Data Lake

Last updated: February 8, 2026Reviewed date: February 8, 2026Reading time: 5 min read
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Article Summary

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions. Why do you need a data lake? Organizations that...

Key Takeaways

  • This article explains Why do you need a data lake? in simple medical language.
  • This article explains What are the essential elements of a data lake and analytics solution? in simple medical language.
  • This article explains How does a data warehouse compare to a data lake? in simple medical language.
  • This article explains What is the value of data lakes? in simple medical language.
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  • Severe symptoms, breathing difficulty, fainting, confusion, or rapidly worsening illness.
  • New weakness, severe pain, high fever, or symptoms after a serious injury.
  • Any symptom that feels urgent, unusual, or unsafe for the patient.
1

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2

See a doctor

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A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.

Why do you need a data lake?

Organizations that successfully generate business value from their data, will outperform their peers. An Aberdeen survey saw organizations who implemented a data lake outperforming similar companies by 9% in organic revenue growth. These leaders were able to do new types of analytics like machine learning over new sources like log files, data from click-streams, social media, and internet connected devices stored in the data lake. This helped them to identify, and act upon opportunities for business growth faster by attracting and retaining customers, boosting productivity, proactively maintaining devices, and making informed decisions.

What are the essential elements of a data lake and analytics solution?

As organizations are building data lakes and an analytics platform, they need to consider a number of key capabilities including:

Data movement

Data lakes allow you to import any amount of data that can come in real-time. Data is collected from multiple sources, and moved into the data lake in its original format. This process allows you to scale to data of any size, while saving time of defining data structures, schema, and transformations.

Securely store and catalog data

Data lakes allow you to store relational data like operational databases and data from line of business applications, and non-relational data like mobile apps, IoT devices, and social media. They also give you the ability to understand what data is in the lake through crawling, cataloging, and indexing of data. Finally, data must be secured to ensure your data assets are protected.

Analytics

Data lakes allow various roles in your organization like data scientists, data developers, and business analysts to access data with their choice of analytic tools and frameworks. This includes open source frameworks such as Apache Hadoop, Presto, and Apache Spark, and commercial offerings from data warehouse and business intelligence vendors. Data lakes allow you to run analytics without the need to move your data to a separate analytics system.

Machine Learning

Data lakes will allow organizations to generate different types of insights including reporting on historical data, and doing machine learning where models are built to forecast likely outcomes, and suggest a range of prescribed actions to achieve the optimal result.

How does a data warehouse compare to a data lake?

Depending on the requirements, a typical organization will require both a data warehouse and a data lake as they serve different needs, and use cases.

A data warehouse is a database optimized to analyze relational data coming from transactional systems and line of business applications. The data structure, and schema are defined in advance to optimize for fast SQL queries, where the results are typically used for operational reporting and analysis. Data is cleaned, enriched, and transformed so it can act as the “single source of truth” that users can trust.

A data lake is different, because it stores relational data from line of business applications, and non-relational data from mobile apps, IoT devices, and social media. The structure of the data or schema is not defined when data is captured. This means you can store all of your data without careful design or the need to know what questions you might need answers for in the future. Different types of analytics on your data like SQL queries, big data analytics, full text search, real-time analytics, and machine learning can be used to uncover insights.

As organizations with data warehouses see the benefits of data lakes, they are evolving their warehouse to include data lakes, and enable diverse query capabilities, data science use-cases, and advanced capabilities for discovering new information models. Gartner names this evolution the “Data Management Solution for Analytics” or “DMSA.”

For an in-depth comparison between data lakes and data warehouses, visit our dedicated comparison page for data lakes vs data warehouses.

What is the value of data lakes?

The ability to harness more data, from more sources, in less time, and empowering users to collaborate and analyze data in different ways leads to better, faster decision making. Examples where Data lakes have added value include:

Improved customer interactions

A Data lake can combine customer data from a CRM platform with social media analytics, a marketing platform that includes buying history, and incident tickets to empower the business to understand the most profitable customer cohort, the cause of customer churn, and the promotions or rewards that will increase loyalty.

Improve R&D innovation choices

A data lake can help your R&D teams test their hypothesis, refine assumptions, and assess results—such as choosing the right materials in your product design resulting in faster performance, doing genomic research leading to more effective medication, or understanding the willingness of customers to pay for different attributes.

Increase operational efficiencies

The Internet of Things (IoT) introduces more ways to collect data on processes like manufacturing, with real-time data coming from internet connected devices. A data lake makes it easy to store, and run analytics on machine-generated IoT data to discover ways to reduce operational costs, and increase quality.

What are the challenges of data lakes?

The main challenge with a data lake architecture is that raw data is stored with no oversight of the contents. For a data lake to make data usable, it needs to have defined mechanisms to catalog, and secure data. Without these elements, data cannot be found, or trusted resulting in a “data swamp.” Meeting the needs of wider audiences require data lakes to have governance, semantic consistency, and access controls.

How do you deploy data lakes in the cloud?

