How Delta Lake Powers Scalable BI & AI: Real-World Practices and Optimizations

1. Guandata Analytics Product Overview

Guandata, founded in 2016 in Hangzhou, provides an end‑to‑end data analysis and intelligent decision‑making platform for enterprises across retail, finance, internet and other domains. The product emphasizes usability, offering features such as a low‑threshold smart ETL that lets business users build data pipelines by drag‑and‑drop, and a Delta Lake‑based data explanation module for multidimensional analysis.

One notable customer, a leading bank, runs a BI platform with over 40,000 monthly active users, achieving 3‑5 second response times on 90% of queries, powered by an 18,000‑core Spark cluster backed by Delta Lake storage.

2. Delta Lake Application Practice

Data Lake Architecture

Delta Lake is an open‑source data‑lake storage solution from Databricks that integrates tightly with Spark. It sits on top of HDFS, object storage, or cloud storage and supports both batch and streaming ingestion. The architecture includes layers for data ingestion (full, incremental, CDC), storage and metadata management, processing and scheduling, and finally BI, data‑science and application layers.

Delta Lake Features and Applications

Key capabilities include ACID transactions, full/incremental updates, schema management, multi‑engine support (Spark, machine‑learning frameworks, etc.), data versioning, partitioning, storage‑compute separation, and unified batch‑stream processing.

Delta Lake stores table changes in a

_delta_log

directory; each commit creates a JSON file and every ten commits generate a checkpoint file to accelerate Spark reads. Partitioned data appears as directories like

date=2019-01-01

containing Parquet files.

ACID Transactions

Delta Lake uses the delta log to track file writes. Each transaction writes data files, then records the changed file paths in a new log entry, incrementing the table version. It employs optimistic concurrency control with three phases: read latest version, write data files, then validate and commit. The default isolation level is serializable writes, providing high throughput.

Concurrency and Optimization

To avoid conflicts during concurrent updates, a write‑operation queue can serialize actions such as small‑file compaction and version cleanup. Full and incremental updates are supported: full overwrite for initial loads, incremental loads using timestamps, and append‑only writes that preserve historical data.

Schema Management

Delta Lake enforces schema compatibility by default but allows schema evolution via the

mergeSchema

option. When source schema changes (e.g., column added or removed), enabling

mergeSchema

updates the target table schema accordingly.

Multi‑Engine Support

Spark remains the core engine for batch and streaming processing, while ClickHouse is used for query acceleration in specific scenarios. Delta‑rs (a Rust library with Python bindings) offers lightweight read access without launching a full Spark job, improving performance for algorithmic workloads. Standalone Reader (Java) provides simple data preview capabilities.

Time Travel

Delta Lake’s versioning enables “time travel,” allowing users to query historical snapshots—useful for algorithm experiments that compare results across data versions.

Partitioning

Partitioning (commonly by date) isolates write paths, enabling concurrent writes without conflict and improving query performance for large tables.

Streaming Ingestion

Real‑time data is captured via Kafka, then processed with Spark Structured Streaming to incrementally update Delta Lake tables, making fresh data immediately available to downstream BI and AI services.

Performance Optimizations

Small‑file compaction to reduce the number of files and improve Spark query speed.

Vacuuming to clean up obsolete versions, especially when time‑travel is not needed.

Column‑pruning: read only required columns to leverage columnar storage.

Regularly upgrade Delta Lake versions to benefit from new features such as Z‑Order indexing.

3. Summary and Outlook

Guandata will continue to adopt new Delta Lake features (e.g., Z‑Order, enhanced DML), move toward a cloud‑native architecture integrating multiple engines like Databricks and ClickHouse, expose Delta Lake via SQL interfaces, and develop data‑catalog‑based asset management. The team also contributes to the open‑source community.