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Data Lake Vs Data Warehouse – Essential differences | Saras Analytics
Data Engineering

Data Lake vs Data Warehouse: Key Differences

11 minutes read


Table of Contents

Data Lakes and Data Warehouses are two data storage structures with distinctive characteristics and capabilities. The selection is dependent on the intended use of the acquired data and the organization’s objectives. The only similarity between the two is that they both store data, but the way in which they manage it is vastly different. Let us learn the key differences between Data Lake and Data Warehouse to see which may be the optimal choice for your company.

Data Lake vs. Data Warehouse: Why do they matter

Today’s most precious asset is data. Companies with superior data management can advance and dominate their sectors more quickly. Decisions, strategy, and business are all fueled by data. Therefore, data collection, management, and storage are key aspects for successful businesses.

Companies that incorporate data into their business strategy are aware that storage is not a solely technological issue. The data architecture must accommodate the huge data intake. Businesses require an efficient management system to respond swiftly to market demands, comply with data rules (such as GPRD), and assess and plan their future steps. In conclusion, to remain competitive in a fast-paced, information-rich environment.

Data Lakes and Data Warehouses are the two basic data architecture options

Data lake Data warehouse
Data structure Raw Processed
Purpose of data None Presently working
User Data Scientists Business experts
Accessibility Quick updating and universally accessible Costly and complicated during changes

What exactly is a Data Lake

A Data Lake is a large-scale repository for unstructured, semi-structured, and raw data. It is a site where you may save any type of data in its original format, with no limits on account size or file size. It offers a vast quantity of data to enhance local integration and analytical efficacy.

A data lake stores data using a basic architecture, whereas a hierarchical data warehouse stores data in files and folders. Each data object in a lake is assigned a unique identification and tagged with a series of enhanced metadata tags. Upon the emergence of a business inquiry, the data lake may be queried to locate pertinent data, which may then be analyzed to assist in answering the query.

The purpose of a data lake is to preserve everything in its original, untouched state. In contrast to a traditional data warehouse, which modifies and processes data as it is being fed, this one does not.

Characteristics of a Data Lake

The key characteristic of a Data Lake is its centralization. Data Lakes are a practical and cost-effective option for gathering and storing data of all types and sizes. Data Lakes hold unprocessed raw, unstructured, semi-structured, and structured data. Only during data retrieval is data structured, which provides Data Scientists additional opportunities.

Data Lakes are also very adaptable and simple to administer. There are no restrictions on creating new data types, which facilitates the use of various applications. And, since scaling is not an issue, it is one of the favored Big Data designs.

This strategy is advantageous for firms that collect data in real-time and value each piece of information equally. Data Lakes may be utilized by businesses to manage data and provide it at the disposal of marketing departments. There is an abundance of fragmented user data – time, region, preferences, and demographics – that may be leveraged to create hyper-personalized segmented ads.

What are the Components of Data Lake Architecture

A data lake architecture consists of five essential elements. These components are essential to comprehend how a data lake operates.

Data Ingestion

Data Ingestion is the movement of data from numerous sources to a storage medium where it may be accessed, utilized, and evaluated by an organization.

Data Storage

A magnetic, optical, or mechanical media that stores and maintains digital data for future use.

Data Security

Data security is the process of protecting digital data from unauthorized access, alteration, or theft throughout its lifetime.

Data Lineage or Analysis

Data lineage is the process of comprehending, recording, and presenting data as it flows from data sources to consumers. This includes how the data was transformed, what changed, and why it changed along the journey.

Data Governance

Data governance is the process of governing the availability, accessibility, quality, and security of data in corporate systems in accordance with internal data standards and rules that also regulate data consumption. Effective data governance ensures that data is consistent, trustworthy, and safe, as well as not mistreated.

What is a Data Warehouse

A data warehouse is a system that gathers and organizes massive quantities of data from several sources. Its analytic nature helps firms to get valuable business insights from their data, enabling them to make more informed decisions. It captures and preserves historical records that data scientists and business analysts may find incredibly relevant in the future.

A data warehouse is often described as a centralized store of data that can be studied to assist individuals in making better decisions. Regularly, data from transaction processing systems, relational databases, and other sources comes into a data warehouse. Decision-makers, project managers, data engineers, business analysts, data scientists, and data scientists access the data using business intelligence tools, SQL clients, and other analytics applications.

Features of a Data Warehouse

Since data has a specified purpose, Data Warehouse design demands careful planning: what type of data will be retrieved, and what technologies will be utilized for its collection, organization, processing, and retrieval? The objective is to have a corpus of consistent data in defined forms that is ready to be evaluated.  As it is a management system comprised of many technologies and not a repository, it requires a greater investment. The reward is higher-quality data that facilitates quicker decision-making.

Data Warehouses routinely collect pertinent data from specific applications, whether internal or external, which are supplied by analytics, customer, and partner systems. This data is then structured and stored according to warehouse allocations, matching the format of previously existing goods. The data is then analyzed to generate outputs matched to the business’s decision-making procedure. One of the strengths of Data Warehouses is format consistency, which ensures the integrity and quality of information that is ready to be examined and utilized without processing delays.

