Introduction

In this constantly growing era, the volume of data is increasing rapidly, and tons of data points are produced every second. Now, businesses are looking for different types of data storage to store and manage their data effectively. Organizations can collect millions of data, but if they’re lacking in storing that data, those efforts don’t mean anything; that’s why Data storage is equally important for business. Data lake and warehouse are two types of data storage, majorly used for storing big data, but they are very different and can’t be used as interchangeable terms. The only real similarity between the two is their high-level purpose of data storage.

Data lakes are famous for storing big data of all structures, whereas data warehouses provide processed data ready to gain insights.

In this article, we’ll focus on the difference between Data Lake and Data Warehouse, which helps you to choose the best storage for your business.

Learning Objectives:

1. Understanding the difference between Data Lake and Data Warehouse
2. Use cases of Data Lake and Data Warehouse
3. Advantages and disadvantages of Data Lake and Data Warehouse

This article was published as a part of the Data Science Blogathon.

Table of Contents

1. What is Data Lake?
2. What is Data Warehouse?
3. Difference between Data Lake and Data Warehouse
4. When to use which?
5. Use cases of Data Lake
6. Use cases of Data Warehouse
7. Summary

What is Data Lake?

The term “Data Lake” is very similar to a real lake; in a lake, we have multiple water tributaries coming in. Similarly, a data lake has structured data, semi-structured data, unstructured data, machine-to-machine, and logs running through in real time.

A Data Lake is a container or highly scalable data storage repository that can store large volumes of raw data, which can be structured, semi-structured, and unstructured in its original format until it is required.

Source: awsamazon.com

This massive storage pool handles the vast data that most industries produce without the need to structure it first. There are no fixed limits on account size or file to store it in a data lake. Data lakes help businesses to store and analyze the vast volume of unprocessed data to gain unexpected and previously unavailable business insights.

Data scientists and Data engineers are the end-users of data lakes.

What is Data Warehouse?

Data Warehouse is a large repository of organizational data which collects and manages data from varied sources(operational and external data sources) to provide meaningful business insights.

We can understand it as a process of transforming raw data into information because data is first processed and then organized into sections.

Source: www.sap.com

Data in a warehouse is structured, filtered, already processed, and ready for use to support historical analysis and advanced querying.

They are used to store information about products, orders, customers, employees, inventory, etc., and used by businesses to share data and content across department-specific databases. Entrepreneurs and Business users are the end-users of a data warehouse.

Difference Between Data Lake and Data Warehouse

Source: spec-india.com

• Data Storage

A data lake stores raw and unprocessed data from various sources like IoT devices, user data, real-time social media streams, and web application transactions. In the data lake, we keep all data regardless of source and structure; that’s why they need a large storage pool. Moreover, raw data is flexible, can be quickly analyzed for any purpose, and is perfect for machine learning. The only concern with data lakes is that sometimes they become data swamps without appropriate data quality and data governance measures at a point.

A data warehouse can only store the structured data that is extricated from value-based frameworks and has already been processed and refined. Data warehouses contain past data that have been cleaned to fit a relational schema and are ready for strategic analysis based on predefined business requirements. Data warehouses require no extra storage as they only store data that will be used in the future and avoid non-traditional data sources like web server logs, sensor data, social media activity, text, images, etc.

• Users

Data Scientists, Big Data Engineers, and Machine Learning Engineers are the major users of data lakes because data in the data lakes is highly unstructured and can only be used by users who want to study data in its raw state to gain unique business insight.

Source: bryteflow.com

Business Analysts, Operational clients, Managers, Business professionals, and end-users are the major users of data warehouses as they are familiar with the topic represented in the processed data. These users gain insights from business KPIs, as the data has already been processed to provide solutions to pre-determined questions for analysis.

• Analysis

Data engineers often use the flexible and scalable unstructured data stored in data lakes for big data analytics. However, we can use services like Apache Spark and Hadoop to run Big data analytics on data lakes. It offers predictive analytics, data visualization, machine learning, BI, and Big data analytics.

The cleaned and archival data stored in data warehouses are typically set to read-only for analyst users. It usually offers data visualization, BI, and data analytics.

• Schema

In a data lake, the schema is defined after the data is stored; this makes the process of capturing and storing the data faster. Also, a data lake uses the schema-on-read approach to process the data.

In a data warehouse, the schema is defined before the data is stored; this increases the time it takes to process the data.

But once the data is processed and stored in a warehouse, it is ready for consistent, confident use across the industry. Also, the data warehouse uses a schema-on-write approach to process the data and provide its shape and structure.

• Processing

Data lake uses ELT (Extract Load Transform) process where the data is extracted from its source and directly loaded in the data lake without any transformation. The data will only be processed when required.

Source: faun.pub

Data Warehouses use the ETL (Extract Transform Load) process, where the data is extracted from its source, cleaned or structured, and finally loaded into the warehouse.

• Cost

Data lakes are low-cost data storage, as the data storage is unprocessed. Also, they consume much less time to manage data, reducing operational costs.

On the other hand, data warehouses cost more than data lakes as the data stored in a warehouse is cleaned and highly structured. Also, they need more time to manage data which increases operational costs.

When to Use Which?

Both the data lake and data warehouse have their significance and purpose of use, but still, people get confused about which to use where. To understand this better, organizations must first understand their business model and its requirements. Suppose the organization’s goal is to understand its business patterns and analytics or to launch something new based on its previous customer insights. In that case, the warehouse can be the best choice.

