’ and 37.7 % have confined within ‘interesting’.

Also read: Top Databases of 2017 to Watch Out For

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The results of the survey, which database has gathered the maximum attention of  respondents, are as follows:

  • Elasticsearch declared runner-up with an overall 59%.
  • The amalgamation of Lucene and Solr roaring with 43.8%
  • More interest devoured in Apache Spark with 3%
  • Hadoop scoring a meager of 8%

Next, it unveils that the US respondents have mainly opted for Elasticsearch to PostgreSQL, and Oracle have failed to evoke any interest in the mind of US respondents. However, the picture is completely opposite for the European respondents.

Also read: Data Analytics for the Big Screen


The ending note states that it is high time we realize that the dire need of the hour is data storage and processing. This conclusion is supported by the fact that so many respondents have invested their valuable time in the survey and clearly shows that database is here to stay.

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How to Connect Oracle BI Server with Tableau

How-to-Connect-Oracle-BI-Server-with-Tableau

Here, we will discuss about how to incorporate Oracle BI server and make use of the existing Subject Areas built on RPD in Tableau desktop workbook as Data Source.

NOTE: This is applicable for 8.2, 8.3, 9.0 and later versions.

Firstly, to launch an ODBC connection with the Oracle BI Server to access RPD Online, you have to install a copy of Oracle Business Intelligence Developer Client Tools (available from the Oracle website). Following, you can use the same DSN to connect to Tableau through ODBC connections.

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As soon as you are done with installing the client, carry out the following steps to create a database source name (DSN):

  1. Follow the steps mentioned below to add a new system DSN for Oracle BI Server ODBC in the ODBC Administrator tool..
  • Go to the System DSN tab and click Add
  • Choose the Oracle BI Server DSN, among other available drivers

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    1. Give correct connection details and connect to BI Server

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      3. Save the System DSN
      In total, there are 3 levels in RPD:

      • Physical Layer (right pane) – This is the layer where a connection is established between each data source and the raw tables are disclosed. Joins across tables are performed here.
      • Business Layer (middle pane) – This is where logical relations, data modelling and hierarchy development are implemented.
      • Presentation Layer (left pane) – This is the layer exposed to the business through “subject areas”. The subject areas are clearly modelled to display data in the most easy-to-understand format.

      Under Tableau, tables in the presentation layer are adjoined as data sources, only.

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      Locate the Key Fields in Dimensions and Fact Table in Physical Layer of RPD and disclose the same to respective Presentation Tables in Presentation Layer of Sample Sales Lite Subject Area.

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      Create a “.tdc” file

      Create a Tableau data source customization (.tdc) file to connect Tableau Desktop to an Oracle BI server.

      1. Open a text editor; copy and paste the below code onto it:

      <connection-customization class=’genericodbc’ enabled=’true’ version=’9.3′><vendor name=’Oracle Business Intelligence’ />
      <driver name=’Oracle BI Server 1′ />
      <customizations>
      <customization name=’CAP_SET_ISOLATION_LEVEL_VIA_ODBC_API’ value=’no’ />
      <customization name=’CAP_SUPPRESS_DISCOVERY_QUERIES’ value=’no’ />
      <customization name=’SQL_CATALOG_USAGE’ value=’0′ />
      <customization name=’SQL_SCHEMA_USAGE’ value=’0′ />
      <customization name=’CAP_FAST_METADATA’ value=’yes’ />
      </customizations>
      </connection-customization>

      Nota bene: Version is crucial; adjust the version with that of your Tableau desktop version. In my case, it is 9.3.

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Parsing Data With Python

Parsing-data-with-python

First download this machine learning data file, before we get on with our tutorial to teach you how you can pare data with python. The above mentioned dataset provided in the link mimics exactly the way the data was when we visited the web pages at that point of time, but the interesting thing about this is we need not visit the page even.

We do in fact have the full HTML source code so it will exactly be like parsing the website without the annoying use of the bandwidth. Now the first thing to do when we start is to correspond the date to our data, and then we can pull the actual data.

This is the way we start:

import pandas as pd
import os
import time
from datetime import datetime

path ="X:/Backups/intraQuarter"

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