Data lakes are an ideal workload to be deployed in the cloud, because the cloud provides performance, scalability, reliability, availability, a diverse set of analytic engines, and massive economies of scale. ESG research found 39% of respondents considering cloud as their primary deployment for analytics, 41% for data warehouses, and 43% for Spark. The top reasons customers perceived the cloud as an advantage for Data lakes are better security, faster time to deployment, better availability, more frequent feature/functionality updates, more elasticity, more geographic coverage, and costs linked to actual utilization.

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A rural-friendly guide: warning signs, when to see a doctor, related articles, tests to discuss, and OTC safety education.
1 Symptom 2 Severity 3 Safe guidance
First safety question

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Start here: Write or select a symptom. The guide will show warning signs, doctor guidance, diagnostic tests to discuss, OTC safety education, and related RX articles.

Important: This tool is educational only. It cannot diagnose, treat, or replace a doctor. OTC information is not a prescription. In an emergency, contact local emergency services or go to the nearest hospital.

Doctor visit helper

Prepare before seeing a doctor

A simple rural-patient checklist to help you explain symptoms clearly, ask better questions, and avoid unsafe self-treatment.

Safety note: This is not a prescription or diagnosis. For severe symptoms, pregnancy danger signs, children with serious illness, chest pain, breathing difficulty, stroke-like weakness, or major injury, seek urgent care.

Which doctor may help?

Start with a registered doctor or the nearest qualified health center.

What to tell the doctor

  • Write when the problem started and how it changed.
  • Bring old prescriptions, investigation reports, and current medicines.
  • Write allergies, pregnancy status, diabetes, kidney/liver disease, and major past illnesses.
  • Bring one family member if the patient is weak, elderly, confused, or a child.

Questions to ask

  • What is the most likely cause of my symptoms?
  • Which danger signs mean I should go to hospital quickly?
  • Which tests are necessary now, and which can wait?
  • How should I take medicines safely and what side effects should I watch for?
  • When should I come for follow-up?

Tests to discuss

  • Vital signs: temperature, pulse, blood pressure, oxygen saturation
  • Basic physical examination by a clinician
  • CBC, urine test, blood sugar, or imaging only when clinically needed

Avoid these mistakes

  • Do not use antibiotics, steroid tablets/injections, or strong painkillers without proper medical advice.
  • Do not hide pregnancy, kidney disease, ulcer, allergy, or blood thinner use.
  • Do not delay emergency care when danger signs are present.

Medicine safety and first-aid guide

This section is for patient education only. It does not replace a doctor, pharmacist, or emergency care.

Safe first steps

  • Avoid heavy lifting, sudden bending, and prolonged bed rest.
  • Use comfortable posture and gentle movement as tolerated.
  • Discuss physiotherapy, X-ray, or MRI only when clinically needed.

OTC medicine safety

  • For mild back pain, pain-relief medicine may be discussed with a doctor or pharmacist.
  • Avoid repeated painkiller use if you have kidney disease, stomach ulcer, uncontrolled blood pressure, or are taking blood thinners.

Avoid these mistakes

  • Do not start antibiotics without a proper medical decision.
  • Do not use steroid tablets or injections casually for quick relief.
  • Do not delay emergency care because of home remedies.

Get urgent help if

  • Back pain with leg weakness, numbness around private area, loss of urine/stool control, fever, cancer history, or major injury needs urgent care.
Medicine names, dose, and timing must be decided by a qualified clinician or pharmacist after checking age, pregnancy, allergy, other diseases, and current medicines.

For rural patients and family caregivers

Patient health record and symptom diary

Write your symptoms, medicines already taken, test results, and questions before visiting a doctor. This note stays on your device unless you print or copy it.

Doctor to discuss: Doctor / qualified healthcare provider
Tests to discuss with doctor
  • Basic vital signs: temperature, pulse, blood pressure, oxygen level if needed
  • Relevant blood, urine, imaging, or specialist tests only after clinical assessment
Questions to ask
  • What is the most likely cause of my symptoms?
  • Which warning signs mean I should go to emergency care?
  • Which tests are really needed now?
  • Which medicines are safe for my age, pregnancy status, allergy, kidney/liver/stomach condition, and current medicines?

Emergency warning signs such as chest pain, severe breathing difficulty, sudden weakness, confusion, severe dehydration, major injury, or loss of bladder/bowel control need urgent medical care. Do not wait for online information.

Safe pathway to proper treatment

Back pain care roadmap

Use this simple roadmap to understand the next safe steps. It is educational and does not replace examination by a doctor.

Go to emergency care if you notice:
  • New leg weakness, numbness around private area, or loss of bladder/bowel control
  • Back pain after major injury, fever, unexplained weight loss, cancer history, or severe night pain
Doctor / service to discuss: Orthopedic/spine specialist, physical medicine doctor, physiotherapist under guidance, or qualified clinician.
  1. Step 1

    Check danger signs first

    If danger signs are present, seek emergency care and do not wait for online information.

  2. Step 2

    Record the symptom story

    Write when symptoms started, severity, medicines already taken, allergies, pregnancy status, and test results.

