Data Lake vs Data Warehouse: Know the Key Differences

Quick Summary– Data lakes and data warehouses are both extensively used for big data storage, and each is different from different perspectives, such as structure and processing. This guide offers definitions and practical advice to help you understand the differences as you evaluate Data Lake vs Data Warehouse before you make the big move to data storage.

The value of data is hard to explain in a few words for any business. It drives different business functions — from creating targeted programs for customers and prospects, optimizing manufacturing and operations processes, and developing innovative products and services.

Hence, investing in effective data storage is paramount, enabling organizations to transform their operations, and resulting in enhanced efficiency and long-term growth.

Leveraging modern data storage platforms, businesses can deliver real-time, analytics-ready, and actionable data to any functional environment. 

Data Storage- What Is All the Hype About?

Most of us think that it is very easy to store data and doesn’t require any additional effort. But not many of us realize it is indeed a challenge.

IDC predicts that the collective sum of the world’s data will grow from 33 zettabytes this year to 175ZB by 2025, for a compounded annual growth rate of 61%.

Let us first understand this scenario: every bit of our action, movement, and interaction is diligently monitored to generate vital information, so we can be better served than yesterday.

On the other hand, this derived information is growing bigger and bigger and to manage and store this information; we require a reliable solution.

Enterprises are investing heavily in systems that can carry out this task efficiently and help them scale in a data-driven competitive world.

And this is where you need to understand the difference between Data Lake and Data Warehouse.

Data Lakes & Data Warehouse- Your Go-to Data Storage Solution

Yeah, as we all know, data lakes and data warehouses are the most incredible solutions embraced by modern enterprises.

These two platforms are capable of housing colossal amounts of data effortlessly.

But there is a twist!

If you think that these two platforms are similar, then you are hugely mistaken.

They might sound similar, but they have enough on their tables to draw significant differences in their structures, processing methods, and solutions.

Let’s unwind the curtain and find out Data Lake vs Data Warehouse…

Data Lake- a Detailed Overview

A data lake is a repository storing all the data of an organization in both structured and unstructured forms. It allows a gigantic storage pool for data in its natural and raw state.

With data lake architecture capabilities, organizations can effortlessly manage the massive volumes of data they produce without the necessity of structuring it beforehand. 

Businesses can construct efficient data pipelines to extract and process imperative information from these data lakes. This can be used to drive informed decision-making across the enterprise. 

This concept can easily be defined as one of the most highly scalable data storage platforms that can accommodate a vast amount of raw data.

Isn’t it enticing?

Yes, and there is much more!

With this versatile repository, it becomes easier to preserve, access, and analyze diverse data types. This platform can store data from various sources and rapidly adapt to evolving data processing and analytics needs. 

Types of Data Lake 

• Structured – it contains structured data from relational databases, i.e., rows and columns.
• Unstructured – it contains unstructured data from emails, documents, and PDFs.
• Semi-structured – it consists of semi-structured data like CSV, logs, XML, and JSON.
• Binary – It consists of images, audio, and video.

Data Lake Benefits

• Enables easy configuration for queries, data models, or applications without the need for pre-planning.
• Supports real-time analytics, big data analytics, and machine learning.
• Allows data import in its original format from multiple sources in real-time. 
• Experience scalability at par excellence, as it can handle massive volumes of structured and unstructured data.
• Improves speed because raw data doesn’t require transforming the data and developing schemas. This platform does not call for large data volumes to be structured prior to storing, enabling skilled data scientists or end-to-end self-service-bi tools to gain access to a broader range of data far faster.
• Glean better insights from unexpected and previously unavailable insights by analyzing a broader range of data in new ways.
• A cost-effective option because data lakes have lower operational costs. They are less time-consuming to manage, and most of the tools are open-source.

Must read- Big Data in Manufacturing: Challenges, Solutions and Use Cases

Data Lake Architecture

There are a number of different tools that can be used to build and manage a data lake, such as Azure, Amazon S3, and Hadoop. 

Furthermore, data teams can build ETL data pipelines and scfhema-on-read transformations and store data in a data lake. This available data can be used for data science, ML, business analytics, and intelligence tools. 

