Data is the most valuable ingredient for any organization, right from processing huge volumes of data to storing them to analyzing them for further insights. Data storage is a competitive task when we talk of big data, especially because of the absolute volume of data involved. Two popular methodologies focus on data storage and are often compared with each other – Data Lake vs Data warehouse.
Both these terminologies are mainly used for Big Data Storage and hence often used interchangeably. But there are major differences in what they offer. They have different purposes and are applicable to organizations, as per requirements. They have different structures and processing capabilities and hence will have a distinct user base.
Before we look at data lake vs warehouse, let us first understand the basic concept of these two technologies, their benefits, and salient features.
A data lake is a system or repository of data stored in its natural/raw format, usually, object blobs or files. A data lake is usually a single store of data including raw copies of source system data, sensor data, social data etc., and transformed data used for tasks such as reporting, visualization, advanced analytics, and machine learning. – Wikipedia
As the data lake definition suggests, a data lake is a huge storage repository that has ample raw data in its basic format. Just like there are multiple tributaries that get in water into lakes, there are multiple sources from where real-time data comes into data lakes. The data could be structured, semi-structured, or unstructured. It is highly flexible, has no fixed limit on size, and is used maximum by data scientists and engineers. It stores all the data irrespective of whether it is needed or not. It provides a huge amount of quantity of data for enhanced performance and native integration.
Each data component in the data lake is offered a unique identifier and there are certain extended metadata tags associated, that provide great analytical competencies. Data is stored with a flat architecture, and it can load and store data without transformation. Certain popular data lake organizations are Azure, Hadoop, Amazon S3, etc.
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. – Wikipedia
A data warehouse is a large storage location that gathers data from different sources, based on which the basis for business intelligence is generated. Especially for medium to large-sized organizations, data warehouses work best for sharing data and making data-driven decisions, across teams or databases. It is a mix of technologies and different components for the best use of information. Data warehouses focus on the electronic storage of vast information that is especially meant to provide analytical information as desired.
They transform the raw data into meaningful information. Popular organizations that offer data warehouses are Teradata, Snowflake, Yellowbrick, etc. The major functionalities that encompass the data warehouses are extraction, cleansing, transformation, loading, and refreshing of data. They store data in different files and folders that assist in using the data to make the best of business decisions, through a multi-dimensional view of data available in real-time. There is advanced querying and analytics available through a well-structured infrastructure. Even the cloud supports data warehouses and cloud-based data warehouses are the big thing now.
|Factors||Data Lake||Data Warehouse|
|Access to Data||Users can access raw data anytime from anywhere prior to its transformation. This makes it quick and effective to get results.||Users can access data only when it is set for transformation. Hence, it takes more time for the changes to get reflected.|
|Analytics and Purpose||Machine learning, data discovery, predictive analytics||Business intelligence, visualization, batch reporting|
|Type of Data||Non-relational and relational from IoT devices, mobile apps, social media, corporate apps||Relational from operational databases, transactional systems, business applications|
|Storage and Compute||Data lakes have decoupled storage and compute||Data warehouses have tightly coupled storage and compute|
|History||Relatively new technology in the world of big data||Has been used for decades for various databases|
|Flexibility and Scalability||Data lakes are easy to change and highly flexible||Data warehouses are very structured and hence tough to scale and change|
|Data Quality||Contains raw data that may or may not be curated||Contains high-quality data that is curated prior to storage|
|Data Security||Relatively evolving security concepts as it is a newer technology||Well-defined security processes as it has been existing for a long time|
|Ingestion Capabilities||Storage is possible with the least processing and data can be transformed only when needed||Data needs to be cleansed and refined prior to storage|
|Schema||Writing while doing the analysis. Schema is defined while the data is stored.||Designing before implementation. Schema is defined before the data is stored.|
|Query Results and Storage Costs||Quick query results through low-cost storage. Data storing is inexpensive.||Quick query results through high-cost storage. Data storing is costlier.|
|Users||Business analysts, data scientists, developers||Business analysts, operational users|
|Storage||All data is preserved in its raw form and transformed only when needed||Data is extracted from transactional systems, cleaned, and transformed|
|Agility||Highly agile, can configure and reconfigure as needed||Less agile and has a fixed configuration|
|Capturing of Data||Can collect varied data – structured, semi-structure, and unstructured in its original form||Can collect structured information and then organize it as necessary|
|Data Processing Method||Data lakes use Extract, Load, and Transform process||Data warehouses use Extract, Transform, Load process|
|Data Volume||Generally, in PBs or hundreds of PBs||Generally, in TBs|
|Data Format||Diversified with multiple sources and formats||Proprietary|
|Popular Tools||Amazon S3, Azure Blob Storage, etc.||Amazon Redshift, Google BigQuery, Panoply, etc.|
As they both belong to the ‘data’ community, there are certain characteristics that are common to both – data lakes and data warehouses. Here are they:
Businesses must use data lakes when,
Businesses must use data warehouses when,
The world of business intelligence services and big data is impacted heavily by data solutions. Of the lot, data lakes and data warehouses have always created an exciting comparison that is healthy to perceive. The above comparison matrix is clear enough to explain the goodness of both and why both have their niche carved for themselves.
Though different from each other, data lakes and data warehouses are complementary data solutions and work as a complete solution for enterprises. Together, they can extract the best value out of data. Let the users enjoy the benefits of data lakes and data warehouses, as the world of data gets larger and bigger!
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