Category Archives: Spark

Normal Distribution Fitness Test with Chi Square on Spark

Posted on January 30, 2019 by

Many Machine Learning models is based on certain assumptions made about the data. For example, in ZScore based  anomaly detection, it is  assumed that the data has normal distribution. Your Machine Learning model will be as good as how those … Continue reading →

Posted in Anomaly Detection, Big Data, Data Science, Spark, Statistics | Tagged , | Leave a comment

Time Series Seasonal Cycle Detection with Auto Correlation on Spark

Posted on December 23, 2018 by

There are may benefits of auto correlation analysis on time series data, as we will be alluding to in detail later. It allows us to gain important insights on the nature of the time series data. Cycle detection is one … Continue reading →

Posted in Big Data, Correlation, Spark, Statistics, Time Series Analytic | Tagged , , | 1 Comment

Bulk Mutation in an Integration Data Lake with Spark

Posted on November 19, 2018 by

Data lakes act as repository of data from various sources, possibly of different formats. It can be used to build data warehouse or to perform other data analysis activities. Data lakes are generally built on top of Hadoop Distributed File … Continue reading →

Posted in Big Data, Data Warehouse, eCommerce, ETL, Spark | Tagged , , , , | Leave a comment

Learning Alarm Threshold from User Feedback using Decision Tree on Spark

Posted on October 15, 2018 by

Alarm fatigue is a phenomena where some one is exposed to large number of alarms, become desensitized to them and start ignoring them. It’s been reported that security professionals ignore 32% of alarms because they are thought to be false. … Continue reading →

Posted in Anomaly Detection, Big Data, Data Science, Outlier Detection, Spark | Tagged , , , , | Leave a comment

Contextual Outlier Detection with Statistical Modeling on Spark

Posted on September 18, 2018 by

Sometimes an outlier is defined with respect to a context. Whether a data point should be labeled as an outlier depends on the associated context. For a bank ATM, transactions that are considered normal between 6 AM and 10 PM, … Continue reading →

Posted in Anomaly Detection, Big Data, Data Science, Spark | Tagged , , | 1 Comment

Pluggable Rule Driven Data Validation with Spark

Posted on August 20, 2018 by

Data validation is an essential component in any ETL data pipeline. As we all know most Data Engineers and Scientist spend most of their time cleaning and preparing their data before they can even get to the core processing of … Continue reading →

Posted in Big Data, Data Science, ETL, Spark | Tagged , | 2 Comments

Leave One Out Encoding for Categorical Feature Variables on Spark

Posted on June 18, 2018 by

Categorical feature variables is a thorny issue for many supervised Machine Learning algorithms. Many learning algorithms can not handle categorical feature variables. In this post, we will go over an encoding scheme called Leave One Out Encoding, as implemented with … Continue reading →

Posted in Big Data, Data Science, ETL, Spark | Tagged | 1 Comment