STAVAN MEHTA – QA Manager, Altruist
SDATA TESTING
“Data Testing with Great Expectations, Snowflake, Postgres, and Raw Files: Building Robust Testing Pipelines with AWS Airflow and GitLab”
In today’s data-driven world, ensuring the quality and accuracy of data is critical for businesses. The use of Great Expectations, Snowflake, Postgres, and raw files has become increasingly popular for data testing, enabling organizations to verify the quality of their data, ensure compliance with regulations, and improve decision-making.
In this conference, we will delve into the best practices for using Great Expectations, Snowflake, Postgres, and raw files to test and validate data. We will also explore how to build robust testing pipelines using AWS Airflow and GitLab.
The conference will feature expert speakers who will share their experiences and insights on topics such as:
- Setting up and configuring Great Expectations, Snowflake, Postgres, and raw files for data testing
- Best practices for creating test suites and expectations using Great Expectations
- Leveraging AWS Airflow for creating and scheduling data testing pipelines
- Using GitLab to manage and automate testing pipelines, including version control and continuous integration/continuous deployment (CI/CD)
- Tips and tricks for efficiently testing data at scale
- Case studies showcasing successful implementations of data testing using these tools and technologies
- Whether you are a data engineer, data analyst, or data scientist, this conference will provide you with practical insights and strategies for ensuring the quality and accuracy of your data, as well as enhancing your overall data management practices.
JEFF SING – Director of Engineering, Iterable
CONNECTING THE DOTS: HOW SERVICE DELIVERY REVIEWS LEAD TO EFFECTIVE QUALITY ROAD MAPPING
Do you ever feel in your role as a QA leader or Testing Engineer that you get assigned tasks that seem more like stop gaps than actual work that will drive lasting quality improvement? Does it ever feel challenging to get other engineering leaders to align on what projects will be more impactful, rather than reacting to what’s currently blocking them? Do you find it hard to express what quality really looks like to your senior leadership team?
In the last half-decade of running Quality Engineering Programs, he often had these challenges in determining what work we should be delivering. One of the tools I utilized to help navigate and establish direction was running a Quality Service Delivery Review which helped establish :
- What does healthy quality look like in your engineering organization (code quality, engineering process, deliverables?)
- Is the Quality Organization successful in delivering this (what KPIs and how is this consumed)?
- Is our overall engineering output actually delivering with quality (what happens if it’s not)?
- Are our customers satisfied with our product and how can we determine this?
Being able to answer the questions above will allow you to align with the engineering leadership team on what quality initiatives should be worked on quarter to quarter.
