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Delta Lake Architecture: Simplifying Data Engineering & Analytics Needs.

Chanderkant Sharma
Aug 24 · 4 min read

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

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…

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How Delta Lake Can Help In Solving These Challenges?

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Delta Lake Overview

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.

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Delta Lake Architecture

It organizes our data into layers or folders as 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.

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