Unveiling the Power of Lakehouses: Bridging Gaps Across Data Warehouses, Data Lakes, and Beyond
BACK TO THE BASICS— INTRODUCTION TO DATA WAREHOUSE, DATA LAKE AND DATA LAKEHOUSE…
✎ Co-author — Sen Sayantani
❑ Overview
In the realm of analytics, one of the most common query we get among customers embarking on their journey is: “What sets Data Lake, Data Lakehouse, and Data Warehouse apart?”. The use of these jargons interchangeable in their development often adds to the confusion. The transition from Data Warehouses to Data Lakes marks a significant evolution in data management. This blog aims to demystify these essential concepts in a straightforward manner, shedding light on various industry jargon along the way.
❑ Let's understand the End Goal?
What is the End goal?
The end goal is simple — Get value from data!!!
We receive data from various sources in diverse formats, and the sheer magnitude of data can sometimes be overwhelming. So, how do we extract value from this data? Through the ability to analyze it and derive actionable insights. The resulting business value can manifest tactically, improving day-to-day operations, and strategically, providing long-term benefits.
❑ What is Data Warehouse?
A Data warehouses is a centralized data repository of integrated data from one or more disparate sources in a unified format to support reporting and data analysis which is considered as a core component of business intelligence.
ETL (Extract, Transform, Load) extracts data from diverse sources, transforms it to fit analytical requirements, and loads it into the data warehouse, ensuring data quality and consistency. The data warehouse, in turn, serves as a centralized repository for organized and optimized data, facilitating efficient querying and reporting for business intelligence purposes.
A data mart is a simple form of a data warehouse that is focused on a single subject (or functional area), hence they draw data from a limited number of sources such as sales, finance or marketing. The sources could be internal operational systems, a central data warehouse, or external data.
Examples of data warehouses include Azure Synapse Dedicated Pool, Amazon Redshift and Google BigQuery.
Advantages:
🗹 It is optimized for complex queries and analysis for fast and efficient retrieval of large datasets.
🗹 It commonly incorporates procedures to clean and transform data before loading, enhancing data quality and ensuring the consistency and reliability of information used for analysis and reporting.
🗹 It supports business intelligence and decision-making users to create sophisticated reports, dashboards, and visualizations, enhancing the presentation of insights to stakeholders.
Disadvantages:
🗷 It couples compute and storage together which proves expensive for organizations and is not scalable after a certain point.
🗷 It is mostly limited to structured data in the form of tables, wherein we need to define schema and adhere to it, hence they are not suitable for semi-structured and un-structured data.
🗷 Traditional data warehouses are designed for batch processing. For applications that require real-time data processing(up-to-the-minute) data, a data warehouse may not be the most suitable solution.
🗷 Organizations may face vendor lock-in if they choose a specific vendor’s technology for their data warehouse. Switching to a different platform later on can be challenging and costly.
❑ What is Data Lake?
With data warehouse, Organizations could get great value from the structured data, but they also felt that there are other types of unstructured data like textual data (Emails, procedure documents), image data (Medical images— X Rays, CT, and MRI scans), audio data (Call center transcripts), and IOT data (Industrial Sensors) which could of tremendous business value.
They realized that they could use image data to identify quality defects in manufacturing, audio data in call centers to analyze customer sentiment & validate the quality of service, and video data of remote operations such as oil and gas pipelines to perform predictive maintenance. Data lake was born!!!
The data lake is an amalgamation of all of the different kinds of data found in the organization.
With flexibility, cost-effectiveness, and real-time data support, data lakes empower data scientists and analysts to extract meaningful knowledge, valuable business insights and uncovering hidden patterns. This really fosters informed decision-making and drives innovation in today’s data-centric landscape.
Advantages:
🗹 It supports all kinds of data — structured, unstructured & semi structured data.
🗹 It stores data in generic and open file formats, such as Apache Parquet and ORC directly accessible to a wide range of analytics engines.
🗹 It decouples storage and compute so that both can be scaled independently (If deployed on cloud).
🗹 It supports both Real time and Batch processing.
🗹 It is cost-effective compared to data warehouses.
Disadvantages:
🗷 The data quality can be low due to the raw nature of the data (they can easily become a “Data Swap”)
🗷 One of the key disadvantages of Data Lake is lack of support of ACID transactions making it not as performant as Data Warehouse for structured data.
🗷 Due to this lack of performance and quality issues, enterprises use ETL (Extract/Transform/Load) to copy a small subset of data in the data lake to a downstream data warehouse for the most important decision support and BI applications. This dual system architecture requires continuous engineering & and can incur expenses associated with data movement between the lake and warehouse.
🗷 It is Complex to set up and maintain.
Examples of data lakes include Amazon S3 and Microsoft Azure Data Lake Storage.
❑ What is a Data Lakehouse?
Data lakes were really well suited for processing unstructured data and machine learning applications but suffered from their lack of ACID transactions. In order to address some of these limitations and challenges associated with traditional data warehouses and data lakes, Databricks introduced the concept of “Lakehouse”. A Lakehouse is a modern hybrid data solution that combines the best features of a data warehouse and a data lake.
A data lakehouse combines the flexibility of a data lake allowing you to store unstructured data as well as the management methods of a data warehouse. It supports ACID operations, provides better BI performance than lakes, and is well-governed with fine-grained access controls.
At the core of the Lakehouse concept lies the emphasis on open standards and file formats. These open metadata layers shift the management of the unstructured segment of the data lake from a file-centric approach to a more organized, logical table-level structure. Notable examples of such transformative technologies include Delta (developed by Databricks), Hudi (crafted by Uber), and Iceberg (engineered by Netflix).
If you want to want to understand different data lake challenges & how ow delta helps in overcoming, please read my blog Delta Lake
This foundational lakehouse logical layer is what separates a data lake from a data lakehouse.
Advantages:
🗹 It is a simple, easy, and open architecture that gives you the best of warehouses and lakes.
🗹 It allows you to use a single tool for data lake and data warehouse removing Data Redundancy.
🗹 Optimizing performance — It helps in enabling various optimization techniques, such as caching, multi-dimensional clustering, z-ordering, and data skipping, by leveraging file statistics and data compaction to right-size the files.
🗹 Data consumers can access data directly from the lakehouse using any compatible compute engine.
🗹 It helps you to implement BI to AI — all kinds of workloads.
🗹 It helps you to implement a well-governed and secure system.
Examples of data warehouses include Microsoft Fabric, Azure Databricks.
❑ Key Differences
Let's summarize & explore the table below to understand the distinctions between Data Warehousing, Data Lakes, and Data Lakehouse.
I hope this blog helped you in understanding the difference between Data Warehouse, Data Lake & Data Lakehouse. There is still more to come…Happy Learning!!!
Please Note — All opinions expressed here are my personal views and not of my employer.
Thought of the moment-
“If you want to shine like a sun, first burn like a sun.” — APJ Abdul Kalam
