Explore our overview of current use cases and the advantages of establishing a data lake. Learn why it’s worth investing in such a solution even if you already have a data warehouse.

Data lake use cases and benefits: Pour all of your proprietary and industry-related data into a single place


Once you figure out a suitable way of collecting, storing, managing, transforming, analyzing, and then using your data, you will be able to improve lots of your internal operations. According to a 2023 KPMG survey of 2100 executives around the world, 44 percent have connected all of their data sources to enable unhindered advanced analytics. New data analytics opportunities allow businesses to generate more revenue, make better strategic decisions, and tell compelling stories to their customers.

To consolidate all of their data assets in a single place, successful businesses implement data warehouses, data lakehouses, and data lakes. In this blog post, we dive deep into the concept of data lakes.

We discuss the benefits of a data lake for your business, explain use cases that data lakes are most suited for, and provide you with a table comparing several popular data lake providers to help you choose the best one based on your budget, functionality, and integration preferences. We also briefly compare data lakes with data warehouses to give you a general understanding of how data lakes stand out.

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What is a data lake and how does it work?

A data lake is a centralized data repository that can combine all types of data — structured, unstructured, and semi-structured — and allows for simple data retrieval and further use for data analytics. With the help of a data lake, businesses can better manage big data streams from both inside and outside sources, maintaining a comprehensive understanding of the state of affairs within the business. It’s a much better option than storing data in siloed databases scattered across departments.

What’s the working principle of a data lake? Compared to data warehouses, data lakes can store much larger amounts of data (and different types of data). In fact, the need for data lakes appeared due to the limited capabilities of a data warehouse. 

To be put into a data lake, data doesn’t need to be transformed and can stay in a raw format, which significantly increases the speed of data ingestion compared to a data warehouse. While data warehouses work based on an extract, transform, load (ETL) engine, data lakes use the extract, load, transform (ELT) pattern, as they transform raw data only upon request but not immediately after data extraction as data warehouses do.

Why and when should you use a data lake at your organization?

With a data lake, you can store any data together, reducing data storage costs and enabling new analytics capabilities that a data warehouse cannot support. However, having both types of data repositories can be beneficial, as data warehouses have a more convenient data structure for business intelligence and historical data analysis.

Grasp the core differences between data warehouses and data lakes.

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A data lake can be the right option for you if your business goals include the following:

Improving real-time data aggregation from diverse sources. If your company has lots of sources of incoming data such as IoT sensors and devices, customer data including reviews or surveys, social media, and corporate systems, then implementing a data lake can be a viable option. Plus, a data lake is extremely scalable (making it able to ingest lots of data) and can be optimized for low-latency queries and data retrieval. Additionally, implementing a data lake can allow you to combine all critical business data assets in a single storage place and help you conduct better root cause analysis to quickly uncover cross-company issues.

Enabling big data processing and analytics. Data lakes store all types of structured, unstructured, and semi-structured data and can easily integrate with advanced analytics and machine learning tools, allowing data scientists to efficiently perform deep data analysis. However, if implementing advanced data analytics seems complicated to you, consider Yalantis’ vast data science services to ensure this process is seamless and beneficial for your business.

Effectively using machine learning algorithms. The majority of data lake providers mention that data lakes can be particularly useful for data scientists, data engineers, and AI engineers for data exploration and model training purposes. Primarily, this is because data lakes provide access to big data sets that are comprehensive and complex enough for training and building accurate and effective machine learning algorithms. Plus, data lakes don’t have predefined schemas (used mostly for organizing data in relational databases and data warehouses) and offer more flexibility.

Seamlessly and cost-effectively integrating with a cloud environment. Transitioning your IT infrastructure from on-premises to the cloud can also include shifting most of your proprietary data into a data lake. Most cloud services vendors such as Amazon provide cost-effective and scalable data lake solutions. Maintaining a data lake on-premises can be difficult in terms of dealing with hardware failures, for instance, as a data lake handles large datasets and requires high computational power.

Domain-agnostic data lake integration capabilities

For their State of Cloud Software Spending report, Battery Ventures surveyed 100 chief experience officers (CXOs) in the manufacturing, financial, healthcare, retail, education, media, and travel industries. Below, you can see a table from this report showcasing what aspects of data management companies prioritize the most in 2023. We can see that investing in a robust data warehouse or data lake is a number one priority for businesses worldwide. When you establish an efficient data storage solution, all other aspects of the data infrastructure, such as data governance procedures, data visualization capabilities, data applications, and real-time data management, fall into place.

If you need your data to offer more insights than it does now, investing in a data lake can be advantageous.