How does a Data Warehouse work

A data warehouse serves as the central repository for data acquired from various sources. Information may be structured, semi-structured, or unstructured. The data warehouse ingests, converts, and analyzes the data before making it accessible to users for decision-making.

By merging massive volumes of data in a data warehouse, a company may build a more thorough study, guaranteeing that it has evaluated all relevant aspects prior to reaching a decision.

In this context, the word data warehouse architecture refers to the overall architecture of data transport, processing, and presentation for end-user computing inside an enterprise. Each data warehouse is distinct, yet they all include the same essential components.

Three Principal Categories of Data Warehouses

Enterprise Data Storage (EDW)

This form of data warehouse serves as the enterprise’s primary database for decision-support services. EDW provides access to cross-organizational data, an integrated approach to data representation, and the ability to execute complicated queries.

Operational Data Store (ODS)

ODS is used to execute normal functions, including the storing of personnel records, and is updated in real-time. Here, data may be cleansed, reviewed for duplication, and resolved. In addition, it may be used to merge contradictory data from many sources so that business operations, analysis, and reporting can be conducted efficiently.

Data Mart

A data mart is a subset of the data warehouse, storing data for a certain department, area, or business unit. The usage of a data mart increases user replies and minimizes the amount of data required for analysis. Occasionally, data from here is saved in the ODS. The ODS subsequently transmits the data to the EDW for storage and utilization.

Data Lakes vs. Data Warehouses

Learn the distinctions between the two most popular solutions for storing copious amounts of data. The two most common alternatives for storing substantial amounts of data are data lakes and data warehouses. Data warehouses are used to analyze archived structured data, whereas data lakes are used to store unstructured large data.

 Criteria Data Lake Data Warehouse
Storage Primarily used to store unstructured data Raw data is stored in its native form and gets transformed when it is analyzed. It can also be used to store structured data A large volume of data. Primarily used to store structured data. The data is cleaned and transformed before loading into the data warehouse.
Size It can be up to petabytes Generally, a few Gigabytes or Terabytes
Data Ingest Supports batch, real-time, and streaming data ingest Supports batch, real-time, and streaming data ingest. But often, it is used for batch data ingestion.
Purpose Ideal for Machine Learning and Deep Learning use cases like Personalized recommendations, forecasting, autonomous driving, etc. Ideal for uses such as monthly reports, executive reporting, business analytics
Schema Schema on read is the preferred approach which leads to faster data ingestion and more flexibility down the line. Schema on write is the preferred approach which leads to more upfront effort, delivers more structure while being less flexible.
Utility Unstructured data, explorations, innovation, flexibility. Structured data, high performance, repeatability, constant use.
Users Data Scientists, Software Engineers Data Analysts, BI Developers, Business Users
Skills required Spark, Kafka, Python, Java, Hive, etc. SQL, Python, or R
Supported Use cases Predictive analytics, Machine learning, Deep learning, NLP, etc. Enterprise reporting – sales dashboards, marketing dashboards, web scorecards, etc.
Data Pipeline ELT methodology – Extract, Load, and Transform later ELT or ETL process depending on the use case
Key guiding principle Data accuracy and completeness Support for high data volume, variety, value, and integrity of data
Benefits Highly secure and performant Universally available and scalable

Data lake Vs Data warehouse – What is right for you

Most businesses that are serious about becoming data and insights-driven tend to have both. If you are a business that is in the preliminary stages of adopting data to drive business decisions, then you may want to start off with a data warehouse. Automate repeatable enterprise reporting by leveraging a cloud data warehouse and reduce the time spent on manual assimilation of reports. For requirements that involve unstructured data analysis or dealing in large volumes of data, separate data pipelines into a data lake should be set up to get the raw data delivered in a data lake. Unleash your data scientists to find insights and drive positive business outcomes.

How can Daton help

Data movement and data consolidation is the first and most critical stage in any data lake or data warehousing initiative. As the diversity of your applications increases, so does the complexity of your data pipeline. Consolidating data from sales, marketing, analytics, Clickstream, customer support, log files, etc., is daunting even for seasoned data engineers. It is not only daunting, but it is also costly to build and manage in-house. Why re-invent the wheel when Daton can consolidate all your enterprise data in just a few clicks?

Whether you are building a data lake or a data warehouse like BigQuery, Snowflake, Redshift Daton can help you quickly assemble your data in one place. Daton is an ecommerce-focused data pipeline designed to simplify your data pipeline and get you working on your project’s revenue-generating aspects while leaving the plumbing to Daton. Daton is a secure, fast, flexible, and cost-effective cloud data pipeline that can accelerate your journey to insights.

Data Lake vs. Data Warehouse: Takeaway

Data Lakes can serve as the first source of information for Data Warehouses. Consider data to be water: we can retrieve it from the Lake and store it in the Warehouse. Before it can be placed in the Warehouse, however, it must be bottled and tagged for space-efficient placement and quick retrieval.

Data Lakes and Data Warehouses are both fundamentally methods for storing and utilizing vast volumes of collected data for company development. The distinction resides in how data is handled and its intended use. Understanding how and why data is utilized can help your firm determine the optimal storage and management solution.

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