On the other hand, if the requirement is to study a huge volume of raw, granular, structured, and unstructured data especially required for machine learning and deep learning data, then a data lake will be the best choice for storage.

Source: aws.amazon.com

Some points organizations can consider while choosing the right data storage are.

Data lakes can be the right choice when:

• You are unaware of the data types that must be stored in advance.

• The data is messy and difficult to fit into a tabular or relational model.

• Datasets are constantly increasing in volume, and storage cost is a concern.

• You are not aware of the relationships between data elements in advance

• The project demands a complete raw dataset, especially used for data exploration, predictive analytics, and machine learning projects

A Data warehouse can be the right choice when:

• You know the data types that need to be stored in advance, and companies are uncomfortable with duplicate or additional data.

• Changes are very rare in data formats, and companies demand standard sets of reports for accurate results.

• The project demands highly structured datasets, especially those used for marketing, banking, and government-related projects.

Use Cases for Data Lake

1. Cybersecurity: Nowadays, online scams are becoming a new trend; no matter how large or small a firm you’re running, the fear of cyber attacks with phishing emails, ransomware, viruses, or DDoS attacks is constant. You have to be proactive instead of reactive to minimize the effects of cyberattacks. You must collect a huge volume of information to detect hacking patterns and easily protect your firm from these hackers. Data Lake is the best pool to store this massive information and works as a safeguard even if you get hacked by storing your data safely.

Source: brighttalk.com

2. Education: Like all other industries, Educational organizations are also competing to generate enormous amounts of data. Organizations are using the data lakes to store critical student data, including grades, attendance, etc., which help students get back on track but can also help predict potential issues before they occur in real-time. The flexibility of data lakes also helps educational organizations streamline billing, improve fundraising, etc.

Source: nus.edu.sg

3. Government: India is becoming a hub of governments, political parties, and non-profit organizations. All have one common motive of making our country smart, and even the smart city projects are already live in various states. We want to improve law enforcement practices, optimize waterways, enhance education systems, automate hospitals, and a lot more to make our country smart. Now, to implement these processes, all our government needs is unthinkable amounts of data from multiple sources like vehicles and citizens. The government uses data lakes to initiate the smart city project by dumping all the unexpected data into it.

4. Healthcare: For many years, we have been using data warehouses to store the critically large amount of data generated by the healthcare industries. But we lacked real-time insights from that because the highest part of data is unstructured data in the healthcare industry (i.e., physicians’ notes, clinical data, etc.). So, using data lakes capable of storing both structured and unstructured data tends to be a better fit for healthcare industries.

Source: allerin.com

5. Transportation: The ability of data lakes to make predictions helps various industries by providing a great source of insights. In the transportation industry(especially in supply chain management), predictions can help companies reduce costs by examining data from forms within the transport pipeline and improving predictive maintenance.

6. Genetics: Genetics in itself is the branch of science that deals with the abundance of human body patterns, and it needs immense amounts of data to be taken to further steps. Every human body generates tons of information that can be used to identify correlations and discoveries. Data scientists use Data lakes to collect massive amounts of human data; they need to understand better the human genome, which in turn makes revolutionary improvements to our lives.

Use Cases for Data warehouse

1. Finance and Banking: A data warehouse is often the best storage model in the finance and banking industries, as it allows structured access by the entire organization rather than an individual data scientist. It plays a vital role in investment due to the significant amounts of money at stake. When it comes to money, a single point difference can result in devastating financial losses for millions of people. Data warehouses act as smart storage in such cases by storing only relevant data to make precise forecasts.

Source: corporatefinanceinstitute.com

2. Hospitality industry: In the hospitality industry, data warehouses play a major role in advertising and promotion campaigns targeting users based on their feedback and travel patterns. With the help of structured data stored in data warehouses, we can easily track the inventory, analyze promotions and pricing policies, and closely monitor the customer’s purchasing behavior. This information is very crucial and helps a lot when it comes to business intelligence systems and marketing strategies.

3. Public sector: When it comes to the public sector, where reports play a major role, data warehouses help firms to analyze and maintain tax records, insurance policies, etc., building both personal profiles and group records.

Source: psmarketresearch.com

4. Laboratory: When we talk about medical reports, a single mistake can lead to disastrous outcomes, which means a difference between life and death. Data warehouses store the medical reports carefully, which helps in making accurate predictions, creating treatment reports, exchanging data with insurance agencies, etc.

Conclusion

Nowadays, right storage is the demand of every project or business because we can’t waste a huge amount of time or money in confusion. This difference will help firms choose the right data storage per their needs. Don’t forget that sometimes you might need a combination of both storage solutions, known as data lakehouse, which merge the flexibility of a data lake with the data management capabilities of a data warehouse, mainly used in building data pipelines. The key takeaways from the data lake and warehouses blog are:-

• Data lakes are the storages used when our purpose is to collect a large volume of heterogeneous data for gaining data insights to create a new model. Usually, data scientists use data lakes to generate fresh data patterns.
• Data warehouses are the storages used when our purpose is based upon the previous data of the same firm to analyze the structured data and know our customers’ behavior.
• Data lakes and Data warehouses are usually considered similar technologies, but the only similarity between them is their usage for storage. Otherwise, they’re very different in terms of user, schema, processing, cost, etc.