Delta Lake: A solid Data Platform for Big Data


According to the international market research institute IDC, the global amount of data produced will increase from 64 zettabytes in 2020 to about 175 zettabytes in 2025. For your reference: 1 zettabyte corresponds to 1 trillion bytes or 1 billion terabytes!

From a business perspective, the use of data creates enormous opportunities. The Federation of German Industries (BDI) estimates the value creation potential of the data economy at up to EUR 425 billion by 2025 for Germany alone, and up to EUR 1.25 trillion for Europe as a whole. Data is a key competitive and value-creation factor as well as a driver of innovation in the economy. Among other things, they can help to make better-informed business decisions, optimize processes, or develop entirely new business models. At the same time, handling data is a major challenge for all stakeholders. This is particularly true for SMEs, which need to maintain technical and organizational expertise in order to benefit from the various value creation potentials of the data economy. Therefore, it is important to have a solid and reliable platform for managing and analyzing big data. Delta Lake is such a platform. In this article, we would like to introduce Delta Lake to you in a little more detail and discuss its various uses cases.

What is Delta Lake?

Delta Lake is an open source storage framework that enables building a lakehouse architecture with compute engines such as Spark, PrestoDB, Flink, Trino and Hive. It provides APIs for Scala, Java, Rust, Ruby and Python. Delta Lake offers a solution that combines the benefits of data warehousing, data lakes, and streaming, with features such as ACID transactions, versioning, and unified batch and streaming processing. Delta Lake can be run on on-premises servers or in the cloud and provides a wide range of capabilities required to manage Big Data.

Why Delta Lake?

Delta Lake offers several advantages over other storage frameworks. These include:

Transactional integrity:

Delta Lake provides transactional integrity for Big Data. Which means that the data in Delta Lake supports atomic ACID transactions, ensuring consistency and isolation. This feature enables developers to safely and reliably execute complex ETL (Extract, Transform, Load) processes. Transactional integrity can prevent erroneous or inconsistent data, which in turn enables reliable and accurate analysis.

Scalability and performance:

Delta Lake is designed to handle Big Data at scale. It uses the benefits of Spark, a powerful cluster computing framework, to enable scaling of workloads across many compute nodes. This results in improved processing speed and performance. Delta Lake also enables incremental updates and optimized queries, making complex query operations more efficient and faster. In addition, Delta Lake can run on local servers as well as in the cloud.

Data quality and data management:

Delta Lake provides mechanisms to ensure data quality and data management. By using Schema Evolution, data structures can be updated and managed without affecting existing data. This simplifies the handling of shifting requirements and facilitates collaboration in teams. Delta Lake also supports versioning, which makes it possible to access previous versions of data and track changes. This is especially important for traceability and meeting compliance requirements.

Reliability and recoverability:

By using write-ahead logs and snapshots, Delta Lake ensures that data changes are secure and reliable. In the event of failure or corruption, data can be easily recovered without data loss. This contributes to the security and stability of data processing and mitigates potential risks.

Use cases for Delta Lake

Delta Lake can be used in a variety of use cases, including:

  1. Real-time analytics: Delta Lake can be used for real-time analysis of data streams to help organizations respond quickly to changes and make informed business decisions.
  2. Machine Learning: Delta Lake can be used to manage training data for machine learning models.
  3. Data warehousing: Delta Lake can be used as a data warehouse solution for storing and processing data.
  4. Data integration: Delta Lake can integrate and unify disparate data sources to help organizations gain comprehensive insights from their data.


Delta Lake has proven to be a game changer for Big Data processing. Supporting transactional integrity, scalability and performance, data quality and data management support, and reliability and recoverability, Delta Lake provides a comprehensive solution for organizations looking to process and analyze large volumes of data. This enables companies to gain valuable insights, make informed decisions and increase their competitiveness. Delta Lake has undoubtedly changed the way we process Big Data and will continue to play an important role in the future.