Data Lakes, Explained

You might have heard terms like data lake, data warehouse, and data mart and wondered what to do with all of them. They sound similar, but they do not play the exact same role.

Before comparing everything, let’s take it one concept at a time. Today, we are tackling data lakes.

The term “data lake” began to gain traction around 2010 as a new way to think about storing, managing, and analyzing large volumes of data.

What is a data lake?

In simple terms, a data lake is a central repository that can hold large volumes of data in its original format.

That data can be:

• structured, like tables from a database;

• semi-structured, like JSON, XML, or log files;

• unstructured, like images, videos, PDFs, emails, or text documents.

The main idea is that you do not always need to know exactly how the data will be used before storing it. A data lake gives you a place to collect different types of data first, then explore, transform, or analyze it later.

This is why data lakes are often described as using a schema-on-read approach: the structure is applied when the data is read or analyzed, not necessarily before it is stored.

Modern data lakes are often built on object storage. Instead of putting everything into rigid tables from day one, data is stored as files or objects. Then, metadata, catalogs, access rules, partitions, and data engineering practices help people find, organize, secure, and use the data properly.

Why use a data lake?

A data lake can be useful when an organization has a lot of different data coming from many sources.

For example, a company might want to keep customer transactions, website clicks, application logs, IoT sensor data, and support messages in one place. Some of that data may be useful for dashboards. Some of it may be useful for machine learning. Some of it may simply need to be stored for future analysis.

Data lakes are especially helpful for organizations that want to build a strong analytics culture, experiment with data, train machine learning models, or support research and advanced insights.

That said, a data lake is not just a place to dump everything and hope for the best. Without governance, metadata, quality checks, and clear ownership, it can quickly become messy. People sometimes call this a data swamp: the data is there, but nobody can easily understand, trust, or use it.

What is the difference between a data lake and a data warehouse?

A data warehouse is usually more structured. Before data is loaded, it is cleaned, transformed, and organized into a defined schema. This makes data warehouses very useful for reporting, dashboards, and business intelligence, because the data is already prepared for analysis.

This approach is often called schema-on-write: the structure is defined before or during the loading process.

A data lake, on the other hand, is more flexible. It can store structured, semi-structured, and unstructured data in its raw or near-raw format. The data does not always need to be transformed before it lands in the lake.

This approach is often called schema-on-read: the structure is applied later, when someone needs to query or analyze the data.

You may also hear about data marts. A data mart is a smaller, more focused repository built for a specific team, department, or business area. For example, a finance team might have a finance data mart, while a marketing team might have a marketing data mart.

So, very simply:

• a data lake stores many types of data, often in raw form;

• a data warehouse stores structured, cleaned data for analysis and reporting;

• a data mart is a smaller, focused subset of data for a specific business group.

How does a data lake work?

First, it depends a lot on the organization. You can decide to have storage and compute resources on-premises, in the cloud, in a hybrid configuration, and so on.

Let’s start with the data sources.

Data Sources

As mentioned before, you usually deal with three main types of data:

• Structured data sources: this data comes from relational databases and tables. Examples include Google Cloud SQL and Azure SQL Database.

• Semi-structured data sources: this data has some organization, but it does not fit neatly into a tabular structure. It might have tags, keys, or a hierarchy, but it still needs some processing before being fully structured. Examples include JSON files or XML file.

• Unstructured data sources: this includes a wide range of data types without a predefined structure. Examples include media files or IoT data.

The second step is ingesting this data. This leads us to data ingestion.

Data ingestion

Data ingestion is the process of importing data into the data lake from different sources. Think of it as the gateway through which data enters the lake before being processed.

There are two main modes:

• Batch ingestion: this is a scheduled, interval-based method. Large chunks of data are ingested at a time. Examples of tools include AWS Glue and Azure Data Factory.

• Real-time ingestion: this brings data into your data lake as it is generated. This is important for time-sensitive applications, like fraud detection. Examples of tools include Amazon Kinesis Data Streams and Azure Event Hubs.

For this, you can use different protocols, APIs, or connection methods to link internal and external data sources. Having the right connectors helps ensure smooth data flows.

Once the data is in, we have to process and store it.

Data storage and processing

Once the data is ingested, it needs to be stored.

First, ingested data lands in the raw data store section, also called the landing zone. The data is kept in its native format, and the raw data store acts as a repository where data is staged before anything is done to it. Examples of solutions include Amazon S3 and Azure Data Lake Storage Gen2.

Then comes the transformation section. This is where we can transform the data; we can cleanse it by removing or correcting inaccurate records, discrepancies, and inconsistencies. We can also enrich the data by adding information or context, normalize it, structure it, and prepare it for future use.

Once that is done, the data becomes trusted data. It is reliable, clean, and suitable for analytics and machine learning models.

Now that the data has been transformed, it is moved to the refined or conformed data zone. More transformations may still be possible depending on the use case, but refined data is usually what analysts will interact with.

Tools like Amazon Athena or Google BigQuery may be used for querying this refined data.