  3. Step 3

    Visit a qualified clinician

    A doctor, nurse, or qualified healthcare provider can examine you and decide which tests or treatment are needed.

  4. Step 4

    Do only useful tests

    Discuss neurological examination first. X-ray or MRI may be needed only when red flags, injury, nerve weakness, or persistent severe symptoms are present.

  5. Step 5

    Follow up and return early if worse

    If symptoms worsen, new warning signs appear, or treatment is not helping, return for review quickly.

Rural patient practical tips
  • Take a written symptom diary and all previous prescriptions/test reports.
  • Do not hide medicines already taken, even herbal or over-the-counter medicines.
  • Ask which warning signs mean urgent referral to hospital.
  • Avoid forceful massage or bone-setting when there is weakness, injury, fever, or nerve symptoms.

This roadmap is for education. A real diagnosis and treatment plan requires history, examination, and clinical judgment.

RX Patient Help

Ask a health question safely

Write your symptom story. A health professional or site editor can review it before any answer is prepared. This box is not for emergency care.

Emergency first: Severe chest pain, breathing trouble, unconsciousness, stroke signs, severe injury, heavy bleeding, or rapidly worsening symptoms need urgent local medical care now.

Frequently Asked Questions

Why do you need a data lake?

Organizations that successfully generate business value from their data, will outperform their peers. An Aberdeen survey saw organizations who implemented a data lake outperforming similar companies by 9% in organic revenue growth. These leaders were able to do new types of analytics like machine learning over new sources like log files, data from click-streams, social media, and internet connected devices stored in the data lake. This helped them to identify, and act upon opportunities for business growth faster by attracting and retaining…

What are the essential elements of a data lake and analytics solution?

As organizations are building data lakes and an analytics platform, they need to consider a number of key capabilities including:

Data movement Data lakes allow you to import any amount of data that can come in real-time. Data is collected from multiple sources, and moved into the data lake in its original format. This process allows you to scale to data of any size, while saving time of defining data structures, schema, and transformations. Securely store and catalog data Data lakes allow you to store relational data like operational databases and data from line of business applications, and non-relational data like mobile apps, IoT devices, and social media. They also give you the ability to understand what data is in the lake through crawling, cataloging, and indexing of data. Finally, data must be secured to ensure your data assets are protected. Analytics Data lakes allow various roles in your organization like data scientists, data developers, and business analysts to access data with their choice of analytic tools and frameworks. This includes open source frameworks such as Apache Hadoop, Presto, and Apache Spark, and commercial offerings from data warehouse and business intelligence vendors. Data lakes allow you to run analytics without the need to move your data to a separate analytics system. Machine Learning Data lakes will allow organizations to generate different types of insights including reporting on historical data, and doing machine learning where models are built to forecast likely outcomes, and suggest a range of prescribed actions to achieve the optimal result.How does a data warehouse compare to a data lake?

Depending on the requirements, a typical organization will require both a data warehouse and a data lake as they serve different needs, and use cases. A data warehouse is a database optimized to analyze relational data coming from transactional systems and line of business applications. The data structure, and schema are defined in advance to optimize for fast SQL queries, where the results are typically used for operational reporting and analysis. Data is cleaned, enriched, and transformed so it can…

What is the value of data lakes?

The ability to harness more data, from more sources, in less time, and empowering users to collaborate and analyze data in different ways leads to better, faster decision making. Examples where Data lakes have added value include:

Improved customer interactions A Data lake can combine customer data from a CRM platform with social media analytics, a marketing platform that includes buying history, and incident tickets to empower the business to understand the most profitable customer cohort, the cause of customer churn, and the promotions or rewards that will increase loyalty. Improve R&D innovation choices A data lake can help your R&D teams test their hypothesis, refine assumptions, and assess results—such as choosing the right materials in your product design resulting in faster performance, doing genomic research leading to more effective medication, or understanding the willingness of customers to pay for different attributes. Increase operational efficiencies The Internet of Things (IoT) introduces more ways to collect data on processes like manufacturing, with real-time data coming from internet connected devices. A data lake makes it easy to store, and run analytics on machine-generated IoT data to discover ways to reduce operational costs, and increase quality.What are the challenges of data lakes?

The main challenge with a data lake architecture is that raw data is stored with no oversight of the contents. For a data lake to make data usable, it needs to have defined mechanisms to catalog, and secure data. Without these elements, data cannot be found, or trusted resulting in a “data swamp." Meeting the needs of wider audiences require data lakes to have governance, semantic consistency, and access controls.

How do you deploy data lakes in the cloud?

Data lakes are an ideal workload to be deployed in the cloud, because the cloud provides performance, scalability, reliability, availability, a diverse set of analytic engines, and massive economies of scale. ESG research found 39% of respondents considering cloud as their primary deployment for analytics, 41% for data warehouses, and 43% for Spark. The top reasons customers perceived the cloud as an advantage for Data lakes are better security, faster time to deployment, better availability, more frequent feature/functionality updates, more elasticity, more…

References

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