What is Delta Lake?

Delta lake is considered as a defacto solution by organizations as it addresses all the challenges posed by data lakes and data warehouses, be it scalability, flexibility or governance. It is an open-source storage and management layer that transforms raw data stored in an organization’s data lake into a structured tabular format using Apache Parquet. It handles both batch and streaming data efficiently, ensuring that the data is consistent and accurate across different applications with a reliable unified source of truth. With advanced indexing and schema enforcement features, the delta lake improves speed at scale. It also strengthens data governance with robust audit logging.

Why Should Organizations Choose Delta Lake?

With the ever-growing volumes of data, organizations of the current era need a data storage solution that can handle massive datasets without degrading query performance. Delta lake can be a suitable choice for such organizations due to several reasons:

It leverages distributed processing and advanced indexing features to handle large datasets efficiently while enhancing query performance. The platform streamlines data engineering by executing ETL processes within the data lake. This reduces the time and effort to prepare data for analysis as no complex data pipelines are needed. Since it is an open-source platform, it can be deployed on various cloud platforms and can be integrated seamlessly with most of the data lake technologies. Additionally, delta lake improves data accessibility for organizations by providing access to real-time data instantly and making it available for data analysis, data science and ML applications. This ensures that organizations gain timely insights for decision-making and reliably meet compliance standards like GDPR and CCPA.

Delta Lake vs. Data Warehouse vs. Data Lakes

Delta Lakes, data warehouses and data lakes each of them follow their own rules and strategies for data management. Let’s see how they are different from each other:

Data warehouses collect data from various sources and consolidate it into one centralized repository. They work well for structured data and batch processing but struggle to analyze semi-structured and unstructured data, such as streaming data. It provides robust SQL support for data analysis but can be inflexible and expensive to maintain.

Data lakes operate without pre-defined schemas. It allows organizations to ingest and process large volumes of structured, semi-structured and unstructured data in its original format. Due to their flexibility and scalability, they are chosen by many organizations that have large volumes of data that they want to analyze to gain actionable insights.

Delta lake is a better choice for organizations in terms of scalability, flexibility and governance capabilities. Let’s look into some of the features that make it better from data lake and data warehouses:

Features of Delta Lake

• ACID compliant transactions: The ACID transactions capability provides the highest level of data consistency, reliability and integrity. When users perform multiple simultaneous transactions, delta lake manages to perform read, write and delete operations while maintaining data integrity. This is why users can see consistent views of their data even when new data is being written to the same table in real time.
• Metadata handling: The metadata is stored in a distributed manner across the cluster to handle metadata operations efficiently.
• Schema validation: If a user performs write operation on a table, and the schema of this table is not compatible with the predefined schema of the target table, then delta lake rejects the writes and raises an exception. This way, delta lake upholds data quality and consistency.
• Integrated batch processing and streaming: In delta lake, a single table can serve multiple purposes, from handling streaming data ingestion to acting as a destination for batch historical backfill and functioning as a target for interactive queries. The unified approach to data processing eliminates the need for separate batch and streaming systems, hence reducing operational overhead.
• Data versioning: Users can access previous versions of data and perform various functions on it such as auditing, debugging and reproducing experiments. It keeps the history of all the changes made to data so that users don’t have to waste their time on creating versions.