关于Delta Lake的技术困惑：为何仍需非ACID事务数据湖及双副本？

Let’s break down your questions one by one—these are exactly the kind of pragmatic decisions data teams grapple with every day:

Why do we still need non-ACID data lakes when Delta Lake exists? #

• Legacy tooling & ecosystem lock-in: A lot of older systems, custom scripts, or niche analytics tools only work with native data lake formats (like raw Parquet, CSV, or ORC files on cloud storage). Integrating Delta Lake requires updating drivers, rewriting ETL pipelines, or even replacing tools entirely—costs that don’t make sense for small teams or low-priority workloads.
• Cost efficiency for low-value data: Delta Lake adds overhead with transaction logs, version metadata, and optimized storage layers. For one-off batch loads, transient data, or datasets you’ll never update/delete after ingestion, the extra storage and processing costs are unnecessary. Traditional data lakes are simpler and cheaper for these "write-once, read-maybe" scenarios.
• Simplified workflows for basic needs: If you only need to dump raw data for ad-hoc analysis or long-term archival, and never need features like rollbacks, concurrent writes, or schema enforcement, a non-ACID data lake avoids overengineering. It’s a "good enough" solution that gets the job done without extra complexity.

Should Delta Lake replace regular data lakes entirely? #

Short answer: No—Delta Lake is a specialized tool, not a universal replacement.

• Delta excels where transactionality matters: If you’re dealing with concurrent writes, frequent updates/deletes, change data capture (CDC), or need time travel for debugging/auditing, Delta Lake is far more reliable than a traditional data lake. It solves real pain points that non-ACID lakes can’t address.
• Traditional data lakes still win on simplicity: For teams with limited engineering resources, or for use cases where you just need a raw data landing zone, a regular data lake is faster to set up and maintain. You don’t need ACID guarantees if you’re never going to modify the data after ingestion.
• Migration barriers are real: Many enterprises have petabytes of existing data in native formats. Migrating all of this to Delta Lake would require massive compute resources, testing, and downtime. It’s often more practical to adopt Delta incrementally for new datasets rather than replacing everything at once.

Why would anyone keep both a regular data lake and Delta Lake copy of data? #

• Tool chain compatibility: Some BI tools, legacy reporting systems, or third-party integrations can’t read Delta Lake tables natively, but work seamlessly with raw Parquet/CSV. Keeping a native copy lets you support these tools without reworking your entire stack.
• Gradual, low-risk migration: When transitioning from a traditional data lake to Delta Lake, teams often use a "dual-write" approach: write data to both systems during the transition period. This ensures business continuity while validating Delta pipelines and updating downstream consumers.
• Compliance & backup requirements: Some regulations mandate retaining raw, unmodified data in its original format. Delta Lake tables are optimized and versioned, so keeping a separate native copy serves as a compliant backup that matches the exact state of data when it was first ingested.

内容的提问来源于stack exchange，提问作者user13128577