Analytical Sandboxes

Analytical sandboxes are isolated environments used for data exploration. They allow activities like machine learning, predictive modeling, and data analysis without affecting the main storage and transformation layers.

This separation is important because analysts and data scientists can experiment freely without compromising the integrity or quality of the data in other zones.

Both raw and processed data can be used in these sandboxes. Raw data is useful for exploratory work, especially when the original context matters. Processed data is better suited for refined analytics, machine learning models, and business-ready analysis.

A few activities that can happen in analytical sandboxes include:

• Data discovery: this is when analysts and data scientists explore data to understand its structure, quality, and potential value. They might use statistics, summaries, or data visualization to get a first understanding of what the data contains.

• Machine learning and predictive modeling: this is where teams use data to train models, make predictions, or identify patterns. Examples of platforms that can support this kind of work include Amazon SageMaker and Google Vertex AI.

• Exploratory data analysis, or EDA: this is when graphs, plots, and summary tables are used to analyze the data and understand relationships between variables, patterns, or anomalies, without starting with strict assumptions.

Sandbox environments can be created using notebook or machine learning platforms such as Google Vertex AI Workbench and Oracle Machine Learning Notebooks. These tools allow users to write code, create visualizations, test ideas, and document their work in the same environment.

Data consumption

Finally, the consumption layer is where reliable data is used by people, applications, or reporting tools.

This is where business users, analysts, and decision-makers interact with the data through dashboards, reports, APIs, or analytics platforms. Examples of tools used at this stage include Microsoft Power BI and Google Looker.

At this point, the goal is no longer just to store or prepare the data. The goal is to turn it into something useful: insights, reports, predictions, and decisions.

Governance, security, and monitoring

To make a data lake work properly, we need governance, security, monitoring, and stewardship.

Without them, a data lake can quickly become difficult to use. The data might be stored somewhere, but people may not know what it means, where it came from, who owns it, or whether they can trust it.

Data governance

Data governance establishes the rules, policies, and procedures used to manage data. It helps answer questions like:

1. Who can access this data?

2. What does this data mean?

3. Where did it come from?

4. Is it sensitive?

5. Can it be used for reporting, analytics, or machine learning?

Governance also helps ensure data quality. This means checking whether the data is complete, accurate, consistent, and reliable enough for use.

Tools like Collibra can help add this governance layer.

Collibra is a data governance and data intelligence platform. In simple terms, it helps organizations understand, organize, and control their data. It can be used as a data catalog, meaning it gives users a searchable inventory of data assets across the organization.

For example, instead of asking, “Where is the customer data?” or “Can I trust this table?”, users can search in Collibra to find the right dataset, see its definition, understand who owns it, check its lineage, and review any policies attached to it.

Security protocols

Security protocols protect the data lake from unauthorized access and help organizations comply with data protection regulations.

A few important security controls include:

• Authentication: this checks who the user is. For example, a company can require users to log in through identity services before accessing the data lake.

• Authorization: this checks what the user is allowed to do. Someone might be allowed to read a dataset but not edit it, delete it, or share it.

• Role-based access control, or RBAC: this gives permissions based on roles. For example, a data engineer, a data analyst, and a business user may all have different levels of access.

• Access control lists, or ACLs: these define more detailed permissions for specific files, folders, or objects.

• Encryption: this protects data by making it unreadable without the right key. Encryption can be used when data is stored, also called encryption at rest, and when data is moving between systems, also called encryption in transit.

• Audit logging: this records activity in the data lake. It helps track who accessed what, when they accessed it, and what actions they performed.

For example, AWS Lake Formation can help manage permissions and secure access to data stored in a data lake. Azure Data Lake Storage Gen2 can use Azure role-based access control and access control lists to manage who can access specific resources.

Security is not just about blocking people. It is about giving the right people the right access to the right data at the right time.

Monitoring and ELT processes

Monitoring helps ensure that the data lake keeps working correctly. It tracks data pipelines, ingestion jobs, storage usage, processing tasks, access patterns, and possible failures.

For example, monitoring can help detect if a data pipeline stopped running, if data arrived late, or if a transformation produced unexpected results.

ELT processes help move data from raw form into more usable formats. In an ELT approach, data is first extracted and loaded into the data lake, then transformed later depending on the use case.

This works well with data lakes because they are designed to store large amounts of raw or semi-raw data before all transformations are known.

Data stewardship

Data stewardship involves the active management and oversight of data. It is often performed by specialized teams or designated data owners.

A data steward helps make sure that data is properly defined, documented, protected, and maintained. They may review data quality issues, clarify business definitions, approve access requests, or make sure that governance rules are followed.

Conclusion

So, what should we remember?

A data lake is a flexible way to store large amounts of different types of data. It can hold structured, semi-structured, and unstructured data, often in its raw format, so organizations can use it later for analytics, machine learning, reporting, or research.

But a data lake is not magic storage. It needs good architecture, clear zones, ingestion processes, transformation logic, governance, security, monitoring, and stewardship.

When done well, a data lake can become a powerful foundation for data-driven work. It gives teams the freedom to explore data, build models, create dashboards, and generate insights.