Data Lakes Tools

• Azure Data Lake Storage – it helps in creating a single, unified data storage space, and facilitates advanced security & data authentication features.
• AWS Lake Formation – it offers a simple solution to the integration of a data lake with AWS-based analytics and ML services. 
• Qubole – with this data can be stored in an open format and accessed through open standards in real-time.
• Infor Data Lake – it collects data from different sources and ingests it into a structure that helps in deriving instant value.
• Intelligent Data Lake – It ensures customers gain maximum value from Hadoop-based Data Lake without using much coding for running large-scale data queries. 

Data Warehouse- a Detailed Overview

With a similar approach to a data lake, a data warehouse is also a repository for business data. But it only calls for highly structured and unified data to support business intelligence and analytics needs. 

You can consider Data Warehouse architecture more like an actual warehouse, where articles are processed, then organized into sections and placed on shelves.

The data derived from this is ready for use to support historical analysis and reporting to inform decision-making across different functions of an organization.

This platform enables different business applications to generate or collect data at a central repository. Further, this information is stored and utilized for analytical purposes to make data-driven decisions. 

It goes without saying that, such refined, consolidated data collected from multiple sources, it simplifies business intelligence processes.

Types of Data Warehouse

• Enterprise Data Warehouse (EDW) – It caters as the main database helping in decision-support services within the enterprise. It is the best bet for cross-organizational information, an integrated approach to data representation, and can run complex queries. 
• Operational Data Store (ODS)– It runs in real-time for routine tasks, including storage of employee records. This data can be scrubbed, check for duplication, and resolved further. 
• Data Mart– It works as a subset of the data warehouse storing data for a particular department, region, or unit of a business. It is the best fit to increase user responses and reduce the volume of data for analysis. 

Data Warehouse Benefits

• Allows faster decision-making across the organization
• Access to better data quality, because the data has been cleansed, de-duplicated, and standardized. 
• With a consistent, “single source of truth,” enterprises can foster trust in the insights and decisions derived from the analysis.
• Unifies and harmonizes data from a wide range of sources offering a more complete picture of the business
• Allows utilizing BI activities such as data mining, augmented analytics, and machine learning to find patterns
• Bide adieu to data silos and incongruent data
• The availability of accurate and complete data helps turn information into insight faster.

Also Read- Amazing Use Cases of Big Data Analytics

Data Warehouse Architecture (ELT Process)

There are a variety of data sources enabling the warehouse to use the Extract, Load, and Transform (ELT) process. It  has a three-tier architecture, as outlined below:

• Top tier– In this tier there is a front-end user interface to perform ad hoc analysis and view reports.
• Middle tier– This tier reflects the analytics engine tier, typically an OLAP server to access and analyze data.
• Bottom tier– This tier consists of a database server, which serves as a  relational database system, where data is loaded and stored. 

Data Warehouse Tools

• Amazon Redshift – it helps in executing multiple concurrent queries without any operational overhead.  
• Microsoft Azure – it allows massively parallel processing to help extract and visualize business insights swiftly.
• Google BigQuery – it helps in building robust AI models using Cloud ML and TensorFlow. 
• Snowflake – it enables the analysis of data from various structured and unstructured sources and scales CPU resources based on the user’s activities.  
• Micro Focus Vertica – it offers built-in analytics capability for machine learning, pattern matching, and time series.   
• Amazon DynamoDB – it scales query capacity up to 10 or 20 trillion requests over petabytes of data. 

Differences- Data Lake vs Data Warehouse

Every organization has a different need to serve, and both of these platforms—a data lake and a data warehouse can cover the spectrum of their data storage needs. 

Let’s take a side-by-side look at data lake vs data warehouse, and understand how they can be combined to provide a holistic data storage solution for your business.

Data Lake vs Data Warehouse: Key differences

• Data Structure

A data warehouse serves as a repository for organized, filtered, and processed data. Data lakes, on the other hand, store raw data that has not been processed for a specific purpose yet. 

These vast repositories can hold structured, semi-structured, and unstructured data, making them a versatile option for storing information.

Henceforth, it can be stated that in the race of Data Lake vs Data Warehouse,data lakes require a much larger storage capacity than data warehouses since data is more flexible and is perfect for quick analysis.

• Processing

With a data warehouse, organizations can implement a schema-on-write approach, enabling the efficient storage and retrieval of vast amounts of data. 

