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- Data Lake vs. Data Warehouse
Data Lake vs. Data Warehouse
Analysts discover valuable new uses for existing business data every day. Big data-based decisions derived from real-time information drive companies in smarter directions, so proper data storage has become vital.
When it comes to data lakes versus data warehouses, the fact that both store data is one of their few similarities. Their structures, optimization, and goals could not be more different, and each specializes in different forms of storage and retrieval.
What Is a Data Lake?
Data lakes take their name from their structure: a massive pool of undefined, unsorted, unstructured data which may or may not have a current business purpose.
This raw data is rarely sorted or compressed, thereby requiring less processing power. The data remains unconverted and unsorted until it's retrieved, which saves time on both ends.
Information in a data lake can take any form. Server logs, social network activity, communication records, images, and sensor data can all be found in a data lake. Many users store historical data in case future analysts can use it.
This structure provides flexibility. Analysts with questions outside existing business practices dive into data lakes to find source information and context. Data warehouses are far too rigid.
Data Lake Example
Data lakes present a more convenient navigation solution when users need to access varied information fast. Health providers can build patient history files that include health records, photos, digital documents of visit notes, and more.
A data lake provides the flexibility needed to easily access those file types, which vary from patient to patient.
What Is a Data Warehouse?
Like a data lake, a data warehouse takes its name from its structure and the way it stores data. The similarities end there.
A warehouse is a single centralized structure for a specific purpose, with a standard template for sorting, storage, retrieval, and presentation that it follows in the same way every time.
Data warehouses only store processed data with a proven use. Information that you can extract in batches, that generates broad-scale reports, or that provides quick insights is well suited for a data warehouse.
This convenience requires investment in implementation. Once data is processed and reformatted, it’s hard to change.
Data Warehouse Example
Given the scale and flexibility of data lakes, it's easy to ask, "what is a data warehouse used for?” Despite their size, data lakes aren’t suited for every task.
Financiers who make snap decisions based on current trends need this convenience. With consistent information, investors waste no time searching for data they need and instead make necessary decisions.
Key Differences Between a Data Lake and a Data Warehouse
Since every business has different needs, in some cases, a hybrid model can be valuable.
The type of data stored by either data lake or warehouse differs. In a data lake, information is raw. This means it has not been processed, sorted, or converted into a usable format; data in a warehouse has.
The open schema makes information stored in data lakes more accessible, but the sheer volume of data also requires a greater storage volume.
Data warehouses store and process information in a more portable format. Charts, spreadsheets, tables, and graphs are easier to understand, so the structure ensures the data is more immediately useful and accessible to business users.
Information with a known purpose is stored in a formation, which may have no current business value at all. Data lakes are a future-proofing measure to create an archive for information that may be useful at some point.
Compare that to warehouses. It’s important to remember that the formatted information in a data warehouse already has a use.
It provides quick insights for business users who need the same information delivered in the same way every time. Just as data warehouses follow a data structure to store information, they deliver it in a structured, established way.
Use of Data
Data scientists and specialized tools are needed to navigate and translate information in a data lake. Their freedom allows them to ask new questions.
Business professionals do not need this flexibility. They need relevant data displayed in the same format every time. Data warehouses collate data into metrics and reports for ease of access.
Data Lake vs. Data Warehouse vs. Data Base
Not all companies need to store information from multiple applications. In that situation, a data base contains only information relevant to its assigned program.
Data Lake vs. Data Warehouse vs. Data Base: Which to Choose
Ultimately, all three centralize data to provide insights.
Unlike the multi-source formats of warehouses and lakes, a data base stores, searches, and reports information from one source. Its limited scope makes it the easiest to create and install. Most take the form of a relational data base, which not only records information but the connections between different items.
However, a data base should only be used when a single application generates the information. The other storage solutions handle information across all departments.
Structured vs. Unstructured vs. Semi-Structured Data
New business questions and requests for alternate information move too fast for data warehouses to keep up. In a data lake, this unstructured data is easy to access and accelerates the pace of research. Data bases are too tied to a single application to be useful for this type of large-scale processing.
Data bases store information in a rigid structure and do not store data from multiple sources well. Multiple formats and structures are not easy to parse in a data base. This same limiting structure makes them excellent for data analysis and monolithic applications. Just like the software they serve; data bases are best when self-contained.
Similarly, the data warehouse structure smooths the analysis process for those willing and able to work within their limitations. Operational users who need KPIs, metrics and to keep things moving find this format suited to them.
Data Processing Requirements
To store data in a warehouse, it must be analyzed and sorted. This work costs time and money. If both of those are in short supply, consider a lake, which requires no processing at all.
Both lakes and warehouses work well for multi-source data gathering with varied users and formats. Data bases can only pull from one application, making it easier to gather and sort the relevant information.
Data Storage Restraints
Thanks to sheer volume, data lakes require far more storage space, with increased costs as a result. Data bases tied to a single application require less space, and data warehouses provide a middle ground.
Data warehouses only store currently relevant information, which ensures no costs or space are wasted on important information.
Data Cost Restraints
This cost efficiency comes with higher setup costs but easily reaps many benefits with excellent cloud service providers and expert setup.
Who Will Be Accessing the Data?
Consider the target user base. Data warehouses deliver insights to a large audience fast, preferable for business clients, while data lakes free scientists to imagine out-of-the-box solutions.
Seagate Lyve Cloud for Data Lake and Warehouse Needs
No matter which architecture works best, Seagate is prepared to provide. With always-on availability and unmatched flexibility, Seagate Lyve Cloud has earned its place as the leading storage solution.