Data lake benefits: From flexibility to reduced costs

Moving from the reasons for adopting a data lake, we should cover how you can benefit from its implementation. Here are a few common and uncommon data lake benefits:

Long-term data preservation. No datasets get lost or missed if you use a data lake. This type of data repository allows you to store all data your organization produces for the sake of further use during extensive data analysis. With a data lake, you don’t have to know all the use cases for your data right away but can simply store it for future data analysis. 

Flexibility and scalability. As you acquire more software solutions and devices for streamlined service delivery or improved business process management, a data lake has your back with its vast storage capacity. With less strict rules for storing data compared to data warehouses, data lakes prove more flexible and allow for simple data query and retrieval.

Enhanced customer service. Collecting miscellaneous customer data including purchasing choices, digital behavior, presence and level of activity on social media platforms, and feedback about your company or services can help you come up with personalized services, recommendations, and products during the data analytics process, thereby significantly increasing customer loyalty.

Vast data exploration and research capabilities. Storing and processing large amounts of data can unveil previously unknown business insights and help you conduct deep-dive marketing and business research. In this way, and with support from a big data company, you can better match your products and services to current customer needs.

Reduced IT overhead and capital expenses. Among the many advantages of a data lake are the fact that it’s a scalable, cost-efficient solution that centralizes large and varied datasets in a single storage space and provides self-service data access. Thus, implementing a single data lake can help you save energy, eliminate the need to hire more data engineers, and establish additional data storage systems to match the increasing business data flow.

Improved business continuity. Another benefit of a data lake is the ability to implement it as a single storage system to speed up your service delivery and maintain proper business continuity. For instance, Grand River Hospital rolled out a data lake on AWS and managed to migrate almost three terabytes of patient data. Thanks to this migration, they no longer needed 27 diverse healthcare applications. The data lake also allowed the hospital to use their data efficiently and economically.

Data lake advantages aren’t limited to those described above, as each business is unique. To receive the most value, you should consider integrating a custom data lake solution that fits your business routine and fulfills all of your needs. In the next section, we cover use cases where a data lake is an absolute necessity.

Data lake use cases: Make a data lake an integral part of your business processes

Implementing a data lake is not new or progressive, but as data lakes improve we can find more and more applications for them. Let’s discuss data lake use cases to see how you can efficiently use data lake solutions in different business scenarios.

Always-on business services. Real-time data ingestion allows data lakes to make any business data available at any time and upon request. For instance, mission-critical software like banking applications and clinical decision-making software need to function 24/7 to allow users to perform financial operations and transactions without interruption.

360-degree customer overview. Rolling out a data lake can help you aggregate all available customer data in a single storage space and enable all-around tracking of your customer data to offer customers timely services and increase customer lifetime value (CLV). For instance, the German online retailer Zalando decided to transition to a data lake to avoid storing customer data in legacy data warehouses and scattered databases across the company and enable simplified data querying for their business analysts and data scientists.

Real-time risk control. To comply with laws and regulations, businesses in different industries need to constantly monitor their data storage and data processing practices. With a data lake, conducting compliance monitoring is much easier, as you can rapidly overview large amounts of cross-functional data at one time. Overviewing data scattered across disparate databases and data warehouses can be time-consuming, prolonging regular compliance checks.

IoT ecosystem management. Data lakes are also beneficial if you have a large IoT ecosystem that regularly generates lots of data. This data is often unstructured and can be further used for advanced analytics, and a data lake can easily collect and store such data so you can use it whenever necessary. For instance, IoT devices provider Samsara uses a data lake to have sufficient reliability and scalability for querying data and performing large-scale data analysis.

As we’ve already discovered the benefits of a data lake, use cases for data lake solutions, and reasons to adopt a data lake, we can proceed to comparing common examples of data lakes to help you make a balanced decision by analyzing the critical characteristics of each data lake. 

Discover how Yalantis helped a manufacturing company consolidate production and business data in a data lake.

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Brief overview of how to build a data lake

To build a data lake solution, you need to design a comprehensive data architecture, taking into account data sources and types. A proper data architecture is critical to ensure seamless data aggregation, storage, and transfer by using diverse technologies. The choice of technologies largely depends on the combination of your technical team’s skills and expertise as well as your business requirements.

Plus, data lakes have a separate storage and retrieval flow for different storage classes:

  • Hot data that is used repeatedly and accessed regularly
  • Cold data that is used occasionally
  • Archived data that is no longer in use and is only necessary for rare reviews

Let’s explore reasons to establish a data lake at your company.

