For anyone unfamiliar with big data, hearing the term ‘data lake’ may conjure the image of a holiday hot spot frequented by the top dogs of Silicon Valley. But banish the thought of Zuckerberg in a speedo from your mind for a moment (you’re welcome)… You could not be further from the truth.
In fact, the term data lake, and its older cousin, data warehouse, describe types of data storage and, often, the types of technologies used to facilitate that storage. Both are used by enterprise businesses to manage data of different formats and enable executives and analysts to derive decision-making insights.
In this blog, we explore both concepts and ask, data warehouse vs data lake: what's the difference So that you know which type of data storage is best suited to your business needs.
First, let’s tackle data warehousing.
What is a data warehouse?
A data warehouse is a centralised repository of information that enterprises can use to support business intelligence (BI) activities such as analytics.
Data warehouses typically store data from various sources, integrating the source data to provide a unified view. These sources can include transactional systems, application log files, relational databases and more. The data in question is most often historical data required for periodical queries; this historical record is referred to as a business's ‘single source of truth’.
Source: GM Insights
A standard data warehouse will include the following elements and functionality:
- A relational database
A relational database allows analysts to identify and access data in relation to other data via a structure made up of tables, rows and columns.
- An ETL solution
ETL stands for extract, transform and load; the acronym describes a common data preparation method for warehousing.
- Business intelligence capabilities
Tools that allow for statistical analysis, reporting and visualisation enable executives to mine and present data efficiently.
What is a data lake?
A data lake is a repository that allows for the storage of vast amounts of raw data, that is, data that has not been prepared, processed or manipulated to fit a particular schema.
Data lakes are typically housed within a Hadoop technology environment; Hadoop is an open-source software framework that facilitates massive data storage capabilities.
A key feature of a data lake is that no data is turned away, meaning that both structured and unstructured data formats can be stored. Because the data in a data lake is not subjected to data structures when it is ingested from the source, an ‘on-read’ schema is applied to facilitate data analysis as and when required.
Source: Grandview Research
Every data lake has the following common characteristics:
- A single, shared repository of data
With Hadoop-enabled data lakes, raw data is stored, and modifications to the source data are pushed to the shared repository throughout the data lifecycle - making data lakes particularly beneficial for auditing and compliance.
- Data management capabilities
Data lakes can facilitate both basic data management techniques (metadata management, data access control, and data asset management) and advanced data management capabilities (job management, process orchestration, data quality and data governance).
- Shared metadata
Metadata summarises information about each piece of data, making it easier to find and work with - it gives data context and meaning. Author, date created, date modified, and file size are all basic examples of metadata.
Data lakes provide full metadata, meaning analysts can manage all data-related elements to a far more advanced query level. Examples of advanced metadata accessible in data lakes include data sources, data formats, connection information, data schemas, and permission management capabilities.
Differences between data lakes and data warehouses
Now that you have a basic understanding of each term, let’s delve into the differences; there are several key factors that differentiate data lakes from data warehouses:
Type of Data
What type of data is stored?
- Data lake: Raw data that has not been processed or transformed - can include structured, unstructured or semi-structured data and data from mobile apps, IoT devices, and social media.
- Data warehouse: Data that has been structured during the transformation process to fit the relational database schema.
Why is it important to a business?
- Data lake: A data lake aims to store vast amounts and formats of data for future use when a determined need arises.
- Data warehouse: Solely intended to perform queries and analysis of large amounts of historical data. Warehoused data is defined as ‘in-use’ as it has been processed for a particular reason and to a specific format.
Who uses it?
- Data lake: As raw, unstructured data can be complex to navigate, data lake’s are primarily operated and managed by data scientists.
- Data warehouse: Accessible to operational users, such as data analysts and business executives with less technical knowledge as structured data makes it easier to use and understand.
What tasks does it perform?
- Data lake: Whilst data lakes are primarily tasked with the robust and cost-effective storage of large amounts of data, the flexible and scalable nature of unstructured data means they are good for big data analytics and deep learning projects.
- Data warehouse: Enables transactional reporting, expands business intelligence capabilities, enables accessible data visualisation and predictive analytics for future business forecasting.
How large is its storage capacity?
- Data lake: Larger in size as they contain all data, no matter the structure. For example, data lakes can often be petabytes in size.
- Data warehouse: More selective about the data they store, data warehouses are smaller than data lakes but are still large when compared to traditional databases.
How reliable is the data?
- Data lake: Considered to be less reliable due to the absence of a schema; all data is allowed in, curated or otherwise, and thus, data lakes contain a lower level of data quality.
- Data warehouse: Generally trusted as a ‘single source of truth’ - the fixed schema applied to data during the ETL process means a high level of data quality is achieved.
How is data processed?
- Data lake: On-read processing means that data is not transformed to fit schema until after it is loaded into the lake and accessed.
- Data warehouse: On-write processing means that the schema is applied to data as it enters the warehouse, transforming it accordingly.
How does it respond to change?
- Data lake: Agility is built into data lakes as standard as they have the ability to be configured and reconfigured regularly and at speed. Data lakes remove the need to wait for long development cycles to garner insights from their data.
- Data warehouse: The nature of a data warehouse is highly structured with a fixed configuration that is built to house specific data formats and answer particular questions - for this reason, they are less agile than data lakes.
Despite their differences, data lakes and data warehouses can be used to complement each other and expand a businesses capabilities in terms of unstructured data handling. For many companies, enhancing their existing data warehouse with a data lake, a practice known as DW augmentation, has proven to boost their return on investment and keep them competitive in an increasingly data-orientated marketplace.
Proper data storage ensures that your business can draw insights from the analytics produced and, in turn, make more informed decisions. When deciding what data storage method is right for your business, you must consider the types of data you work with, the volume of data, and your data analysts’ capabilities. An understanding of all three is essential to get the most from your investment.
If you’re struggling to become an omnichannel brand, why not book a demo of Hurree’s data unification and segmentation platform here or email any of your questions to me at firstname.lastname@example.org. It’s time to get your tools talking and we can help you do it!