As the environment of data management evolves, companies are invariably looking for ways to bring together their varied data assets in order to make sense out of them. In traditional data architectures, data silos make it difficult for organizations to maintain data quality or ensure consistency. More advanced analytics is out of the question. It is for these reasons that Delta Lake emerges as a technology whose time is already ripe: it brings with it unified data management and the rise of the data lakehouse architecture.
Historically, data was often stored in separate systems. Analytical workloads required relational databases; data warehouses were always strategic for their workload; and data lakes held raw, unstructured data. While data lakes did deliver huge volumes of data that were not only scalable but also could be adapted, usually they lacked what data warehouses have--ACID transactions, schema enforcement and solid data quality controls. So they often resulted in "data swamps," unmanaged and unreliable data repositories.
Delta Lake is an open-source distribution by Databricks which brings reliability and performance to typical data lakes, overcoming the traditional limitations. Based on existing data lake storage (like S3, ADLS, GCS or HDFS), it is a layer that combines Parquet data files with a rather aggressive file-based transaction log. The upshot of this approach is major data management functionality empowerment to data lake characters.
At its core, Delta Lake consists of Parquet data files and a transaction log.
ACID Transactions: Delta Lake's ACID compliance guarantees reliable, consistent data operations - even with concurrent writes and reads. This prevents data corruption and makes it possible for complex operations such as upserts (update or insert) or deletes on the data lake.
Schema Enforcement and Evolution: With Delta Lake, administrators can define and enforce schemas. That is, the system will reject anything that doesn't fit the prescribed format. Instead of those records being sent along further down in the pipeline to cause trouble later on when analysts realize they're all bad data, they're eliminated right there at the source. This improves data quality significantly. Delta Lake also supports schema evolution - i.e., any changes you want to make to existing schemas, such as adding new columns.
Time Travel (Data Versioning): Every write to a Delta table creates a new version. This means that users can access and return to earlier is earlier versions of the data, making rollback possible. They also obtain an audit trail thereby. Users can re-run machine learning experiments even after training their models on old data
Unified Batch and Streaming Processing: Delta Lake connects batch and streaming data so organizations have only the one copy of data for both real-time input real time analytics. This makes data pipelines simpler, more efficient and has decreased operating overheads.
Audit History: The transaction log carefully keeps an audit trail of all changes so that data tampering can be quickly caught.
Scalable Metadata Handling: Delta Lake can manage petabyte tables with billions of files and partitions, making it suitable for large-scale data environments.
Delta Lakes' features have been instrumental in the rise of the data lakehouse architecture. A data lakehouse marries the flexibility and economy of a data lake with the performance, reliability and governance features of a data warehouse. It lets organizations run data warehousing, advanced analytics and machine learning direct on their data lake - without having to maintain multiple redundant systems.
This unified approach is about the data environment, teamwork (data engineers, business intelligence analysts, and data scientists working together), and unification of all data work. The cornerstone of this new open platform era that brings data warehousing and advanced analytics together is an open-source storage framework like Delta Lake which runs on the amount of processing or storage required in respect to results.
It's important for modern enterprises to understand and employ the latest data technologies like Delta Lake. At Stratalligent, we specialize in guiding companies through the mysteries of unified data management and in helping them grasp data architecture by using lakehouses. Our ability can assist you in changing your data approach, ensure data quality and bring forth priceless insights.
To learn more about how Stratalligent can help you with unified data management and Delta Lake, please schedule a demo with us.
For more details, please contact contact@stratilligent.com.