In the era of data-driven decision-making, choosing the right data storage solution is crucial for organizations. Although two prominent options – data lakes vs. data warehouses – may sound like they’re describing the same thing, they offer distinct approaches to data management. As with any decision, the choice between data lakes and data warehouses comes with trade-offs.
In this blog, we’ll cover the differences between a data lake and a data warehouse, the benefits and disadvantages of each, and the scenarios that call for which solutions.
What is a Data Lake vs. Data Warehouse?
A data lake is used to store raw data, which can include structured, semi-structured, and unstructured formats. This data can later be processed and analyzed to uncover valuable insights.
Unlike a data lake, a data warehouse is a specialized repository designed specifically for structured data. This data has been thoroughly cleaned, organized, and processed, making it readily available for analysis using analytics and business intelligence (BI) tools. The path from data warehouse to reporting is considerably shorter than the journey from data lake to reporting.
What Are the Key Differences Between a Data Lake and a Data Warehouse?
Although data lakes and data warehouses can both serve as cloud-based solutions, they differ in many ways, including: structure and design, purpose and focus, and the sources included.
Data Structure and Design
As previously mentioned, data is stored in its raw format in data lakes. This could be structured (like database tables or Excel sheets), unstructured (such as images or audio files), or semi-structured data (XML files, web pages, etc.). Structured data is stored in data warehouses, which are more ready for specific analytics and BI processes.
Purpose and Focus
The data structure and design of data lakes and data warehouses also dictate their respective purposes. Data lakes are well-suited for data exploration and discovery. They are often used in conjunction with machine learning or advanced analytics processes. On the other hand, data warehouses are primarily used more for reporting and decision-making instead of purely exploration.
Utilization and Users
Engineers and data scientists often prefer data lakes because of their flexibility with raw data. Data lakes enable users to access raw data for tasks like machine learning or initial exploration, with the option to structure and analyze it later. Conversely, data warehouses are primarily used by BI analysts and other users focused on creating front-end data reports. Data warehouses offer structured and organized data, making them suitable for users requiring refined and processed data for analysis and reporting.
Accessibility
While data lakes can be more accessible because of how adaptable the data can be in its raw format, this also means that an intermediate step may be required before it can be used to make connections and decisions. Data warehouses can be more accessible to business users, especially those who have experience with BI tools and how to analyze and build queries.
Data Sources
While data warehouses store structured data, data lakes can store data from a broader range of sources, including:
- Internal and external databases
- On-premises storage
- Cloud storage
- Sensor data
- Internet of Things (IoT) devices
- Log files
- Unstructured data (i.e. videos, images, text)
Preprocessing
Data lakes store raw data in its native format, without needing preprocessing. Data warehouses, on the other hand, require preprocessing before data is loaded. For structured data, cleaning, transformation, and formatting are necessary to align it with a predefined schema before loading it into a data warehouse. This preprocessing guarantees data consistency and accuracy in the warehouse, enabling efficient querying and analysis using BI tools.
Data Quality
Because data lakes can store any kind of data, from structured to unstructured, it’s also safe to say that there is a lot of variability in quality. While high-quality data may exist in a lake, it can be harder to find.
Data warehouses, because they only store processed data, ensure that you can find high-quality data that’s ready for use.
Performance
It can be a lot harder to find what you’re looking for in a messy room compared to one that is organized. The same principle is true for data lakes and data warehouses. Think of a data lake like a messy room. Even if everything is present, and then some, finding the data you need through querying can take a while, which means performance suffers. Data warehouses can be queried more quickly, boosting performance.
Cost
Storage and processing demands are higher for data lakes because of their structureless nature. Managing data warehouses is less expensive, but they can require more upfront costs to set up in the first place.
Security
Data lakes don’t just contain a mix of structured and unstructured data. They also contain data with various levels of sensitivity. Because pre-processed data resides in a data lake, sensitive data may not have even been identified yet. Data warehouses tend to have more robust security features in place. These can include encryption, auditing, and access control.
Benefits and Disadvantages of a Data Lake vs. a Data Warehouse
There are two sides to every coin. The advantages of data lakes and data warehouses come with equal, opposing disadvantages. Knowing which solution is right for your data, along with the benefits and drawbacks, can help you decide how your data needs to be housed.
Data Lake Benefits
Data lakes offer flexibility because they can store raw data in any format. Like resources within a cloud-first strategy, they can be scaled up or down on demand, and they can be a cost-effective solution for storing lots of data.
