Data Lake vs Data Warehouse

To realize data as a competitive advantage in our modern world, value must be extracted from it. Data is fuel for artificial intelligence (AI) solutions that amplify better business decision-making. Data and analytics power C-suites and algorithms alike.

When addressing data in an organization for business use, a major consideration centers around how and where to collect, store, govern and integrate data for analysis and insights. And with the increasing volume and veracity of data generated at high velocity, what structure works best for a data-driven company to manage data at scale? A data warehouse? Data lake? Or the more recent data lakehouse? Is one better than another? What are the differences between them? To gain useful insight, let’s start at the beginning.

How Data Lakes and Data Warehouses Came About

Traditional and siloed databases were the original repositories for storing and managing data.

Fast-forward a decade, and organizations could only go so far with the large amount of information generated day to day and minute to minute.

These traditional on-premises databases manage different types of data separately. The downside of this situation is that silos are not organized in a way to extract information that could lead to insights across an enterprise. To address this issue, a united data repository was developed: the data warehouse. These data warehouses were able to bring together and house structured data. As even newer forms of data came about that had little-to-no structure, yet another type of storage method had to be created: the data lake.

More recently, as business needs evolved and the ways and means of managing all types of data for insights increased, companies grappled with the many approaches to its storage and management and the costs associated. Early on, the concerns centered around resources, cost and complexity, and were an impediment to progress. Now, due to growing pressures brought on by consumer demands, increasing competition and a swiftly advancing digital business landscape, concerns have shifted to modernizing an organization’s digital infrastructure as quickly and efficiently as possible. These and other factors come into play when deciding on the right kind of data storage and data management, including for:

  • New users – The types and the number of users accessing data have changed. In this era of data democratization, everyone across the organization needs quick and easy access to trusted data.
  • Data in the cloud – “By 2025, Gartner estimates that over 95% of new digital workloads will be deployed on cloud-native platforms.”1 This means that data in the cloud will likely grow tenfold compared to overall data doubling every 12 to 18 months. Not only is data distributed across siloed applications, but now it is physically stored in different clouds.
  • Machine Learning/AI – Organizations are looking to implement machine learning and/or AI algorithms to support new use cases, which require vast amounts of data.

Traditional on-premises enterprise databases are not equipped to support these newer demands. Deployed on dedicated hardware acquired by the organization and installed and managed by the IT team, they are expensive and time-consuming to set up, operate and scale. They can also take months to upgrade and often require a fair amount of regular maintenance that only an experienced database administrator can provide. What about data warehouses and data lakes? How do these options come into play with evolving business needs? Let’s start with an explanation of their key details and the differences between them.

Data lake vs data warehouse ROI; accelerate time to value with intelligent automation.

What Is a Data Warehouse?

A data warehouse is a type of infrastructure that allows businesses to bring together structured data sources. Data warehouses replace the kind of structured data environment that siloed databases provided and allow for data throughout an enterprise to be accessed and utilized for analysis at once.

Structured data in data warehouses is standardized, formatted and organized. This makes it easy for search engines and other tools to understand. Examples of structured data include business customer addresses organized into columns. Credit cards, phone numbers and health records are all coded in the same way. Data warehouses are organized, making structured data easy to find. However, data warehouses aren’t wired for unstructured data.

Nowadays, data comes from a variety of sources. Data no longer represents traditional office emails, documents, audio and video files. Key data also comes in the form of unstructured data like clicks on social media, input from IoT devices and user activity on websites. All this information can be extremely valuable to commerce and business. The downside is that it is more difficult to store and keep track of than structured data. Newer solutions came about to store and manage the growing amount of this type of data, called data lakes.

Learn more about data warehouses, the benefits of moving them to the cloud and key industry use cases.

What Is a Data Lake?

Data lakes manage unstructured data. With the rise of unstructured data, solutions came about to store and manage it, such as the open-source software, Apache Hadoop, in the early 2000s. With the software, large data sets could be stored and analyzed more easily. And so began the new era of data lakes.

Unlike a data warehouse, a data lake is perfect for both structured and unstructured data. A data lake manages structured data much like databases and data warehouses can. They can also handle unstructured data that isn’t organized in a predetermined way. And data lakes in the cloud are an effective way to store diverse data and can scale up to petabytes and beyond.

Learn more about data lakes, the benefits of moving them to the cloud and key industry use cases.

Data Lake, Data Warehouse or Both?

There is an increasing reliance on both structured and unstructured information, and the latter has grown exponentially. Data warehouses can't handle different data formats and workloads.

But data warehouses are the most steadfast. They’re consistent, predictable and high performing for structured data. This means data warehouses give you a level of fidelity and confidence. To help scale, enterprises are moving on-premises data warehouses to the cloud as a more cost-effective solution.

