Chen Cuello
MAY 26, 2023
5 min read
Don’t miss a thing!
You can unsubscribe anytime

Many beginners in data processing and analytics are wondering what is a data lake? In essence, data lakes provide storage where users can process and safeguard different types of data, regardless of size or format. Data lakes store massive amounts of data to be further used for data analytics, BI (business intelligence), and ML (machine learning).

One of the key perks of data lakes is that it allows users to safeguard, i.e., store all enterprise data from various sources. When this data is in a data lake, users can then easily collect, store, and process data. 

This article will provide a comprehensive data lake overview, important concepts, benefits, and best practices.  

The Importance of Data Lakes in the Digital Age

As we mentioned, data lakes are your pools of data, all in one place. Data lakes allow users to store relational data (operational data and data from line of business applications) and non-relational data, such as mobile apps, IoT (Internet of Things) devices, and social media.

In today’s digital-first world, data lakes allow users to better understand the lake’s data via cataloging, indexing, and crawling. Because of their versatility, data lakes can be created in a cloud format or on-premise. 

A cloud-based data lake is easy to access from anywhere and uses the data stored at any given moment. 

What is a Data Lake?

In essence, a data lake is a centralized repository that processes, stores, and secures massive amounts of data that are either structured, unstructured, or semi-structured. 

Data lakes can store data in its initial format and process all of it regardless of size. Data lakes double as scalable and secure platforms that enable users to:

  • Ingest data from source(s) at any speed, regardless of whether data comes from cloud-based (Databricks), on-premise, or edge-computing systems.
  • Store large volumes of data of any type.
  • Process all data in real-time or batch mode.
  • Analyze data via Python, R, SQL, other languages, analytics applications, or third-party data sources.

Data Lake Definition 

There are a lot of aspects to a data lake definition. In its simplest form, a data lake is a repository that carries expansive amounts of raw data in its native format until further processed for analytic applications.

Comparably, traditional data warehouses store data in hierarchical tables and dimensions, while data lakes run on flat architecture to store the data, mainly in an object or file storage. This particular trait of data lakes allows users with more flexibility in the management of data, as well as in storing and using data.

Concepts of Data Lakes

Can you imagine Amazon not using a data lake? With mind-boggling amounts of data incoming into the Amazon servers, doing nothing with said data is a waste of money and business intelligence.  

An AWS data lake, short for Amazon Web Services, stores vast amounts of data that are later processed, analyzed, and used as valuable BI to offer Amazon clients top-notch service. In essence, data lakes are often confused with data warehouses, but there are a few differences that a worth mentioning.

Data Lake Overview 

For a comprehensive data lake overview, we’d have to start by saying that data lakes are practically the best option to refine and scrutinize petabytes of information pouring in non-stop from various sources.

One petabyte of data equals 1 million gigabytes! In other words, that’s 500 billion pages of hard-copy text or more than 58 thousand 2-hour movies in high definition. For businesses, data lakes serve to search and analyze petabytes of data.

Data Lakes vs. Data Warehouses 

Data lakes and warehouses are often confused and used interchangeably – but they are quite different. In essence, both concepts revolve around data storage. Both data lakes and data warehouses allow users to store organizational and non-organizational data to deliver better business decisions.

The main distinction between a data lake platform and a data warehouse platform is the architecture. Data warehouses store structured data which is consolidated into the same format, whereas data lakes store both relational and non-relational data in different formats. 

Characteristics of Data Lakes

What separates data lakes from other big data storage solutions are the following characteristics:

  • Data lakes are open to all data, without limits in the type or source of data;
  • Data lakes store data in its native, raw format without consolidating it upon entry.
  • Data lakes follow a schema-on-read strategy.
  • Data lakes can handle massive volumes of call logs, ERP transactions, etc.
  • Data lakes provide queries using much lower-cost storage. 

Benefits of Data Lakes

Data lakes are a great base for data science and advanced analytics applications. Data lakes allow establishments to handle their business operations more efficiently while keeping track of business trends and opportunities. 

Flexibility in Storing and Processing Different Types of Data

Since data lakes store raw, structured, semi-structured, and unstructured data, these storage solutions do what other data storage platforms don’t. Businesses that deal with a plethora of data daily and use data lakes gain valuable time and efficiency by managing all sorts of data, regardless of type and size.

Scalability and Cost-Efficiency 

Cost-wise, data lakes are a less expensive alternative to warehouses, allowing businesses to collect massive volumes of data from rich media, websites, mobile apps, social media, email, and IoT sensors.

In addition, today, data is generated on a per-minute basis, so businesses in the data-analysis industry can prosper a great deal by including a highly scalable data storage platform like a data lake. 

Enhancing Data Quality and Consistency

Data lakes improve the organizational structure by feeding data into the lake. In that context, data is refined to greater quality and consistency.

Improved Data Analytics and Insights

By storing granular, native-form data, data lakes allow for more advanced and complex data analytics, such as predictive modeling and machine learning. 

Use Cases of Data Lakes

Data lakes are the standard for centralized repositories, including and handling both processed and unprocessed data (such as text, images, media files, and server logs).