Data Lake Disadvantages
However, the costs you save in storing data can be canceled out by the costs involved in querying the and finding what you need. There’s no predefined schema, which increases the complexity of managing a data lake as it makes the data more difficult to query. Other challenges include:
- They can be less secure and have lower performance
- It can be a struggle to ensure the quality of the data being added to the data lake
- Data that’s never analyzed or mined may take up unnecessary space
Data Warehouse Benefits
Querying with data warehouses is much more efficient, making it easier for businesses to take the available data and make quick decisions. If users understand the predetermined schema, data warehouses are easier to use. Oftentimes, there are more stringent security measures in place as well.
Data Warehouse Disadvantages
The time saved when using a data warehouse can bring cloud waste or unnecessary costs down, but it’s important to remember that storing data in a structured format can cost more than a data lake. Data warehouses are also less scalable because they use a predefined schema that isn’t as flexible. Other challenges include:
- Since data warehouses are information-driven, there needs to be a significant amount of time dedicated to standardizing business-related terms and common formats, as well as restructuring schema to alsign with business needs while ensuring data accuracy
- Proper planning and setting up data orchestration is critical – an outline needs to be created of how to copy data from source systems to the warehouse, as well as when to migrate historical data from operational data stores to the warehouse
- Data needs to be cleaned as it’s imported into the warehouse to maintain data quality
When to Use Data Lakes vs. Data Warehouses
Choosing between data lakes and data warehouses is an important decision in the world of data management, each has its strengths and best-use characteristics. Consider the following common scenarios when trying to decide whether a data lake or data warehouse is more appropriate for your needs.
Data Lake Use Cases
- Centralized Repository for Business Data: Data lakes can handle vast amounts of data cost-effectively, thanks to their scalability and versatility in accommodating various data types. This allows businesses to store significantly more data in data lakes compared to data warehouses, all without the constant concern of cost optimization.
- IoT Data Storage: IoT devices produce enormous amounts of data, according the the International Data Corportation, the Global DataSphere is expected to double in size from by 2026 with about 45% of data attributed to IoT devices alone. Data lakes are well-suited for storing this data for analysis. This storage capability assists organizations in optimizing operations, enhancing product performance, and elevating customer experiences.
- Data Exploration and Data Discovery: Data scientists and analysts can explore raw data in data lakes to discover new patterns, trends, and insights. Since data lakes can store diverse data types, they provide a playground for exploratory data analysis.
- Big Data Processing: Data lakes can store vast amounts of raw data from multiple sources, enabling organizations to perform complex big data analysis, predictive modeling, and machine learning algorithms on the data.
- Real-Time Analytics: Data lakes can handle real-time data streams, allowing businesses to analyze and gain insights from data as it is generated. This is particularly useful in industries such as finance and online retail, where real-time decisions are crucial.
- Data Warehousing Offloading: Organizations can use data lakes to store raw data before it’s transformed and loaded into a data warehouse. This helps offload the ETL (Extract, Transform, Load) processes, making it more efficient and cost-effective.
Data Warehouse Use Cases
- BI and Reporting: Data warehouses provide a centralized, structured database for historical and current data. Businesses can use this data to generate reports, visualize trends, and gain insights into their operations. This is crucial for making informed business decisions.
- Historical Trend Analysis: The data warehouse can store historical data from multiple sources, representing a single source of truth. Data warehouses enable businesses to analyze trends over time. This analysis aids in understanding long-term patterns in sales, customer behavior, website traffic, and more, assisting businesses in making data-driven decisions.
- Natural Language Processing (NLP): Many organizations seek to enhance customer service via NLP, as it facilitates rapid analysis and can help boost growth in support, sales, and marketing. Data warehouses can effectively store extensive structured and unstructured data, enabling NLP model analysis. This analysis supports real-time responses, whether by internal staff or bots, like live chat assistance or personalized customer interaction based on historical data.
- Compliance and Regulatory Reporting: Industries such as finance and healthcare must adhere to strict regulatory requirements. Data warehouses aid in collecting, storing, and analyzing data necessary for compliance and regulatory reporting.
- Financial Analysis and Planning: Finance departments utilize data warehouses for financial reporting, budgeting, and forecasting. These tools enable detailed analysis of financial data, helping organizations plan and allocate resources effectively.
- Healthcare Analytics: Healthcare organizations use data warehouses to store patient records, medical histories, and treatment outcomes. Data warehouses enable healthcare professionals to analyze this information, improving patient care, treatment effectiveness, and resource allocation.
Choosing a Data Lake vs. Data Warehouse – Which is Right?
A recent Gartner study found that 57% of IT data and analytics leaders are using data warehouses, while 39% were using data lakes. Should your business choose one over the other, or some combination of both?
When you’re debating between data lakes and data warehouses, there’s honestly no “best option.” Really, the right storage solution is going to be what suits your objectives, budget, and skillsets. Our team at TierPoint can help with selection, implementation, and management – learn about our Data and Analytics Consulting Services.
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