This method ensures that the data is structured and fully optimized before it is written to the data storage, thus speeding up the process and reducing the complexity of handling raw data. 

It allows businesses to collect comprehensive insights and make informed decisions quickly, making it an essential tool for modern enterprises.

On the other hand, a data lake is a versatile storage solution using schema-on-read, which allows for flexible and on-demand processing of the data.

This approach stands in contrast to schema-on-write, which requires a predefined structure before any data is written. 

• Cost

Many organizations face challenges in managing the expenses associated with maintaining such vast amounts of information. 

This is where storing data in a data warehouse can be costly, particularly if there is a large volume of data. Data lakes are different and are designed for low-cost data storage. 

Its scalable nature enables organizations to store vast amounts of raw data from diverse sources, providing flexibility and adaptability. 

• Purpose

Unlike traditional databases, data warehouses only hold processed data that has been used for a specific purpose. 

This allows organizations to gain valuable insights and make better-informed decisions more efficiently.

On the flip side, as more companies shift towards a data-driven approach, this is where data lakes empower them to harness insights from structured and unstructured data. 

With a structured implementation, data lakes can significantly improve an organization’s ability to analyze, interpret, and act upon the gathered information.

• Users

IT and business professionals with an in-depth understanding of the subject matter encapsulated in the processed data primarily utilize the sophisticated system of a data warehouse.

Whereas, data lakes consist of unstructured data, which necessitates the expertise of data scientists or engineers to organize and categorize the data.

• Accessibility

You should know that data warehouses are generally designed to be highly structured, which makes it challenging to access and manipulate stored information. 

Thus, it requires advanced technical knowledge and skill sets to make the most out of the data.

In contrast, data lakes present a more flexible and adaptable solution due to their minimal limitations. 

With easy access to vast repositories of diverse data, users can quickly make modifications and analyze the stored information as required. 

• Technologies

The technological ecosystem imbibed within the data warehouse is closely linked with relational databases. 

And this is made possible due to its exceptional performance in executing high-speed queries on well-organized data. 

These technologies have been developed to support large volumes of data storage and facilitate the swift retrieval of crucial information.

On the other hand, the technological structure of data lakes is based on Big data technologies, such as the Hadoop Distributed File System (HDFS). 

This significantly increases the potential of data lakes for analytics purposes. It further leverages the scalability and flexibility of HDFS to store and process massive amounts of data from various sources. 

Which Platform Is the Right Fit for Your Organization?

The best answer to this question is based largely on how an organization decides to use its data.

As we have aforementioned, data warehouses are all about containing historical data that has already been processed and is ready to be used for analytics. It is a feasible option for a team with the least amount of exposure, as its design is simple to work with.

It goes without saying, but a well-structured warehouse architecture makes it a perfect bet to be used sacrosanctly in enterprise systems.

On the other hand, with a data lake approach, organizations that ingest vast amounts of data from high-volume sources can utilize it.

Indeed, the data ingestion is relatively uncomplicated as it stores raw data, which is difficult to navigate and work with.

Henceforth, this approach is more convenient for data scientists to use within advanced analytics applications, or for enterprises with diverse analytics needs.

Hidden Brains- Your Trusted Technology Partner

It is obvious to feel perplexed with such a huge amount of information to be processed and the right decision to be made.

Remember, a well-executed data-driven strategy can lead to improved efficiency, better customer experiences, and, ultimately, a thriving and successful business.

Here, we ease the woes of selection. You can approach our team of experts for any query to get better insights about the right solution.

We pride ourselves on delivering exceptional advice and assistance to help you achieve your desired outcomes with efficient Big Data Analytics Services.

Final Call

Future-ready businesses require data to transform their functions and make informed decisions.

Data lake vs Data warehouse pose an extreme potential, with each offering unique advantages and potential drawbacks depending on the specific requirements of the organization. 

Henceforth, it is crucial for you to diligently evaluate the right choice and select the most appropriate solution that aligns with your goals and objectives.

Feel free to get in touch with the Hidden Brains team to navigate a comprehensive understanding of the options available to you, and scale higher. 

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Albert Smith

Albert Smith, Marketing Manager at Hidden Brains, drives brand growth with innovative strategies in web, mobile app, and software development.