By Pohan Lin, Senior Web Marketing and Localizations Manager, Databricks

Although data lakes and data warehouses are used to store large amounts of data, the terms are not interchangeable.

A data lake is a large pool of unstructured data with no apparent purpose. A data warehouse is a location where structured, filtered data that has previously been collected for a specific purpose can be stored.

The data lakehouse, which combines the fluidity of a data lake with the data management capabilities of a data warehouse, is an emerging architecture trend in data management.

In reality, their only similarity is that they both store data at a high level.

What Is a Data Warehouse?

A data warehouse, often known as an enterprise data warehouse, is a reporting and data analysis system thought to be a key component of business intelligence in computing. They are central data repositories that combine data from one or more sources.

Data warehouses utilize a schema-on-write data architecture, which means that before entering the warehouse, source data must match a specified structure (schema). An ETL (Extract-Transform-Load) procedure usually accomplishes this.

Some data warehouse examples are listed below:

• Amazon Redshift.
• IBM Db2 Warehouse.
• Google BigQuery.
• Microsoft Azure Synapse.
  

 

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When Should You Use a Data Warehouse?

Data warehouses are a suitable alternative for storing substantial amounts of historical data or undertaking in-depth data analysis to develop business intelligence. Data warehouse data analysis is generally simple and can be handled by data scientists and business analysts due to its highly structured format.

Data warehouses are not designed to meet an application's transaction and concurrency requirements. If a data warehouse is beneficial to your company, you will need an external database or databases to run daily operations.

What Is a Data Lake?

A data lake is a collection of data from several sources kept in its original, unprocessed state. In data lakes, data is often stored using the Hadoop Distributed File System (HDFS) that functions with MapReduce. The system enables concurrent processing of large data sets.

Data lakes, like data warehouses, store massive volumes of current and historical data. Data storage capacity in various forms, including BSON, TSV, JSON, CSV, Parquet, Avro, and ORC, distinguishes data lakes.

The primary function of a data lake is to analyze data to generate insights. However, some companies employ data lakes just for cheap storage, hoping to use the data for analytics later on.

The following are some examples of tech that can create data lakes and provide scalable and flexible storage:

• Azure Data Lake Storage Gen2
• AWS S3
• Google Cloud Storage
  

If you're still wondering, "What is MapReduce?"-it is a programming pattern or model applied in the Hadoop framework to access large data sets stored within the HDFS.

When Should You Use a Data Lake?

Data lakes offer a low-cost means of storing large amounts of data. Use a data lake to obtain insights from your current and historical data without having to alter or move it. Machine learning and predictive analytics are also supported by data lakes.

 

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What Are the Key Differences Between Data Lakes and Data Warehouses?

Although they are similar and can be combined effectively, there are several differences between both options. A data lake may be appropriate for one firm, whereas a data warehouse may be more appropriate for another.

Here are seven key differences between data lakes and warehouses:

1.    Purpose

Individual data elements in a data lake have no set purpose. Raw data is sent into a data lake, sometimes for a specific future application and maybe without a defined purpose. As a result, data lakes have less data structure and filtration than warehouses.

2.    Users

Inexperienced unprocessed data users may find it challenging to navigate data lakes. Data scientists and specialized tools are usually required to comprehend and translate raw, unstructured data for specific business use cases such as communications using cloud PBX solutions.

Processed data could be in a spreadsheet, chart, table, software proposal template,​​ and other formats such that the majority of your company's personnel can understand. Processed data, such as that found in data warehouses, merely demands that the user comprehends the subject matter.

Alternatively, data preparation technologies that provide self-service access to data stored in a data lake are gaining traction.

3.    Accessibility and ease of use

The terms accessibility and ease of use apply to the entire data repository, not just data within it. Data lake architecture is unstructured, making it simple to access and modify its contents. Furthermore, because data lakes have few limits, any updates to the data can be made quickly.

Data warehouses are more structured. The processing and organization of data make it easier to comprehend, but the structure restrictions make data warehouses complex and expensive to operate. 

 

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4.    Data structure

Raw data is data in its natural form before it is processed. Raw and processed data structures may be the most significant distinction between data lakes and warehouses. Unprocessed data is stored in data lakes, whereas processed and refined data is stored in data warehouses.