What different vendors have to offer: a comparison with data lake examples

In the table below, we compare five popular data lake solutions in terms of pricing, functionality, and setup difficulty. We have filled this table based on information available on each vendor’s website and the G2 platform, which includes thousands of real user reviews.

Cost. Establishing a data lake can be more cost-effective than rolling out a data warehouse. For example, for a data warehouse solution, Snowflake charges $40 per terabyte per month in the US, which comes out to $0.04 per gigabyte. Yet with a data warehouse, you get limited capabilities, especially if your aim is launching big data analytics.

Features. Each data lake example in the table above has particular features to stand out among competitors. Your task is to choose a provider with a feature set that is relevant to your business model. IBM offers an entirely new storage class called smart data storage to offer you more flexibility in data distribution. Azure puts particular emphasis on advanced scaling capabilities and acquiring security certifications to be able to service businesses across industries.

Implementation. According to user feedback on the G2 platform, compared to all other data lakes, Amazon S3 and IBM Cloud Object Storage got the lowest scores for ease of setup (8.5 and 8.1 points respectively). As for other vendors, customer reviews on G2 show that competing data lake solutions are moderately complex to set up, with scores ranging from 9.3 (Azure Data Lake Store) to 8.9 (Google Cloud Storage) and 8.8 (Snowflake). Many renowned data warehouse providers such as Snowflake have started offering data lake solutions to stay relevant on the market and offer their customers an easy transition. So if you already have a Snowflake or an Amazon data warehouse, it might be reasonable cost- and time-wise to consider implementing a Snowflake data lake.

Let’s move from data lake examples to a practical overview of how you can prepare for a seamless data lake implementation.

6 steps to successfully implement a data lake

Below, we share six important steps for data lake integration that can make this path clearer and less disruptive for you.

Step 1. Define business objectives. Understand why you need a data lake, what types of data you want to store, and what you want to achieve thanks to data lake implementation. You should also get stakeholder buy-in for this solution and ensure business and technical teams are on the same page.

Step 2. Select a storage solution. Choose a storage solution that fits your needs. Many organizations opt for cloud-based object storage like AWS S3, Azure Data Lake Storage, or Google Cloud Storage. Your choice can differ from the common variants we’ve discussed in the previous section. Plus, it’s important to ensure that the software company that sets up a data lake for you has experience working with the provider you’re interested in.

Step 3. Design a data architecture. Your data architecture should take into account data ingestion and data governance practices. Data ingestion processes are critical to bringing data from various sources into the data lake. This may involve batch or real-time data ingestion. Your data architect also needs to define data governance policies, including data quality standards, data retention policies, and access controls. 

Step 4. Organize data processing. Choose data processing tools and frameworks for data transformation, cleaning, and analysis. With their help, it will be easier for you to ensure a seamless big data analytics process. Such frameworks as Apache Spark, Apache Hadoop, and serverless cloud-based services are common choices.

Step 5. Ensure sufficient security. Security measures like encryption and regular monitoring are crucial to protect your data lake from unauthorized access and malicious attacks. For instance, AWS provides access control lists and access history to keep you up to date with all activity in your data lake.

Step 6. Enable effective data cataloging and discovery. Creating a data catalog makes it easier for data scientists and data engineers to query and understand the data in your data lake. You should also consider establishing metadata management practices to preserve relevant information regarding your datasets.

If a data lake is still a new concept for you, the above recommendations can be helpful. But if your aim is to improve the situation with a current data lake that no longer serves your needs, you should consider consulting our data engineering team to discover all the bottlenecks and come up with relevant solutions. 

We’re eager to help you thoroughly prepare your data environment for a streamlined data lake rollout and design a custom and modern data lake solution, considering your unique business needs and requirements. 

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How is a data lake better than a data warehouse?

A data lake is better than a data warehouse in certain scenarios because it offers more flexibility and scalability for storing and analyzing diverse data types, including structured and unstructured data. Unlike data warehouses, data lakes can handle large volumes of raw data without the need for upfront data modeling and schema design. This allows organizations to collect and store data from various sources and decide how to structure and analyze it later.

What data lake services do you have the most experience with?

We’ve released lots of data projects using AWS and Microsoft Azure, as these cloud services not only have data lake formation tools but also lots of other services that help streamline data management and data analytics flows. We can provide you with end-to-end data management solutions beyond merely setting up a data lake.

Is a data lake suitable for small and mid-sized businesses?

Yes. It’s a common misconception that data lakes are only suitable for large enterprises. These data repositories can also be beneficial for small and medium-sized businesses, but their implementation should align with the organization’s specific data and analytics needs and take into account available computing resources.

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