New customer case study with DataStax! Read More

Analyzing & Monitoring Data quality in Google BigQuery

Mona Rakibe

BigQuery is Google’s fully managed, petabyte scale, low cost enterprise data warehouse for analytics and most often used for interactive ad-hoc queries of read-only datasets.

This makes it crucial that the data in BigQuery is always ready for consumption. So it's important to ensure and monitor the quality of the data in your BigQuery

Monitoring the usage and performance of BigQuery

This does not fall into the category of pure data quality monitoring but nonetheless an important part of your overall monitoring strategy. You could leverage Google's native monitoring capabilities or augment it with your central cloud monitoring like Datadog.

Syntactical Validations within BigQuery

Google BigQuery supports a lot of schema level validations, this definitely works well for data type and schema issues.

Open Source Tools

Data Validation tool is a good open source library that might be helpful if you are migrating data from an existing system to BigQuery.

Airflow (Cloud Composer) BigQuery operators provides another good open-source solution to validating data in BigQuery

Additionally there are open source tools like Great Expectations, Tensorflow Data Validation, Deequ, Apache Griffin, etc

These tools work best when you already know what validations have to be programmatically enforced. Most of these tools need some development work.

‍

‍

ML and statical monitoring tools

These tools are more suited for automatic anomaly detection for the unknown issues in the data. These types of issues can fall into trend anomalies and value anomalies. There are quite a few observability companies evolving in the market that monitor trends. Telmai is one such platform and it is able to automatically detect anomalies at row-value level.

In-order to truly build a high quality data source you would start with analysis and then translate these learnings into monitoring metrics and SLI.

Example : Once you standardize the acceptable patterns for phone numbers, you want to be alerted when there is a violation on this.

Hope this was helpful, would love to hear from you how you are monitoring your BigQuery data today..

We have just added our BigQuery integration for Profiler++ and we will be rolling out our beta-version BigQuery monitoring soon.

Please leave your email if you are interested in being a beta customer for our BigQuery data monitoring.

‍

Data profiling helps organizations understand their data, identify issues and discrepancies, and improve data quality. It is an essential part of any data-related project and without it data quality could impact critical business decisions, customer trust, sales and financial opportunities.

‍

To get started, there are four main steps in building a complete and ongoing data profiling process:

Data Collection
Discovery & Analysis
Documenting the Findings
Data Quality Monitoring

We'll explore each of these steps in detail and discuss how they contribute to the overall goal of ensuring accurate and reliable data. Before we get started, let's remind ourself of what is data profiling.

What are the different kinds of data profiling?

Data profiling falls into three major categories: structure discovery, content discovery, and relationship discovery. While they all help in gaining more understanding of the data, the type of insights they provide are different:

Structure discovery analyzes that data is consistent, formatted correctly, and well structured. For example, if you have a ‘Date’ field, structure discovery helps you see the various patterns of dates (e.g., YYYY-MM-DD or YYYY/DD/MM) so you can standardize your data into one format.

Structure discovery also examines simple and basic statistics in the data, for example, minimum and maximum values, means, medians, and standard deviations.

Content discovery looks more closely into the individual attributes and data values to check for data quality issues. This can help you find null values, empty fields, duplicates, incomplete values, outliers, and anomalies.

For example, if you are profiling address information, content discovery helps you see whether your ‘State’ field contains the two-letter abbreviation or the fully spelled out city names, both, or potentially some typos.

Content discovery can also be a way to validate databases with predefined rules. This process helps find ways to improve data quality by identifying instances where the data does not conform to predefined rules. For example, a transaction amount should never be less than $0.

Relationship discovery discovers how different datasets are related to each other. For example, key relationships between database tables, or lookup cells in a spreadsheet. Understanding relationships is most critical in designing a new database schema, a data warehouse, or an ETL flow that requires joining tables and data sets based on those key relationships.

‍

1. Data Collection

Start with data collection. Gather data from various sources and extract it into a single location for analysis. If you have multiple sources, choose a centralized data profiling tool (see our recommendation in the conclusion) that can easily connect and analyze all your data without having you do any prep work.

‍

2. Discovery & Analysis

Now that you have collected your data for analysis, it's time to investigate it. Depending on your use case, you may need structure discovery, content discovery, relationship discovery, or all three. If data content or structure discovery is important for your use case, make sure that you collect and profile your data in its entirety and do not use samples as it will skew your results.

Use visualizations to make your discovery and analysis more understandable. It is much easier to see outliers and anomalies in your data using graphs than in a table format.

‍

3. Documenting the Findings

Create a report or documentation outlining the results of the data profiling process, including any issues or discrepancies found.

Use this step to establish data quality rules that you may not have been aware of. For example, a United States ZIP code of 94061 could have accidentally been typed in as 94 061 with a space in the middle. Documenting this issue could help you establish new rules for the next time you profile the data.

‍

4. Data Quality Monitoring

Now that you know what you have, the next step is to make sure you correct these issues. This may be something that you can correct or something that you need to flag for upstream data owners to fix.

After your data profiling is done and the system goes live, your data quality assurance work is not done – in fact, it's just getting started.

‍

Data constantly changes. If unchecked, data quality defects will continue to occur, both as a result of system and user behavior changes.

‍

Build a platform that can measure and monitor data quality on an ongoing basis.

‍

Take Advantage of Data Observability Tools

Automated tools can help you save time and resources and ensure accuracy in the process.

‍

Unfortunately, traditional data profiling tools offered by legacy ETL and database vendors are complex and require data engineering and technical skills. They also only handle data that is structured and ready for analysis. Semi-structured data sets, nested data formats, blob storage types, or streaming data do not have a place in those solutions.

‍

Today organizations that deal with complex data types or large amounts of data are looking for a newer, more scalable solution.

That’s where a data observability tool like Telmai comes in. Telmai is built to handle the complexity that data profiling projects are faced with today. Some advantages include centralized profiling for all data types, a low-code no-code interface, ML insights, easy integration, and scale and performance.