Aug 24, 2021
Delta Lake Architecture: Simplifying Data Engineering & Analytics Needs
Today, most enterprises struggle with rampant data growth and we need to understand why traditional systems are failing. Over the next five years, global data creation is projected to grow to more than 180 zettabytes.
And data-driven decisions are changing our work and life, whether it’s the government, educational institutes, or other financial organizations, data is being seen as a game-changer. Data is the new oil. We need to find it, extract it, refine it, distribute it and monetize it.
So, we need a robust solution that can practically scale without a limit and can handle any amount of data variety, handle structure, semi-structured, and unstructured data, handle data coming in batches or real-time streaming and verify and validate the data. And it is quite clear that our traditional relational database systems can’t handle this.
Challenges with Legacy Data Architectures
Over time, we have seen the evolution of data-driven systems like data warehousing, big data workloads (batch & stream), lambda architecture, and data science workloads, but these systems are being worked upon by siloed data teams leading to duplication of data and reduced productivity.
These systems have problems like Data overwrite on the same path causing data loss in case of job Failure and updates in historical data.
The below diagram depicts the high-level scenario…
How Delta Lake Can Help In Solving These Challenges?
Delta lake architecture provides solutions for the above-mentioned problem statement. This does not mean, we no longer need data warehousing and data lake solutions, these are not going anywhere.
Let's get into details…
Delta Lake Overview
Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. Delta Lake runs on top of your existing data lake and is fully compatible with Apache Spark APIs.
Delta Lake is an open format storage layer that delivers reliability, security, and performance on your data lake — for both streaming and batch operations. Being open-source gives you the flexibility to migrate your workloads easily to other platforms.
Delta Engine sitting on the top of the data lake, is a high-performance, Apache Spark-compatible query engine that provides an efficient way to process data in data lakes including data stored in open source Delta Lake. Delta Engine optimizations accelerate data lake operations, supporting a variety of workloads ranging from large-scale ETL processing to ad-hoc, interactive queries.
Delta Lake Architecture
The Delta Lake Architecture is a massive improvement upon the conventional Lambda architecture.
At each stage, it improves our data through a connected pipeline and allows us to combine streaming and batch workflows through a shared file store with ACID-compliant transactions.
It organizes our data into layers or folders defined as bronze, silver, and gold as follows…
- Bronze tables have raw data ingested from various sources (RDBMS data, JSON files, IoT data, etc.)
- Silver tables will give a more refined view of our data using joins.
- Gold tables give business-level aggregates often used for dashboarding and reporting.
And these Gold Tables can be consumed by various Business Intelligence tools for reporting and analytics purposes.
Conclusion
To meet exponential Enterprise data growth and design robust data solutions around it, we need a solution that can practically scale without a limit and can handle any amount of data variety. The Delta Lake Architecture can be the right solution as it is a massive improvement upon the conventional Lambda architecture. Using this approach, we can improve our data through a connected pipeline that allows us to combine streaming and batch workflows through a shared file store with ACID-compliant transactions and provides the best of both worlds.
Do let me know what you think about the Delta lake…
Thanks for reading !!!
