What is Data Lakehouse?

A Data Lakehouse combines the flexibility of data lakes with the structured processing and analytics capabilities of data warehouses. It allows you to store, manage, and analyze data efficiently.

Why Migrate to a Data Lakehouse?

Data is vital for modern businesses, but traditional data warehouses face challenges as data grows. The Data Lakehouse provides a solution:

• Avoid getting locked into one vendor with a flexible, multi-cloud platform.
• Get insights quicker.
• Cut operational costs with unified governance.
• Get better value for your money.
• Easily handle data from anywhere.
• Work smoothly with popular BI and SQL tools.
• Enjoy a top-notch SQL development environment.
• Forget about managing infrastructure.
• Ensure precise data governance.
• Keep a single source of truth for all your data.

Who should migrate?

Any organization dealing with increasing data volumes, diverse data types, and the need for real-time insights should consider migration. It’s a solution for those looking to modernize their data infrastructure.

When to migrate?

Now is a great time. The rise of cloud technology, open-source software, and the shift to remote work due to the pandemic has made the migration landscape favorable.

Where to migrate?

You’ll be moving your data from your existing SQL Data Warehouse to a Data Lakehouse, often on a cloud platform like AWS, Azure, or Google Cloud.

The challenges that you may face while transitioning:

• Handling massive data volumes.
• Dealing with unstructured or semi-structured data.
• Need for real-time processing.
• Scalability concerns.
• Limited support for ML and AI.

The solutions that can help you mitigate these challenges:

• Decouple storage and computing for cost-efficiency.
• Embrace diverse data types.
• Simplify your data architecture.
• Harness the power of ML and AI.
• Work with files instead of a database.

Here are some organizations that got the benefits of the migration:

• Bread, a technology-driven payments company, reduced compute costs by 90% after migrating.
• Amgen, a biotech giant, improved data processing times by 75% and reduced compute costs by 25% post-migration.

Planning the Migration Journey? Here are the steps you could follow:

• Discovery Phase: Understand where you are and where you want to go.
• Assessment Phase: Evaluate migration solutions and prioritize use cases.
• Strategy Phase: Plan your architecture, ingestion, ETL, and reporting.
• Production Pilot Phase: Test and validate your approach.
• Execution Phase: Execute the migration.