The Benefits of a Cloud Data Warehouse

  • It’s about high-quality data. Data warehouses provide structure. They make it easy to remove data that is redundant and dated. Clean, structured data enables the foundation for leveraging high-quality business intelligence and identifying key business trends. The validity of these insights correlates with the quality of your data. You’re more apt to move forward confidently with business decisions based on insights from trusted data.
  • Analytics with quality data saves time. Data warehouses ensure all the sources of data being integrated are organized, cleansed and stored. This makes batch analytical processing possible on a daily basis. With good database management, you can tap into essential data analytics without slowing down data flows to your operational systems. Cloud data warehouses can offer big efficiency boosts, although if you need to address regulatory requirements, data privacy or latency issues, you may want to consider an on-premises data warehouse.
  • Faster insights at scale reduce costs. As mentioned, a data warehouse provides clean and organized data. Working with clean data leads to faster insights, which enables better decision-making. When you run your data warehouse in the cloud, you can manage data at scale. This can lead to more efficiency at a lower cost.
  • Long-term decision-making gets a boost. Deeper insights can happen when there is more data at your fingertips. Using a data warehouse to simultaneously store, manage and analyze in real-time leads to better long-term, data-driven decision making.

The Benefits of a Data Lake

Cloud data lakes deliver many benefits to organizations, such as:

  • Quick data ingestion. Large amounts of unstructured data are a reality for nearly all industries, and data lakes provide the means to quickly store that jumble of data. • 
  • Scalability. Industries that dealt in terabytes just a decade ago now verge on petabytes. Data lakes can handle colossal volumes of data — and, since data lakes live in the cloud, they can expand with the needs of your business.
  • Productivity and accessibility. Good data and analytics can inform better policies, illuminate opportunities and demonstrate how resources can be efficiently used. Data lakes provide a means to rapidly store unstructured data prior to it becoming available for analytical tasks and data-driven decision-making.
  • Sharper decision-making. If you don’t store data, you can’t derive insight from it. Data lakes allow decision-makers to glean insights from both structured and unstructured data.
  • Better data science. Scientists and engineers need access to data. Data lakes give them more information to work with and analyze than traditional forms of data storage. AI and machine learning can benefit from data lakes, as they rely on the quality of data input into them.

To achieve business benefits from all this unstructured data, there needs to be a solid framework in place for data management. As unstructured enterprise data grows and grows, data management must be a business imperative for all.

Learn more with the CDO’s Guide to Intelligent Data Lake Management.

Why Do You Need a Cloud Data Lakehouse?

The volume of digital information stored on planet Earth has reached 64 zettabytes. This represents 60 times more bytes than there are stars in the observable universe. EarthWeb, a publication sourcing all things big data, data and statistics, and IOT, calculated that there are 2.5 quintillion bytes of data created every day in 2022. With 18 zeroes, that’s an unfathomable amount of data to store and manage, especially when you multiply that number by 365 days.2

Storing large amounts of unstructured data in one place has its challenges. If a data lake lacks standards or governance, it can quickly become a data swamp. Data swamps may be rich with information but work poorly for gaining insights. The lack of structure makes it difficult to obtain value from a data swamp. This data swamp leaves useful insights buried in its depths. Dirty data can hold a lot of information, but it’s not useful until it’s cleansed with good data management.

Enter the cloud data lakehouse, where the large amount of data in the data lake is given structure and governance. Simultaneously, the data lakehouse can still ingest unstructured, semi-structured or raw data from a variety of sources. A data lakehouse brings together the strengths of the data lake and the data warehouse on one platform. This makes the contents of a data lake more accessible to data scientists, data analysts and any other person or resource that can make use of it.

To address the volume, variety and velocity of data, companies are migrating their analytics workloads to the cloud. They are modernizing their infrastructures and applications. The combination of cloud data warehouses and data lakes offers impressive scalability and performance. They can handle that growing and evolving sea of data. Businesses can extract value from the data in real time using intelligent data management and governance tools. Businesses then have trusted data to speed up their digital transformation.

Learn more about modernizing your data warehouse and data lake in the cloud.

Reference Architecture for a Cloud Data Warehouse and a Cloud Data Lake

Reference Architecture for a Cloud Data Warehouse and a Cloud Data Lake

Data Warehouse and Data Lake Examples

Find out how the University of Rhode Island drives greater student success with data analytics derived from a cloud data lakehouse powered by Informatica’s Intelligent Data Management Cloud.

Read how Sunrun, a solar power company with 4,400 employees, increased their capacity for advanced analytics by moving their on-prem databases to the cloud, simplifying their data infrastructure, reducing costs and improving scalability.

Whether your data warehouse or data lake is on Snowflake, Databricks Delta Lake, Microsoft Azure Synapse, Google BigQuery or Amazon Web Services S3, Informatica can help you derive value from your data.

Data Warehouse and Data Lake Resources

Need more insights? Explore the topic further with these additional resources to understand how to leverage your data most effectively.

eBook: How to Solve the Top 10 Data Lake Challenges

Analyst Report: TDWI Insight Accelerator: Five Must-Have Data Integration Capabilities for your Cloud Data Warehouse

eBook: 10 Critical Factors for Cloud Analytics Success eBook:

6 Steps to Building Intelligent Cloud Data Warehouses and Data Lakes

Learn more: