How does ETL cloud migration affect data quality?

Simply put, ETL - Extraction, Transform and Load - is about getting data from source(s), transforming it as needed and loading to desired target(s). ETL is foundational for data quality because it not only moves data but also ensures that the data is cleansed, standardised, and validated, making it reliable and valuable for downstream analysis and decision-making. Here's few pointers on how it help ensure data quality: - Data validation to ensure data matches its expected format, is complete, and is accurate. - Consistency, can pull from various sources into a unified format. - Can incorporate audit and monitoring mechanisms. - Historical data dandling. - Error handling.

In my opinion, if there is a skill gap or time constraint for ETL cloud migration, we can opt for ELT. In ELT the the data from various sources will be orchestrated on cloud using low code pipelines. Once the data is on cloud then the complex transformations can be done using more code extensive pipelines. This approach will collect all data in data lake and can then be processed later when the team is ready.

Planning an ETL cloud migration involves addressing both technical and team-related challenges to maintain high data quality. Key considerations include: - Skill Gaps: Traditional ETL teams may need training on cloud tools, demanding time and resources. - Security & Compliance: Adhering to regulatory standards is vital when transitioning data. - Integration Hurdles: On-prem tools might not easily align with cloud solutions, affecting data quality. - Cost Factors: The cloud's pay-as-you-go model can make intensive data checks more costly. - Tool Variability: Different cloud ETL tools can handle data differently, risking quality issues; same goes for monitoring.

Ensuring data quality during ETL cloud migration involves a mix of strategic planning, rigorous testing, and continuous monitoring. Here are key steps: - Assessment & Profiling: Begin by evaluating current data, identifying anomalies and understanding data attributes. - Cleansing & Validation: Set validation rules to maintain consistency during transformation. - Testing & Reconciliation: Test in small increments and compare post-migration data to the source to ensure accuracy. - Monitoring & Feedback: Continuously oversee ETL processes, with alerts for discrepancies and a system for end-users to report issues. - Documentation & Updates: Keep detailed ETL process records and stay updated with the latest cloud ETL practices.

There are multiple strategies to consider while dealing with data quality, broadly broken down would entail the below: 1. Initial data check: profile the data at the source to understand what the data looks like and what is to be expected during migration. This should act as a baseline for ensuring data integrity 2. Checks for data in motion: based on the profile of the data some check points and rules should be created to ensure that data quality is maintained in the ETL process 3. DQM framework: although this is a long term process it should be encouraged and utilised to analyse the data quality better. It can help call out skews, ensure better null treatment and check data quality in real time

The best way of ensuring quality is bringing more transparency to your data and ensuring higher adoption Your data model and data definitions should be as much of a white box solution as possible to enable ease of access to your stakeholders. By migrating your ad-hoc processes to a cloud based ETL tool and educating your stakeholders about usage, you can scale up both storage and processing thus increasing opportunities of data consumption, catering to more users,reducing downtime and increasing reliability.

Migrating ETL to the cloud can elevate data quality through: - Democratisation & Innovation: It offers even small businesses advanced computational and ML tools, ensuring precise data processing. - Incorporating AI and streaming data from the cloud guarantees quality data analysis and transformations. - Scalability, Cost Efficiency & Timely Updates: The cloud's ability to handle vast data sets combined with cost efficiency & automatic tool updates, maintains data quality by leveraging the latest standards and protocols. - Security & Central Monitoring: Cloud's stringent security and centralised monitoring ensure data integrity and consistent quality.

In my opinion, if there is a skill gap or time constraint for ETL cloud migration, we can opt for ELT. In ELT the the data from various sources will be orchestrated on cloud using low code pipelines. Once the data is on cloud then the complex transformations can be done using more code extensive pipelines. This approach will collect all data in data lake and can then be processed later when the team is ready.