Imagine a scenario where you need to integrate data from a newly acquired company's legacy systems (e.g., on-premise SQL Server, flat files, and a custom CRM) into your existing cloud-based data warehouse (Snowflake/Databricks). Outline your architectural approach for data ingestion, transformation, and ensuring data quality and governance for this complex integration.
final round · 5-7 minutes
How to structure your answer
Leverage a MECE framework for a comprehensive integration strategy. First, for Data Ingestion, establish secure connectivity to legacy systems (VPN, SSH tunnels). Utilize Fivetran/Stitch for automated CDC from SQL Server, and custom Python/Spark scripts for flat files and CRM API extraction, pushing data to a cloud staging area (S3/ADLS). For Data Transformation, employ Databricks/Spark for schema inference, data cleansing (deduplication, standardization), and enrichment. Implement dbt for Kimball-style dimensional modeling within Snowflake. For Data Quality & Governance, define data contracts and SLAs. Use Great Expectations/Soda Core for automated data quality checks (schema, value, consistency) at ingestion and transformation layers. Implement role-based access control (RBAC) in Snowflake and a data catalog (Collibra/Alation) for metadata management and lineage tracking. Establish a data governance council for policy enforcement.
Sample answer
My architectural approach for integrating legacy systems into Snowflake/Databricks follows a phased, MECE-driven strategy. For Data Ingestion, I'd establish secure network connectivity (e.g., VPN, Direct Connect) to on-premise SQL Server. I'd leverage Fivetran or a similar ELT tool for Change Data Capture (CDC) from SQL Server, pushing raw data to a cloud staging layer (e.g., S3). For flat files and custom CRM, I'd develop custom Python/Spark jobs utilizing APIs or SFTP for extraction, also landing in S3. Data Transformation would occur in Databricks, using Spark for schema inference, data cleansing (e.g., deduplication, type casting), and standardization. I'd then use dbt to build robust, Kimball-style dimensional models within Snowflake, ensuring data is optimized for analytics. For Data Quality and Governance, I'd implement Great Expectations or Soda Core for automated data quality checks at both ingestion and transformation layers, validating schema, referential integrity, and value ranges. Snowflake's native capabilities would manage RBAC and data masking. A data catalog (e.g., Collibra) would track metadata, lineage, and data ownership, ensuring compliance and discoverability. This layered approach ensures data integrity, auditability, and scalability.
Key points to mention
- • Phased approach (discovery, ingestion, transformation, quality, governance)
- • Hybrid ingestion strategy (connectors, file transfer, CDC)
- • Multi-layered data lakehouse architecture (bronze, silver, gold)
- • Data quality framework with automated checks and reconciliation
- • Data governance (catalog, ownership, RBAC, compliance)
- • Tooling choices (Fivetran, Stitch, NiFi, DataSync, Databricks, Snowflake, dbt, Great Expectations, Alation/Collibra)
Common mistakes to avoid
- ✗ Underestimating data discovery and profiling effort, leading to downstream quality issues.
- ✗ Failing to establish clear data ownership and governance early in the process.
- ✗ Ignoring data security and compliance requirements from the outset.
- ✗ Attempting a 'big bang' integration instead of a phased, iterative approach.
- ✗ Not planning for ongoing maintenance and evolution of the integrated data platform.