What Is The Difference Between Data Lake and Data Warehouse?

So, the data warehousing is a late 1980s concept when the term business data warehouse was given by the IBM researchers Barry Devlin and Paul Murphy.

It was a critical thinking to make the flow of data streamlined from the operational systems. This further helped in reducing redundancy and costs and making better data-based decisions.

On the other hand, data lake is a term given by James Dixon, who was the CTO at Pentaho at that time.

Data lake came out to be a modern solution to store huge volumes of raw, structured, and unstructured data in a single, scalable repository, often built on Hadoop systems. 

This blog will learn about Data Lake vs Data Warehouse in detail.

What is a Data Lake?

A data lake is a storage system to keep a massive volume of data in its raw and natural format. It can store: 

• Structured data like tables from databases
• Semi-structured files including CSV, JSON, XML
• Unstructured data like emails, images, audio, video, etc.

A data lake is flexible. It uses a ‘schema-on-read approach’ that means it stores everything as it is and provides structured data only when you need it for analysis.

Thus, it becomes an ideal storage for modern use cases such as advanced analytics, big data processing, and machine learning. Data lakes can live in an organization’s data centers or in the cloud. Cloud storage makes it more scalable for huge data volumes.

Data Lake Example

There are various data lake examples used by many organizations. These include Amazon S3, Google Cloud Storage, or distributed systems like Apache Hadoop HDFS. 

One unique example is the Personal DataLake project at Cardiff University. It helps individuals collect and manage their personal big data in a single place. 

Benefits of Data Lake

Now, let’s discuss the advantages of data lakes.

• A data lake is highly scalable, as it is based on a cloud-based system for storing a large amount of data.
• Whether the data is raw, structured, or semi-structured, a data lake is flexible enough to handle all types of data.
• You can ingest data in real-time via batch processing, and that’s great for streaming data sources.
• As the data is stored in the original form, it’s perfect for AI/ML and advanced data analytics.
• It provides you with pocket-friendly storage solutions like cloud and Hadoop.

What is a Data Warehouse?

It is a single-point system that brings all data together for analysis, reporting, and other intelligence tasks. Another name for a data warehouse is an enterprise data warehouse (EDW).

All the current and historical data from various sources are integrated into a single repository. These sources could be CRMs, ERPs, external APIs, flat files, etc.

A data warehouse works on a ‘schema-on-write approach’ and is not like the traditional databases optimized for daily transactions. It processes information using ETL or ELT and offers quality information to end users. This helps analysts and business managers in faster querying, reliable analytics, and better data decisions.

The data warehouses are likely to hold on to data in the form of relational tables and provide a better summary of large datasets. 

Data Warehouse Example

There are many renowned cloud-based data warehouse software. Some of them are Amazon Redshift, Google BigQuery, and Snowflake. These are popular for high scalability and real-time analytics. 

Snowflake

4.6

Starting Price

$ 2.20      

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Benefits Of Data Warehouse

Let’s discuss the advantages of Data Warehouse.

• A data warehouse provides you with high-performance querying and reporting.
• It avoids data errors by already storing data in a structured format.
• As the data is structured, it helps data scientists to build accurate dashboards and reports and do so in less time.
• Your data remains safe in a data warehouse as it offers more security and compliance than a data lake.
• It makes business workflows easier as it can integrate with many third-party business intelligence software.

Amazon Redshift

4.3

Starting Price

Price on Request

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Architecture & Structure Of Data Lakes and Data Warehouse

Let’s break down data lakes and data warehouses differences in architecture, storage, and data flow:

Data Lake Architecture

• Storage Model:
  It works on the model ‘Schema-on-read’. Data gets stored in raw format, and the schema is applied only when one needs to read or query the data.
• Data Types:
  It works with all data types, whether structured, semi-structured, unstructured, or binary data.
• Layers:
  • Raw data zone: stores raw, ingested data as-is.
  • Cleansed zone: processed and cleaned data ready for further use.
  • Curated zone: organized, aggregated data optimized for analytics and machine learning.
• Data Flow:

Data flows in from sources like IoT devices, APIs, CRM, and ERPs, and is processed in batches or streams. 

• Storage Systems:
  Cloud storage like, Amazon S3, Google Cloud Storage, or distributed file systems like Hadoop HDFS.

Data Warehouse Architecture

• Storage Model:
  It works on a ‘Schema-on-write’ model. The data gets cleaned and structured before it enters the warehouse.
• Data Types:
  It primarily supports structured data from relational databases and enterprise systems.
• Layers:
  • Staging area: stores raw incoming data on a temporary basis
  • Data integration/transformation layer: performs cleaning and enrichment of data
  • Presentation layer: has highly structured data and is stored in tables, best suited to querying and reporting
• Data Flow:
  Data is pulled out of the operational systems and transferred to the warehouse via either ETL or ELT processes.
• Storage Systems:
  Already present databases in your business or cloud data warehouses such as Amazon Redshift, Snowflake, and Google BigQuery.

Key Differences: Data Lake vs Data Warehouse

Aspect	Data Lake	Data Warehouse
Data Support	Stores raw data and processes it later on	Stores structured data for better analysis
Storage Cost	Less cost – uses scalable storage systems	Higher cost for processing data in a structured format
Performance	Slower querying as data needs to be processed before reading	Faster querying as data is already in a structured and optimized format
Flexibility	Highly flexible; can store diverse data due to schema-on-read approach	Less flexible; schema-on-write requires defining the structure in advance
Data Processing	Supports both real-time streaming and batch processing	Batch-oriented, but use of modern tools can help in real-time data loading
Users & Access	Data scientists and engineers	Business analysts and managers

When to Use Data Lake and Data Warehouse ?

You need to go through your data types and use cases before you make a choice between a data lake and a data warehouse. 

Use a Data Lake when:

• You are working on big data that has raw, semi-structured, and unstructured data. It could be social media feeds, IoT data, click-streams, or logs.
• You are an analytics and machine learning driven company, and you need raw data to train models and calculate patterns.
• You generally need flexibility to speed up storing data without worrying about its structure.

Use a Data Warehouse when:

• You require better reporting and business intelligence through well-organized data.
• You need more insightful dashboards, KPIs, and operational reports within teams like sales, marketing, and finance.
• Query performance matters to you, and you need faster data access as data warehouses can work with complex SQL queries.

Hybrid approach (Data Lakehouse):

Many companies go for a hybrid approach, ‘data lakehouse’. It combines both: 

• Scalability and flexibility of a data lake
• Structured data and performance of a data warehouse

This works best when you need to store raw data but also want structured data layers for analytics and business reporting. 

Conclusion 

Data lakes and data warehouses serve the same role, yet in different ways. 

• Data lakes are excellent with respect to storing large volumes of raw data, excellent in advanced analytics and machine learning.
• Data warehouses are perfect in case of quick and reliable access to structured data, which can be used in reports and dashboards.

Selecting one of them will rely on your objectives and data requirements. Most companies integrate the two and/or apply to have the best of both. 

In the end, knowing Data lake vs data warehouse differences helps you build a data setup that fits your business and stays ready for the future.