Use Cases for Data Lakes Across Industries

Thanks to the ability to store vast volumes of data in a central location, data lakes are the most widespread data technology for data scientists and businesses aiming to bypass silos and optimize the power of data.

Examples of Organizations Using Data Lakes

  • The oil and gas industry is one of the pioneers of adopting data lakes. According to industry information, the gas and oil industry generates approximately 1.5 terabytes of data daily. 
  • Life sciences rely on data lakes to deploy data exploration and discovery to ensure a more comprehensive approach to the human genome. The industry aims to predict and detect faults, which is why data lakes are an exceptional asset to the industry.
  • The marketing industry is nothing without data. Data lakes allow the marketing industry to monitor and analyze data in real-time, develop client-specific campaigns, etc.

Data Lake Architecture

The specific architecture of data lakes allows for the involvement of different technologies. Organizations that use data lakes can combine their preferred technologies differently. 

For instance, one organization might make use of Hadoop along with the Spark processing engine and HBase, which is a NoSQL database powered by HDFS. On the other hand, another organization might use the Spark processing engine for data stored in the Amazon Simple Storage service. 

Key Components of Data Lake Architecture

The specific architecture of data lakes ensures different types and sizes of data are properly and efficiently managed. In a nutshell, these are the crucial components of a data lake architecture.

Data Ingestion

As the initial process, data ingestion revolves around the method of transporting data from single or multiple sources to a defined location with the intent to further analyze and process it.

Data Storage

The beauty of data lakes lies in their ability to store massive volumes of data from different sources without losing trace or data quality. The data storage properties of data lakes are very scalable and able to store and process native data and support both encryption and compression at large volumes. 

Data Security

Data lakes are designed to offer impermeable data security options, ranging from using multi-factor authentication, authorization, data protection, role-based access, and so on.

Data Governance

All processes, including data ingestion, preparation, cataloging, integration, and query acceleration, should be aligned to generate data quality at the enterprise level. 

Common Data Lake Architectures

A Lambda architecture is a data-processing method that efficiently handles massive amounts of data. The high level of efficiency of the Lambda architecture is apparent through elevated throughput, minimized latency, and margin-level errors.

A Kappa architecture is an alternative to the Lambda architecture in cases where batch layers and active performance isn’t required. You’ll find kappa architecture in the real-time processing of certain projects. 

Best Practices for Data Lake Implementation

Since the goal of data lakes is to engulf vast, raw data from multiple sources, many major enterprises handling big data are building data lakes to ensure their data is processed, analyzed, and stored in time.

Whether it’s internal, external, structured, unstructured, or semistructured data, data lakes will tend to all of it.

Identifying Data Management Goals and Objectives

Data lakes help organizations implement their business strategies and specify their goals, means of operation, milestones, and so on. Data extracted from data lakes can help identify the organization’s criteria for assessing the success of the data lake and creating a storage system that enables data analysis.

Building a Data Lake Team

Data lakes are flexible and versatile, meaning different professionals within an organization can operate the data lake. In other words, a data lake team can be comprised of data scientists, business analysts, data developers, and more. 

With a data lake team, organizations ensure the quality of data, proper analytics, and refining of the most relevant data to be used for decision-making.

Establishing Data Governance Policies

By employing data lakes in your organization, you are participating in the governance of data that improves data quality effectively and increases data used for a multitude of purposes. With the right data governing policies, organizations can reach the best business decisions and pave the way to operational progress.

Implementing Data Quality Control Measures

Data lakes enable professionals like data developers, data scientists, and operations analysts to access different types and sizes of data to ensure its quality. Data lakes work well with most analytic data tools and frameworks. 

How Can Organizations Implement Data Lakes?

As versatile and flexible data storage solutions, data lakes are a cost-effective solution for data-based organizations and enterprises. Data lakes are a vital component of the data architecture process. Companies rely on data lakes to act as platforms for big data analysis and similar data science applications that need massive amounts of data to fulfill their business goals.

Data lakes are a great asset for different analytics methods in the range of predictive modeling, machine learning, and data mining.

Any organization handling loads of raw, native data can make great use of data lakes. For instance, if you’re prepping data for Amazon Athena, using a data lake can help process and analyze different types and sizes of data to feed for the best business-making decision.


What is a data lake vs. a data warehouse?

In essence, data warehouses are used for storing structured data, while a data lake is used as a centralized repository for storing all types of data at any scale.

What is a data lake vs. a database?

A database is used for storing existing, current data needed to make an application functional. On the other hand, data lakes store both existing and historical data.

Is SQL a data lake?

SQL is used for analytical and transformational purposes, including massive data loads in data lakes. 

Is Hadoop a data lake?

In essence, yes, Hadoop is a data lake. In fact, Hadoop can be best explained as an open-source software framework for storing data.

Is data lake an ETL tool?

To put it plainly, ETL (extract-transform-load) is a process related to data warehouses. On the other hand, ELT (extract-load-transform) is associated with data lakes. 

Minimize the firefighting.
Maximize ROI on pipelines.

icon icon