Evaluating Effectiveness of Delta Lake Over Parquet in Python Pipeline

We have been witnessing rapid growth of data-intensive applications adopting efficient columnar storage formats, with Parquet becoming a widely used standard in modern data pipeline. Parquet has been efficient than traditional databases in the aspect of columnar storage, evolution of schema, efficient compression, and vast range of supported tools. While Parquet still had issues of capturing transactional logs and didn’t support ACID properties or atomic writes, which resulted in data corruption and metadata operations became expensive. Delta Lake, an open-source storage layer built on Parquet, addresses these limitations by introducing ACID transactions, schema evolution, time travel, and unified batch and streaming support. The study conducted on evaluating effectiveness of Delta Lake over Apache Parquet helps to gather and take advantage of the key benefits that delta lake provides in the world of big data and the optimization techniques used in Microsoft fabric as the baseline with delta lake being the backend storage bin. With the extensive increase of data driven critical applications in the IT sector, the columnar storage orientated data formats such as Apache parquet have become the industry standard in the big data world. Though parquet can compress and store different data formats like Json, xml, audio, csv, etc but it lacks the ACID properties that delta lake offers. Parquet formats offer high data compression efficiency and rapid query execution on the other hand lacks schema enforcement, reliability, and transaction guarantees which is crucial in this modern world. In short Delta Lake, an extension of parquet is an open-source storage layer introduced on top of parquet with delta logs that store incremental transaction logs helps to eliminate the limitations of parquet by adding ACID transactions, schema evolution, time travel and unified batch streaming support. This research inquires that parquet offsets to read low optimal workloads (does not support parallel operations and require batch processing) while delta lake provides remarkable advantages for heavy workloads that require data versioning, reliability, and parallel execution. This research draws the effectiveness of Delta Lake in comparison to Parquet by reviewing critical performance parameters such as read/write speed, concurrency management, update/delete efficiency, and operational reliability, within both batch and streaming data (parallel) processing in a Python-based data pipeline.