Data Engineering Services

It depends on your primary workload. Snowflake is generally better for SQL-based Business Intelligence (BI) and ease of use (Data Warehousing). Databricks is superior for Machine Learning, AI, and complex data engineering (Data Lakehouse). The gap is closing (Snowpark vs SQL Warehouse), but Databricks remains the choice for AI-heavy organizations.

Initial setup ranges from $200K to $1M+. Ongoing costs depend heavily on compute usage. A typical mid-sized Snowflake implementation costs $150K-$400K/year. WARNING: Unoptimized queries can spike bills by 10x. You need FinOps controls (auto-suspend, resource monitors) from Day 1.

Oracle licensing costs increase 5-8% annually regardless of usage. 'Lift and Shift' to AWS/Azure often INCREASES costs because you bring inefficient schemas with you. The only escape is a refactor: migrate logic from PL/SQL stored procedures to an open compute layer (Python/Spark/dbt) and move data to Postgres or Snowflake.

Data Mesh is a decentralized approach where domain teams (e.g., Marketing, Sales) own their data products, rather than a central IT team. It solves the bottleneck of a central data lake. However, it requires high organizational maturity. If you have fewer than 50 data engineers, a Data Mesh is likely overkill. Stick to a well-governed Lakehouse.

Yes, traditional ETL (Extract-Transform-Load) is dead. Modern stacks use ELT (Extract-Load-Transform). You load raw data into the warehouse first (using Fivetran/Airbyte) and THEN transform it using SQL (dbt). This preserves the raw data for future use cases (like AI) that might need different transformations than your BI reports.

6-18 months for enterprise migrations. The bottleneck is not moving the data (that's easy); it's migrating the consumption layer (Tableau/PowerBI dashboards) and the transformation logic (Stored Procedures). Automated code conversion tools (like Impetus) can speed this up by 60-80%.