How can you ensure the quality and reliability of your data sources, pipelines, and warehouses?

Validate the networks: Make sure that the networks connecting the source systems and the cloud environment are working properly. ToVerify bandwidth, latency, reliability of sources. Verify the different types of data: Different types of data require different migration strategies.structured data can be migrated using a traditional ETL process, while unstructured data may need to be migrated using data lake ingestion tools. Verify the source connections: Make sure you have necessary permissions to access the source systems and that the data can be extracted without impacting the performance of the source systems. Compress large data files: Compress large data files can reduce the time and bandwidth required to migrate the data to the cloud.

Depending on the type of data source, you may encounter several issues thay may cause data to be inconsistent. Hence, it is a good practice to always check for data quality and consistency before extracting data from the source. For instance, when extracting from an API, write a data quality check that runs on a sample size of data being extracted before extracting the full data. Another way to keep data consistent is to maintain documentation between platforms to be aware of data changes. For example, if there is a schema change on a database, there should be a channel where developers can document the changes, which should be shared with downstream consumers so that we are informed about the changes.

You want to ensure your data matches throughout its journey. From source, into a raw layer… from raw into a bronze layer… from bronze into a gold production layer. Each step can have either manual or automated checks but ultimately the goal is to make sure that data remains intact throughout the entire process.

The next step is to ensure data pipelines are stable. A few tips. 1. Ensure tasks and dependencies are well designed Data pipelines are nothing but a series of tasks that collect, transform, and store data. A well-designed pipeline features tasks that are logically distinct and separated. For instance, an extraction task might be reusable for multiple source systems, and should be separated from transformation tasks to streamline error resolution. 2. Use the right tools with best practices When selecting a tool, be sure to assess at least 3 options for a similar need. A good strategy is to research what big tech companies are using for their data operations. List down the pros and cons in a table and brainstorm with the team about it.

Based on business requirements, data pipelines may be near real-time or batch. These pipelines will be orchestrated using orchestration tools. If a job fails, the team will be alerted through incidents, and the respective support person should receive a call to troubleshoot the issue. Similarly, if any data validation check fails, it should be promptly notified so the team can troubleshoot and resolve the issue.

The data warehouse is the platform to store all structured data to be further utilized for business requirements. Hence, it is important to -Ensure that the data warehouse is well structured for optimization and security. Do extensive research on star and snowflake schemas, facts and dimensions tables, and how you can integrate them according to your business's data needs. -Access control and security should be enabled on dataset and table level. Ensure that the ones accessing the data are only the people who need to. -Messy data warehouses will only reduce trust from externals. Hence, integrating metadata management for data assets which allows stakeholders to search through organized data will help them achieve their goals faster.

Once we say data quality framework, it covers all 6 key components 1. Completeness 2. Accuracy 3. Consistency 4. Validity 5. Uniqueness 6. Integrity. We have to make sure all these dimensions are covered, data quality framework can be build in-house or we can purchase tool from the market. Once, we buy software for framework, we have to make sure this fulfills our requirement.

There needs to be processes around ensuring data accuracy in the data warehouse. In your data pipelines, set up data quality checks based on business logic to maintain data integrity. For instance, for a revenue table, it is wise to integrate a test on the revenue numbers so that it matches expectations. Visualize test outcomes on a validation dashboard and share them with business stakeholders. This not only injects confidence in them, but they can also suggest more tests according to their needs. Establish alerts based on data tests, notifying relevant personnel via your company's preferred channel like email or Slack. The alerts will preemptively address issues, preventing downstream task failures and panic.

This is part of the underlying infrastructure and the acceptable RTO and RPO times, as well as the service level agreements. If it are on-premise datacenters, these should be discussed with your datacenter teams, with the public or private cloud vendors it is a matter of paying for the right service and enabling redundancy. This can be multi-zone but as well as just providing the right failovers. A terraform/ansible/docker script allows to reduce the RTO to redeploy the infrastructure quickly without manually needing to reconfigure it.

1) Define clear data quality standards and metrics 2) Implement data quality processes and tools 3) Educate and train employees on quality standards 4) Empower employees to take ownership of data quality Create a data quality dashboard: A data quality dashboard can be used to track and monitor key data quality metrics. This can help the team to identify and address data quality issues early on. Implement data quality gates: Data quality gates can be used to prevent data quality issues from being loaded into a production data warehouse unless it meets certain quality standards. Establish a data quality community of practice,This can help the team to continuously improve their data quality practices.

Apart from the ideas presented, here are some other aspects to consider to maintain standards Automate Data Testing: Introduce automated testing for data pipelines. For instance, implement unit tests, integration tests, and end-to-end tests.Monitor these tests and get alerted to any failures. Version Control and Change Management: Use version control systems like Git to track changes in your data pipelines.Ensure a change management process is in place to document and review changes to data infrastructure. Audit Trails: Maintain logs of all data operations Maintain Solid Documentation: Document data sources, transformations, business rules, and any assumptions made. Ensure that teams understand data sources and their nuances.

Use Data Monitoring and Alerting along with Data Lineage tracking to ensure data flows through the system and can identify quality issues, also helping them response early avoid major glitches in the data system. To maintain quality and reliability of data sources, pipelines and warehouse it’s important to have a proper process to solve data problems by using appropriate tools and methods.

Data Governance: Assign data ownership. Define clear data quality standards. Manage metadata effectively. Data Source Quality: Profile data sources. Implement data validation rules. Monitor for changes. Data Pipelines: Design ETL processes with data validation. Include robust error handling. Automate testing and monitoring. Data Warehouses: Design efficient data models. Prioritize data security. Implement disaster recovery and optimization. Data Monitoring: Use monitoring tools for real-time anomaly detection. Set up alerts and notifications. Documentation and Versioning: Document configurations. Use version control for changes. Regular Audits: Periodic audits for continuous improvement.