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Database: Delta Lake or PostgreSQL

NoviKamayana
New Contributor

Hey all,

I am searching for a non-political answer to my database questions. Please know that I am a data newbie and litteraly do not know anything about this topic, but I want to learn, so please be gentle. 🙂 

Some context: I am working for an OEM that is currently trying to establish a somewhat data-driven enterprise. Currently we are running to data warehouse, which seems counterintuitive to me (e.g. due to increase consumption of resources). For one we have Delta Lake and we also run our data on a PostgreSQL server. There are a lot of discussions between our Data and Tech team. The data team is defending the usage of Delta Lake and our Tech team is defending the usage of PostgreSQL servers. Enter the newbie: i would like to understand which of the two concepts (?) make more sense in establishing a data-driven enterprise that aims for ML, AI-driven use cases and working with our large customers that are more and more developing in a direction of data lakes. 

Please help me understand whether our current approach makes sense?

Thank youuuu

1 REPLY 1

Kaniz_Fatma
Community Manager
Community Manager

Hi @NoviKamayanaLet’s dive into the world of data storage and explore the differences between Delta Lake and PostgreSQL. As a data newbie, you’re on the right track to learn more about these concepts. 🌟

Data Lake vs. Delta Lake

Benefits and Limitations of Data Lake

Data Lake is like a vast reservoir where you can store all types of data—structured, semi-structured, and unstructured—without worrying about predefined schemas. Here are some key benefits of Data Lake:

  1. Unified Storage: Data Lake allows you to ingest and store massive volumes of diverse data at a fraction of the cost compared to traditional data warehouses. It’s like having a giant repository where you can throw in any data format.

  2. Scalability and Cost-Effectiveness: As your data grows, Data Lake scales effortlessly. Plus, it’s cost-effective because you don’t need to transform or preprocess data before storing it.

  3. Machine Learning Capabilities: Data Lake provides a fertile ground for machine learning (ML) models. You can train ML algorithms directly on raw data without the constraints of a fixed schema.

  4. Advanced Analytics and Insights: With Data Lake, you can perform complex analytics, discover patterns, and gain valuable insights from diverse data sources.

  5. Data Governance and Security: Although Data Lake lacks strict schema enforcement, it compensates with robust data governance features. However, be cautious about data quality and compliance.

Overcoming Data Lake Challenges with Delta Lake

Delta Lake steps in as a game-changer. It addresses some of the limitations of traditional Data Lakes:

  1. Data Quality and Consistency: Delta Lake introduces transactional capabilities, ensuring data consistency and quality. It combines the best of both worlds: the flexibility of a Data Lake and the reliability of a data warehouse.

  2. Schema Enforcement: Unlike traditional Data Lakes, Delta Lake enforces schema upfront. This means you get the benefits of schema-on-read (like Data Lake) while maintaining data integrity.

  3. ACID Transactions: Delta Lake supports ACID transactions, making it suitable for critical use cases. You won’t lose sleep over data corruption or incomplete writes.

  4. Time Travel: Delta Lake allows you to time-travel through data versions. You can rewind to a specific point in time, which is incredibly useful for auditing and debugging.

Which Approach Makes Sense for Your Data-Driven Enterprise?

  1. Delta Lake: If you’re aiming for ML and AI-driven use cases, Delta Lake is a strong contender. It provides the necessary reliability, consistency, and scalability. Plus, it aligns well with the trend toward data lakes.

  2. PostgreSQL: While PostgreSQL is a robust relational database system, it’s not inherently designed for handling unstructured or semi-structured data. However, if you have existing applications relying on PostgreSQL, consider integrating it with Delta Lake for the best of both worlds.

Conclusion

Your data team’s preference for Delta Lake aligns with modern data practices. As you work with large customers and venture into ML and AI, consider embracing Delta Lake as part of your data-driven journey. And remember, even data newbies like you can make informed choices! 🚀

For a deeper dive, explore Databricks’ unified Lakehouse platform, which seamlessly integrates Delta...1.

 
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