As a result, data lakes often demand far more storage than data warehouses. Raw, unprocessed data is also flexible, easy to analyze for any purpose, and great for machine learning.

However, with so much raw data, data lakes can easily create data swamps if adequate data quality and governance mechanisms are not in effect. Furthermore, processed data is easier for a broader audience to interpret.

5.    Data types

Data warehouses typically contain quantitative metrics, the attributes describing them, and data derived from transactional systems. Web server logs, social network activity, sensor data, images, and text are all examples of non-traditional data sources which are ignored. New applications for these data sets continue to emerge, but processing and storing them can be costly and tedious.

The data lake approach embraces these non-traditional data formats. Raw data can be stored in the data lake and only processed when it's time to use it. The term for this process is "Schema on Read" as opposed to data warehouse's "Schema on Write."

6.    Adaptability

The time it takes to modify data warehouses is one of the most common complaints. It takes a significant amount of time to get the warehouse's structure right during development. Where development processes such as a release cycle move faster, this could be a drawback.

A strong warehouse design would adapt to change, but given the complex nature of the data loading process and the effort required to simplify analysis and reporting, these changes will consume resources and time.

Users in the data lake, on the other hand, are free to go beyond the structure of a warehouse to explore data in creative ways and answer their queries at their own pace because data is stored in its raw state and remains accessible.

 

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7.    Generating results

This final difference is a product of the others. Since data lakes contain all forms of data and allow the user to access data before it has been processed or structured, they can get results faster than with a traditional data warehouse.

This easy access to the data, however, comes at some cost. Most or all the data sources necessary for analysis may not be covered by the work done by the data warehouse development team.

Users are in charge of exploring and using the data as they see fit, but many business users may not want to do the work.

Making the Right Data Storage Choice for Your Organization

Let's review the differences between data lakes and data warehouses.

Data warehouses hold structured data, employ a schema-on-write process architecture, have closely integrated storage and compute requirements, and are best suited for data management with established analytics use cases.

Data lakes hold various types of data (unstructured, structured, or semi-structured), employ a schema-on-read process architecture, have loosely integrated storage and compute requirements, and are well-suited to handling data with a variety of use cases.

However, they frequently require data engineers or data scientists' ability to find out how to navigate multi-structured sets of data, as well as requiring integration with analytic APIs or other systems to support BI (Business Intelligence).

The first thing to keep in mind while deciding between a data lake and a data warehouse is that these technologies are not completely incompatible. Each of them by itself does not make up a data & analytics strategy, but both can be part of one.

 

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The data warehouse approach focuses on functionality and performance: ingesting data and transforming it into valuable chunks and then pushing this processed data to downstream analytics and BI applications.

These services are all necessary, but the data warehouse architecture of schema-on-write, closely integrated storage/compute, and dependence on specified use cases make it a poor fit for multi-model capabilities or large amounts of multi-structured data.

Data lakes offer a less restricted mindset that is better suited to addressing the demands of big data: schema-on-read, loosely integrated storage/computing, and dynamic use cases that work together to boost innovation by decreasing data management time, cost, and complexity.

However, a data lake without data warehouse features could become a cluttered sludge of data that's tough to sort through.

To avoid producing data swamps, technologists must integrate data lakes' storage capabilities and design concepts with data warehouse operations such as indexing, query, and analytics.

You will be able to make the most of your data while reducing the cost, time, and complexity of analytics and BI when this happens.

Find the Perfect Balance with Both Options

Organizations frequently use both data lakes and data warehouses. Lakes are employed to manage large amounts of data and users benefit from the unprocessed data, whereas data warehouses are for everyday operational business decisions and operations.

Machine learning and advanced analytics solutions frequently include data lakes. Many firms are already utilizing both data storage solutions, especially where a data warehouse is on top of a data lake.

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ABOUT THE AUTHOR

Pohan Lin - Senior Web Marketing and Localizations Manager

 

A Senior Web Marketing and Localizations Manager at Databricks, Pohan Lin specialises in demonstrating the impact of massive scale data engineering, data analysis, and collaborative data science. With over 18 years of experience in web marketing, online SaaS business, tensorflow company, and ecommerce growth, Pohan Lin is dedicated to innovating the way we use data in marketing.

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