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Reading list

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 implement Incremental Load in QlikView?

Sunil Ray
Sunil Ray Last Updated : 16 Apr, 2015
5 min read

In my previous article, we discussed “How to use QVDs to make your QlikView application more efficient?”. In this article, we will go one step ahead to make our application more efficient while dealing with large transactional data. As discussed in my previous article I was working on a QlikView application, where I had to show the sales across various channels for pre-defined frequencies (e.g. Daily, Monthly, Yearly).

Initially, I was reloading the entire transactions table on a daily basis even though I already had the data till yesterday with me. This not only took significant time, but also increased the load on the database server and the network.This is where incremental load with QVDs made a huge difference by loading only new or updated data from the database into a table.

Incremental loads:

Incremental load is defined as the activity of loading only new or updated records from the database into an established QVD. Incremental loads are useful because they run very efficiently when compared to full loads, particularly so for large data sets.

Incremental Load, QlikView, QVD, Optimize Qlikview

Incremental load can be implemented in different ways, the common methods are as follows:

  1. Insert Only (Do not validate for duplicated records)
  2. Insert and Update
  3. Insert, Update and Delete

Let us understand each of these 3 scenarios with an example

1. Insert Only: 

Let us say, we have sales raw data (in Excel) and whenever a new sales get registered, it is updated with basic details about the sale by modified date. Since, we are working on QVDs, we already have QVD created till yesterday (25-Aug-14 in this case). Now, I want to load only the incremental records (Highlighted in yellow below).

Incremental_Load_Qlikview_2

To perform this exercise, first create a QVD for data till 25-Aug-14. To identify new incremental records, we need to know the date till which, QVD is already updated. This can be identified by checking the maximum of Modified_date in available QVD file.

As mentioned before, I have assumed that “Sales. qvd”  is updated with data till 25-Aug-14. In order to identify the last modified date of “Sales. qvd”, following code can help:

Incremental_Load_Qlikview_3

Here, I have loaded the last updated QVD into the memory and then identifed the last modified date by storing maximum of “Modified_Date”. Next we store this date in a variable “Last_Updated_Date” and drop the table “Sales”. In above code, I have used Peek() function to store maximum of modified date. Here is it’s syntax:

Peek( FieldName, Row Number, TableName)

This function returns the contents of given field for a specified row from the internal table. FieldName and TableName must be given as a string and Row must be an integer. 0 denotes the first record, 1 the second and so on. Negative numbers indicate order from the end of the table. -1 denotes the last record.

Since we know the date after which the records will be considered as new records, we can Load incremental records of the data set (Where clause in Load statement) and merge them with available QVD (Look at the snapshot below).

 

Incremental_Load_Qlikview_4

Now, load updated QVD (Sales), it would have incremental records.

Incremental_Load_Qlikview_5

As you can see, two records of 26-Aug-14 were added. However, we have inserted a duplicate record also. Now we can say that, an INSERT only method does not validate for duplicate records because we have not accessed the available records.

Also, in this method we can not update value of existing records.

To summarize, following are the steps to load only the incremental records to QVD using INSERT only method:

1) Identify New Records and Load it
2) Concatenate this data with QVD file
3) Replace old QVD file with new concatenated table

2. Insert and Update method:

As seen in previous example, we are not able to perform check for duplicate records and update existing record. This is where, Insert and Update method comes to help:

Incremental_Load_Qlikview_6

In the data set above (Right table), we have one record (ID = PRD1458) to add and another one (ID = PRD858) to update (value of sales from 131 to 140). Now, to update and check for duplicate records, we need a primary key in our data set.

Let’s assume that ID is the primary key and based on modification date and ID, we should be able to identify & classify the new or modified records.

In order to execute this method, follow similar steps to identify the new records as we have done in INSERT only method and while concatenating incremental data with existing one, we apply the check for duplicated records or update the value of existing records.

Incremental_Load_Qlikview_7

Here, we have loaded only those records where Primary Key(ID) is new and use of Exists() function stops the QVD from loading the outdated records since the UPDATED version is currently in memory so values of existing records gets updated automatically.

Now, we have all unique records available in QVD with an updated sales value for ID(PRD858).

Incremental_Load_Qlikview_8

3. INSERT, UPDATE, & DELETE method:

The Script for this method is very similar to the INSERT & UPDATE, however here we have an additional step needed to remove deleted records.

We will load primary keys of all records from current data set and apply an inner join with concatenated data set (Old+Incremental). Inner join will retain only common records and therefore delete unwanted records. Let’s assume that we want to delete a record of (ID PRD1058) in the previous example.

Incremental_Load_Qlikview_9

Here, we have a data set with the addition of one record (ID PRD1458), modification of one record (ID PRD158) and deletion of one record (ID PRD1058).

Incremental Load, QlikView, QVD

End Notes :

In this article, we have discussed how incremental loads are better and provide an efficient way to load data as compared to FULL load. As a good practice, you should have regular backup of data because it may get impacted or a data loss can occur, if there are issues with database server and network.

Depending on your industry and need of the application, you can select, which method works for you. Most of the common applications in BFSI industry are based on Insert & Update. Deletion of records is normally not used.

Have you dealt with similar situation or have another hack to improve efficiency of Qlikview applications under your hat? If so, I’d love to hear your thoughts through comments below as it also benefit someone else trying to handle similar situation.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

 

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

Mukesh Rathi
Mukesh Rathi

Thanks Sunil, very useful.

123

Erica
Erica

Hi, i tried the incremental load but i have a problem because qlikview skips the dates, i.e. it should load 17-18-19 April but it skips 18 April. Date format is 17/04/2015 00..00.00 My script is the following: table1: LOAD ESITO, SERVICE_ID, ERROR_CODE, MONTH, WEEK, DAY, HOUR FROM [C:\Users\qvd\table1.qvd] (qvd); LastUpdate: load Max (DAY) as MaxDate /*Max DAY=17Apr*/ Resident table1; let LastUpdate = peek('MaxDate', 0,'LastUpdate'); ODBC CONNECT TO Report; SQL SELECT * FROM Report.dbo.Table WHERE DAY >=$(LastUpdate); New data are about 18-19Apr, so i expected 17-18-19 in the new file qvd, but Qlikview skips 18 April. What's the problem? Thank you

123
Navdeep
Navdeep

Appreciate your efforts..

123

Solomon
Solomon

Truly Inspiring

123

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