Data Quality Improvement – DQ Dimensions = Confusions

Data Quality Improvement – DQ Dimensions = Confusions

DQ Dimensions are Confusing Data quality dimensions are great inventions from our data quality thought leaders and experts. Since the concept of quality dimensions was originally proposed in the course of the Total Data Quality Management (TDQM) program of MIT in the 1980s [5], a large number of data quality dimensions have been defined by … Continue reading Data Quality Improvement – DQ Dimensions = Confusions →

Data Quality Improvement – Conditional Functional Dependency (CFD)

Data Quality Improvement – Conditional Functional Dependency (CFD)

To fulfil the promise I made before, I dedicate this blog post to cover the topic of Conditional Functional Dependency (CFD). The reason that I dedicate a whole blog post to this topic is that CFD is one of the most promising constraints to detect and repair inconsistencies in a dataset. The use of CFD … Continue reading Data Quality Improvement – Conditional Functional Dependency (CFD) →

Data Quality Improvement – Data Profiling

This is the second post of my Data Quality Improvement blog series. This blog post discusses the data profiling tasks that I think are relevant to data quality improvement use cases. For anyone who has ever worked with data, she or he must has already done some sort of data profiling, either using a commercial … Continue reading Data Quality Improvement – Data Profiling →

Data Quality – 80:20 Rule and 1:10:100 Rule

I came across two data quality rules from Martin Doyle's blog today. Martin Doyle is a data quality improvement evangelist and an industry expert on CRM. I found, to a certain extent, those data quality rules provide some kind of theoretical supports to some of my ideas with data quality improvements. 80:20 Rule The 80:20 … Continue reading Data Quality – 80:20 Rule and 1:10:100 Rule →

Data Quality Improvement – Set the Scene Up

In this blog series I plan to write about data quality improvement from a data engineer's perspective. I plan this blog series to cover not only data quality concepts, methodologies, procedures but also to case study the architectural designs of some data quality management platforms and deep dive into technical details for implementing a data … Continue reading Data Quality Improvement – Set the Scene Up →

Our Data Quality is Good, Nothing Breakdown

Boss: Our data quality is good, nothing breakdown IT: Our data quality is good, there are some unimportant known issues, but all under control BI Developer: All data is from source systems, the quality should be good. Hay, look, how cool is the dashboard I built Business Users: Once again, those reports don't make sense … Continue reading Our Data Quality is Good, Nothing Breakdown →

What Makes Me Become a Data Quality Enthusiast

Data Quality is Important Most of the time I don't think I am an absolutist, however, I found I became more and more certain that data quality is the root of all evil. Not only bigger portion of project time should be allocated to data quality management, but also a type of lean, agile and … Continue reading What Makes Me Become a Data Quality Enthusiast →

Create Custom Partitioner for Spark Dataframe

Spark dataframe provides the repartition function to partition the dataframe by a specified column and/or a specified number of partitions. However, for some use cases, the repartition function doesn't work in the way as required. For example, in the previous blog post, Handling Embarrassing Parallel Workload with PySpark Pandas UDF, we want to repartition the traveller dataframe so … Continue reading Create Custom Partitioner for Spark Dataframe →

Configuration-Driven Azure Data Factory Pipelines

In this blog post, I will introduce two configuration-driven Azure Data Factory pipeline patterns I have used in my previous projects, including the Source-Sink pattern and the Key-Value pattern. The Source-Sink pattern is primarily used for parameterising and configuring the data movement activities, with the source location and sink location of the data movement configured in a … Continue reading Configuration-Driven Azure Data Factory Pipelines →

Handling Embarrassing Parallel Workload with PySpark Pandas UDF

Introduction In the previous post, I walked through the approach to handle embarrassing parallel workload with Databricks notebook workflows. However, as all the parallel workloads are running on a single node (the cluster driver), that approach is only able to scale up to a certain point depending on the capability of the driver vm and … Continue reading Handling Embarrassing Parallel Workload with PySpark Pandas UDF →