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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

How to use QVDs (QlikView Data files) to make your Qlikview application efficient?

Sunil Ray
Sunil Ray Last Updated : 17 Apr, 2015
4 min read

In June 2013, I had been using QlikView for about a year. During those days, I was working on a QlikView project where I had to show the sales number across various channels for various periods (e.g. Daily, Monthly, Yearly) and in comparison with last year.

I had two transactional tables, three mapping tables (available in Excel). I had completed this project successfully within a defined timeline. However, there were 2 problems with the model I used:

  • Whenever there was any change in any of the mapping files, I had to repeat the reload process which involved not only reloading the excel, but also reloading the entire transaction table. If there was any issue with the network or database, the reloading would fail.
  • When I used to report daily sales number, I reloaded complete tables on a daily basis whereas data till yesterday was already with me. It was too time consuming process.
Image Source: qlik.com

Image Source: qlik.com

This is when using a QVD (QlikView Data) file made a lot of difference. You can think of these QVD files as a middle layer, so that your front-end queries do not run on the databases directly. Creation of these QVDs can be done during off-peak hours (Less network and database load). QVDs are also essential to load only the incremental data (i.e. Additional or modified records).

qvd_architecture

 

What Is a QVD file?

QVD is a QlikView format and can only be written to and read by QlikView. A single QVD file can store a single table and is created in the load script in a QVW file.

A QVD file consists of three parts:

  • XML header to describe the fields in the table, the layout of the subsequent information and other meta-data.
  • Symbol tables in a byte stuffed format.
  • Actual table data in a bit-stuffed format.

 

Advantages of using QVDs:

QVD files offer many advantages to your QlikView applications, including the following:

  • Faster load time: Reading data from QVD is 10-100 times faster than other sources i.e. decreasing load time. It is because QVD creation uses the same algorithms as QlikView uses to store data in memory, the result of that Loading from QVD is faster, when compared to other formats.
  • Less load on databases and networks: Once data is exported in QVDs, you don’t need to connect with external database again. It reduces the workload on external databases and network. Furthermore, when several QlikView scripts share the same data, it is only necessary to load it once from the source database. The other applications can also make use of the same data via a QVD file.
  • Incremental loads of QVDs: Incremental load (loading only new/ modified detail of a database) can be done only by using QVDs. It reduces the load time significantly as compared to complete load.
  • Consolidating data from multiple data sources and databases: Multi-layer QVDs are used to create a with robust model, when data is fetched from multiple data sources.

 

How to create a QVD?

QVDs are most commonly created during the execution of QlikView load script using the STORE command:

Syntax: STORE <TableName> INTO <Path\FileName.qvd>

It is a good practice to have separate application to create QVD files. This layer is used to handle all interaction with source databases. And the result of this interaction can be reloaded quickly from QVD files.

qlikview_qvd_store

Incremental loads: As mentioned before, Incremental loads are defined as loading only new or updated records from the database into an established QVD. Incremental loads are useful because they run much faster than full loads, particularly for large data sets from databases. (Will discuss it in detail in the next article)

 

How to load data from QVD?

To read data from QVD, we need to write a LOAD statement (similar to the command to load CSV or excel file).

Syntax: 
TableName:
 LOAD
 FieldList 
 FROM <Path\FileName.qvd (qvd) >;

qlikview_qvd_load

QVD files can be read in two modes, standard (fast) and super-fast. The selected mode is determined automatically by the QlikView script engine. Super-fast mode can be utilized only when all fields or a subset of it are required to be read without any manipulation (e.g. formula based field generation), although the renaming of fields is allowed.

End Notes:

Managing QVDs is one of most important part in any QlikView application project. It can be used to provide a logical data layer and hence it can act as a data warehouse in absence of one (one of the big benefits of Qlikview in comparison to other BI tools in the market).

Creating QVDs helps to decrease the database and network load. Once a base QVD is generated, an incremental load script can be run to load only the new or modified records to the QVD. Incremental load also removes the deleted records. As a result of this, your application consumes less space and requires less load time.

If you have used QVDs in past to create multi-level data models or plan to use one in your application now, please feel free to share your experience with me through the comments below.

 

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Sunil Ray
Sunil Ray

Sunil Ray is Chief Content Officer at Analytics Vidhya, India's largest Analytics community. I am deeply passionate about understanding and explaining concepts from first principles. In my current role, I am responsible for creating top notch content for Analytics Vidhya including its courses, conferences, blogs and Competitions.

I thrive in fast paced environment and love building and scaling products which unleash huge value for customers using data and technology. Over the last 6 years, I have built the content team and created multiple data products at Analytics Vidhya.

Prior to Analytics Vidhya, I have 7+ years of experience working with several insurance companies like Max Life, Max Bupa, Birla Sun Life & Aviva Life Insurance in different data roles.

Industry exposure: Insurance, and EdTech

Major capabilities: Content Development, Product Management, Analytics, Growth Strategy.

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Responses From Readers

Clear

solomon
solomon

Nice article Sunil :) !

123

rgv
rgv

hi , very helpful info thnx for sharing

123

Jakir
Jakir

Its very useful.Thanks a lot Sunil

123

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