Paul Brabban, Lead Consultant at Equal Experts
With experience in software development, data engineering and machine learning, I specialise in data-intensive problems and decentralised data engineering at scale. My experience extends to leading teams, technical architecture and product development. Find out more about my experience and publications in my portfolio.
Contact me to see how I can help at paul@tempered.works.
In the last article of the series, I ran into difficulties as I tried to "time-travel" back to earlier points in time. That's an important capability for correct functioning and reproducing results. This article shows how to use window functions (also known as analytic functions) to simplify handling of processing time and avoid the previous problems.
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This walkthrough shows how I can use the functional programming techniques map and filter that I already know and love in SQL engines like BigQuery. These techniques give me a lot of processing power whilst keeping my SQL simple and relatively easy to understand. Unlike custom code, I can use the same SQL and infratructure I'd use to process ten rows to process ten billion rows in seconds.
I recently discovered and responsibly disclosed a vulnerability in the dbt analytics engineering solution. Google Cloud services are my default choice to process data, so I looked into how I could protect myself from data theft when I'm using BigQuery.
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In previous posts, I disambiguated transactions by filtering out any transient statements and noted that changes in primary key values cause big problems. Now I can start to answer useful questions about the current and historical changes in a source table, learning something important about window functions along the way.
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My work on dealing with multiple tables was interrupted when I discovered a subtle scenario that leads to DMS CDC output that cannot be correctly interpreted. I was unable to find a solution, but I will update this post if new information emerges.
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In the CDC output, I get a row for each statement executing in the transaction. Each row reflects the state of the database when that statement is executed. How do I filter out all the transient statements to get the final state of the row when a transaction has finished?
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Yesterday, Safety told me about CVE-2019-8341, a security issue affecting Jinja2. I'll walk through how I investigated and assessed the risk to my website and a dbt pipeline I operate in the public domain. I finish up with a commentary on why I think this vulnerability is real and should be fixed, and why I think we need to risk breaking potentially insecure usage to make vulnerability management manageable in the real world.
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Last time, I set up a CDC system using AWS RDS and DMS services. Now, I'll run some operations through the source database and show what that looks like in the CDC output. I'll introduce some metadata fields that are critical to figuring out what this CDC output means and set us up to look at the specific challenges I've had with interpreting these CDC outputs robustly to solve real-world problems reliably.
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Setting up Change Data Capture from Aurora Serverless PostgreSQL to S3 via the AWS DMS service. I'll walk through the demo setup, using the venerable Northwind dataset, calling out the problems and solutions on the way. The next post in this series will show the challenges we hit trying to work with this kind of CDC data and how we dealt with them.
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CVE-2018-20225 in all versions of pip tripped my vulnerability alerting this morning. If you're scanning for vulnerabilities using Safety, you've probably seen the same alarm. This post captures my reasoning and decision-making process to understand the risk and impact of this vulnerability and then deal with it.
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