What is a data lake?

A data lake is a centralized repository that houses data in its native, unprocessed, and raw form. It is designed to accommodate large amounts of data, including structured, semi-structured, and unstructured data from various sources. It can store as little or as much data as the organization requires. It is equipped to process and organize this raw data irrespective of its size and volume, offering high analytics performance and native integration.

A data lake stores this large amount of raw data in a flat architecture with metadata tags and a unique identifier for easy and quick retrieval. Essentially, a data lake enables enterprises to gather any type of data from any source without having to first structure it and enables them to analyze it using analytics applications or languages like Python, SQL, or R.

Data lakes for businesses

Data lakes are more than huge repositories of data. They facilitate easy ingestion and discoverability of data, along with a robust structure for reporting. They provide an immense amount of context to the data they store, which in turn enables organizations to acquire deeper understanding of business scenarios and run faster analytics experiments, such as performing machine learning (ML) on social media.

Results of such analytics help businesses to identify opportunities and implement strategies which in turn lead to growth in productivity and customer satisfaction. A data lake also makes data available at all levels, irrespective of designation and level, thus enabling better decision making at all levels. It is scalable and versatile. Given that data lakes provide a foundation for artificial intelligence (AI) and analytics, businesses across industries are adopting it for higher revenues and lower risks.

For instance, businesses who implement omnichannel marketing can find a data lake useful since their data sources span over channels, touchpoints, and even third-party data. This complex ecosystem of data continues to grow every day.

Why should an organization use a data lake?

A data lake is best suited for storing data that doesn’t need to be used immediately. Since there is no predefined schema, the data retains all its original attributes, allowing for harmonization later. Data lakes are increasingly becoming a favorite with businesses across industries because they provide an unrefined view to data analysts and are also cost effective since the data is processed only when the need arises. Some of the other reasons why businesses are choosing data lakes over data warehouses include:

Centralized data

Data lakes provide a single storage for massive amounts of data. The centralized repository prevents data silos.

Higher quality of analysis

The diverse and raw format of the data present in a data lake provides analysts with a robust and higher quality of analysis by presenting data in its original form. It is convenient to employ AI/ML techniques to data to gain important business insights.

Schema on read

Data lakes store any type of data, so there is no need to process it into any schema. The data is kept raw until it is needed for analysis, which is called “schema on read.” Schema is only applied when data needs to be analyzed. This saves on processing times during the ingestion of data into the data lakes.


Users can access and explore data in data lakes without moving it into another system. Given that insights and reports from a data lake can be pulled on an ad-hoc basis, it offers more flexibility in data analysis.

Competitive edge

Organizations gain a competitive advantage since better forecasts can be made with the raw data in data lakes. The analytical experiments also enhance the efficiency of business decisions.

Data democracy

Users across the organization, from different departments, levels, and teams, can access and perform a range of analytics on the same set of data.

Data lake concepts

There are some basic key concepts which will assist in understanding the architecture of a data lake.

Data ingestion

Different connectors are enabled to acquire data from different sources and load them into the data lake. Ingestion can be one-time, batch, or real-time loads where unstructured, semi-structured, and structured data can be loaded. Varied data sources like FTP, web servers, databases, or IoT items can be connected.

Data storage

Storage in data lakes is scalable and hence cost effective. It also translates to faster access.

Data governance

Data governance is the process to manage the availability, usability, security, and integrity of the data stored.


Proper and effective security protocols need to be in place to ensure the data is protected, authenticated, accounted for, and controlled. Layers of storage, unearthing, and consumption in the data lake architecture need to be protected to secure data from unauthorized access.


Data is where business value is derived from, thus data quality is an essential part of data lake architecture.

Data exploration

As the first step of data analysis, data exploration helps in identifying the correct data set before beginning analysis.

Data discovery

The data discovery stage is used to tag data in an attempt to understand it by organizing and interpreting it for further analysis.

Data auditing

Data auditing tracks two main changes to the data set:

  • Changes to the dataset elements
  • Logging the “how,” “when,” and “who” of the changes made

This feature helps in maintaining compliance and reducing risk.

Data lineage

Data lineage tracks the movement of data, where it originated from, where it moved over time, and what happened to it. This facilitates resolving errors, if any.

Together, all these elements help the data lakes to function smoothly, evolve over time, and provide access for discovery and exploration.

Architecture of data lakes

There are two components to any data lake: storage and compute. Both can either be stored on the cloud or on-premises, which leads to multiple combinations and configurations. Businesses can choose to host both in the cloud, on-premises, or opt for a hybrid model.

The data lake architecture consists of three components:

1. Sources

Data is ingested into data lakes from various homogenous and heterogenous sources. The following sources are often fed into data lakes.

  • Business applications: Database or file-based data store applications that store transactional data and are connected through connectors, APIs, or web services for the extract, transform, and load process (ETL)
  • Enterprise data warehouse (EDW): Existing enterprise data warehouses can also be sources for a data lake
  • Multiple documents: Flat files storing transactional data
  • SaaS applications
  • Device logs
  • IoT sensors: Data streams that are logged through IoT sensors can also be connected to data lakes

2. Data processing layer

The data processing layer contains the datastore, metadata store, and the replications which support the high availability data. This layer is well designed to support the scalability, resilience, and security of data. The administration maintains proper business rules and configurations.

