Structured Data vs Unstructured Data
Before diving into the difference between a data lake and a data warehouse, it is helpful to understand structured vs unstructured data.
Structured data is generally maintained in a neat, predictable, and orderly format, such as tables or rows and columns in a spreadsheet. This could include: customer names, dates, addresses, order history, product information, and so on.
Data Lake vs Data Warehouse: What is the Difference?
As their names imply, a lake is much larger than a warehouse, so that is a giveaway about their respective storage capacities.
When you think of a warehouse, you likely picture rows upon rows of neatly stacked inventory that is labeled, categorized, catalogued, and accessible. This analogy holds true for a data warehouse.
Traditional data warehouses use an Extract Transform Load (ETL) process, which is where data is first extracted from the original data sources (such as a CRM, ERP, etc.) and mapped to tables in the data warehouse. Next, it undergoes a transformation stage where it is put into a structured format. This enables the data to be consistent, comprehendible, and ready for reporting and BI analysis.
A lake on the other hand is like a large soup of free-flowing elements in their natural (or “raw”) form. The “rawness” of these elements doesn’t necessarily mean that there is no structure or organization. When there is no structure or the structure devolves into chaos over time due to lack of design and planning, your data lake could become a data swamp. This is where proper data quality and data governance measures come into play.
A data lake is a repository for both structured and unstructured data in its original form and format. This data is unprocessed and does not yet have a specific purpose, but may be processed later for future analysis. Since it is unstructured, it typically caters to a more technical user such as a data scientist or a developer. As mentioned, the storage capacity of a data lake is much higher than a data warehouse.
A data warehouse is a repository for structured data from many applications and data sources. Here, the data is cleaned and organized for specific business purposes, and is ready for analysis in a BI tool.
You may have also heard the term. Again, the name gives us a clue to its size. It is essentially a smaller version of a data warehouse that stores a partitioned segment, or subset, of data from a business unit such as sales, marketing, finance, etc.
Benefits of Each Type of Storage
Data Lake – There are benefits to having your data in its raw form inside a data lake. One such benefit is the ability to access and analyze your data quickly and for any purpose. Since a data lake stores data in its original format, it can be immediately accessible for any type of analysis. Data within a data lake is never deleted, which allows the data to be retrieved for analysis, stored for later use, or shared with others.
Data Warehouse – there are three main benefits of storing processed data inside a data warehouse.
- Data warehouses have been around for a long time and have a mature security posture.
- Processed data is much easier to work with and accessible, particularly for less technical users.
- Data warehouse only stores processed data that will be used to answer specific business questions (e.g. what was our gross margin last quarter? How many units did we sell YoY). When you’re just paying for what you need, this means big savings, especially over long periods of time.
Data Mart – As we saw some cost-saving benefits with data warehouses, data marts are yet another cost-effective way to gain actionable insights quickly. Since data marts store a partitioned segment, or subset, of data from a business, this can accelerate processes by allowing less technical business users to access information that is relevant to their particular business unit (i.e. sales, marketing, finance, etc.).
The Role of Data Engineers with Data Lakes and Data Warehouses
The pipes in which data engineers lay help the incoming data conform to a predefined structure. This enables business units to use business intelligence tools to ask specific business questions and glean insights from their data. Any data that is not needed immediately, will not be included in the data warehouse. This is done to improve performance and reduce storage space, which is costly at scale.
However, as data sources increase and data pipelines become more complex, a business’ data storage process needs to evolve. Data warehouses may be insufficient, and for many companies, managing all that data becomes increasingly difficult to handle on their own. Expertise is expensive and hard to find. Instead, what many companies opt for is tasking the workload to a data scientist who may not have the specialized knowledge and experience needed to take on such a task.
At Infostrux, we offer data engineering as a service. We help data-driven organizations get the most out of their data.
Snowflake Data Warehouse and Data Lake
Snowflake Cloud Data Platform brings all your structured and semi-structured data together in a single platform, delivering the best storage and analytical attributes of both a data lake and a data warehouse.
Snowflake also offers unlimited scale and speed, simplicity, cost-effectiveness, and built-in security and governance.
Data lakes running on Snowflake Data Cloud can easily be created and scaled, removing storage limitations. Instead of exorbitant CapEX costs of on-premises data systems, Snowflake has an affordable OpEx, usage-based pricing model. This is cost effective since it is difficult to predict how much or how little storage you may need. Snowflake’s fully scalable on-demand, pay-for-what-you-need data platform can save companies a lot of money.
Snowflake is faster, easier to use, and far more flexible than traditional data platforms. These (among other reasons) is why we work exclusively with Snowflake data cloud platform.
Read: Why We Choose Snowflake