3. Destination and analytics

Once the data is processed through the data processing layer, it is then forwarded to the target systems and applications through connectors. Some of these destinations include:

  • New enterprise data warehouses which are created by consolidating sources
  • Machine learning projects which extract raw data to generate optimized models in support of business cases
  • Many analytics dashboards which are built for data from data lakes
  • Data visualization tools like TIBCO Spotfire which use data from the data lakes to create analytics charts and graphs

Benefits of data lakes

Data lakes are more than just storage for full-fidelity data. They offer context which enables businesses to not only have a deeper understanding of business scenarios but also carry out various analytics experiments on it. Businesses can easily move raw data from different sources into the data lake without transforming it. This “schema on read” saves a lot of processing time and offers analysts the opportunity to access raw data for a range of use cases. A data lake also ensures other business requirements are met.

Simplified data management

Data lakes are equipped to handle large volumes, variety and velocity of data from different sources.


Since the data isn’t processed during ingestion, it can be written fairly quickly.

Reduced ownership costs

Compared to a data warehouse, a data lake is considerably less expensive since it enables companies to collect all sorts of data from a variety of sources without processing them.


Data is processed as and when required for faster and in-depth analytics. It is also easier to incorporate this data with artificial intelligence and machine learning applications.

Accessibility across organizations

A data lake provides “data democracy,” which means users irrespective of their level or designation in the organization can access and utilize data for their reports.

What challenges do data lakes face?

While in theory, data lakes might seem to be the ideal solution for any business, there are a few challenges they face that might hinder it from delivering on all the promises. However, this doesn’t mean organizations should not use data lakes. To ensure users reap all the promised benefits, they just need to manage and maintain data lakes in a proper manner. The following are a few of the challenges organizations may face when adopting data lakes.

High costs

Though there are open-source data lake platforms available, organizations must have the know-how to build and manage them, which might take longer and more resources. The alternative is to invest in managed data lake platforms, which usually have high fees.

Management of the data lake

Managing a data lake is not easy. Figuring out the capacity of the host infrastructure to support scalability and maintaining data integrity are just a few of the concerns that crop up—whether an organization is using an open-source or managed platform.


It takes time for a data lake to ingest large amounts of data and integrate with all other analytical tools to start delivering true value. The process of training in-house resources or recruiting new ones also contributes to longer timelines.

Data governance

As the data volume is considerably higher in data lakes, the process needs to rely more on programmatic administration. Unless proper governance is maintained, data lakes can easily become data swamps, which are inaccessible and a waste of resources. However, proper governance takes money and time.


Security in cloud-based data lakes still looms as a major concern for many businesses. Though appropriate protection layers have been introduced over the years, the uncertainty of data theft is still a challenge faced by data lake vendors.


Since many businesses already have an existing data warehousing system, they might not want to migrate to a system that has no use for all the structured data they have carefully curated over the years.

Outgrowing technology

While data is growing at an exponential rate, it is not being matched by computational powers of systems in place. Unless there is an efficient way of handling this growing data, businesses might end up spending more for computational power while saving on the storage methods.

Data lakes vs. data warehouses

A data lake is often confused with a data warehouse which are similar in their basic objective and purpose, since,

  • Both store data from various sources within any enterprise
  • Both create a one-stop data solution that eventually feeds multiple applications

A data warehouse stores data and processes and helps businesses with their analytics. The data stored is subject oriented (sales inventory, supply chain, etc.) and includes a time variant (day, month, etc.). A data warehouse is capable of combining data from multiple sources as long as they have a consistent data structure.

A data lake, on the other hand, can store data, regardless of its format, from multiple sources and is highly scalable in nature. It is ideal for storing data when it is not required for analysis or processing immediately.

The differences between them include:

1. Data capturing

Data lakes are equipped to capture data of all kinds and structures in their original form from their source systems. Data warehouses can only capture structured information that is organized into a predefined schema.

2. Data storage

The basic difference between a data lake and a data warehouse is the way data is stored in them. While the schema of a data warehouse is pre-defined, there is none in a data lake. This essentially means that a schema is applied while writing data to a data warehouse. Only processed and well-structured data is found in a data warehouse. This ensures quick analysis, but only for specific use cases that the data has been processed for. The data cannot be used for any scenario that has not been prepared for it.

A data lake allows the storage of data in its native, raw form. Hence, data lakes ingest data quickly and the data is processed only when it is used. This is known as “schema on read” as opposed to the traditional “schema on write” used in data warehouses. Data lakes, therefore, have a higher business value since they retain the original attributes of the data which can be used for any use cases that come up in the future.

3. User accessibility

As the data in a data warehouse is well structured and processed, operational users, even the non-tech ones, can easily access it and work with it. Data in data lakes, however, can only be accessed and used by experts who have a thorough understanding of the type of data stored and their relationships. This complexity, suitable for data scientists and analysts, prohibits access by regular users.

4. Flexibility

A data lake is more flexible than a data warehouse since it can adapt to changes quickly and is highly scalable as well. Storage in data warehouses often takes a lot of time and resources since the schema needs to be defined before the data is written in. Also, in case there are any new needs in the future, considerable effort is required to make the necessary changes.

Between the two, data warehouses are a good option for operational users who are looking for reports and other key performance metrics, while data lakes are ideal for businesses looking for in-depth analysis of their data. However, data lakes do not always replace data warehouses. In a few scenarios, a data lake can prove to be a staging area for a data warehouse. Assumptions and hypotheses can be easily tested on the data in a data lake, and only the most important ones can then be loaded into a warehouse for decision making.

With cloud, data science, and artificial intelligence technologies on the forefront of technology today, data lakes are gaining popularity. Its flexible architecture, ability to contain raw data, and holistic views into data patterns makes a data lake interesting for many businesses in their quest for better business insights.

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