In the age of digitization, data has become the real driver of businesses. The data, collected from different sources is turned into information and further into an insight that helps businesses to thrive. But, as the data flows through multiple sources, it is difficult for the traditional, relational database to handle it.
To overcome the data handling problem, two of the popular database architecture used are data lake and data warehouse. While both the terms for architecture are used interchangeably, they are quite different in terms of the data type that they store, the purpose that they serve, sources of data, quality of data, benefits, and more.
Before we move on to the difference between a data lake and a data warehouse, let’s understand what exactly these storage architectures are. How companies build and use data lakes and data warehouses to convert their big data into insightful information.
What is a Data Lake?
A data lake is a central repository that enables businesses to store data in a structured, semistructured, or unstructured state, from multiple sources, and on a different scale.
So basically, a data lake is an architecture for storing high-volume, high-variety, and as-is data at high velocity. The popularity of this storage architecture can be estimated from the fact that the global data lakes market is expected to grow at a rate of 28% between 2017 to 2013. | Market Research Future
Data lakes overcome three major challenges associated with data analytics:
Why a Storage Architecture like Data Lake is Needed?
Data lakes allow data scientists to mine and analyze large amounts of data (called big data). The term big data was coined by Roger Magoulas in 2005 to describe a large amount of data that couldn’t be managed or researched using traditional SQL tools (that were available at that time). Later, Hadoop was introduced that provided a search engine for locating and processing unstructured data for large sets of data, which actually made way for big data research.
If the survey results from Aberdeen has to be believed, organizations who implemented a data lake is outperforming similar companies by 9% in organic revenue growth.
The data in a data lake is stored in its original state and hails from almost any source like social media feeds, log files, internet-connected devices, online transaction processing, image/audio/video format, etc. Data scientists can use analytics tools and technologies like machine learning to identify and act upon opportunities that data brings in. It can help R&D teams in a company test their hypothesis, refine assumptions, and evaluate the output
Data lakes help businesses make informed decisions, increase operational efficiency, make data available from departmental silos/mainframe/legacy systems, and offload capacity from mainframe or data warehouse.
Data Lake vs Data Warehouse: How do they Differ?
A data warehouse is a repository of data from different sources such as relational databases, transactional systems, etc. A data warehouse is a relational database housed in an enterprise mainframe server. The data is accessed through business intelligence (BI) tools, SQL clients, or analytics applications.
There are three tiers of a data warehouse. The bottom tier is the database server where the data is loaded and stored. The middle tier has the analytics engine used for accessing and analyzing data. The top tier is the front-end client that presents the analytics results through reporting, analysis, and data mining tools.
While both the storage architectures, data lake and data warehouse helps businesses in big data analytics, there is a difference between the two. Let’s discuss it.
Warehouse usually had relational data from transactional systems, operational databases, and line of business applications. A data lake, on the other hand, has relational and non-relational data from a variety of sources such as IoT devices, websites, social media, mobile apps, business applications, and more.
Data warehouses have denormalized schemas, i.e. the schema is designed before the warehouse is implemented (scheme-on-write). Data lakes, on the other hand, have normalized schema, i.e. it's written at the time of analysis (schema-on-read).
The warehouse data is optimized for analytics and thus is used by business analysts, data scientists, and data developers. Users for a data lake are data scientists, data developers, and business analysts.
A data warehouse is generally used for batch reporting, data visualization, business intelligence while data lake is for machine learning, predictive analytics, data discovery, and profiling.
Data Lake vs Data Warehouse: What does your Business Need?Making a choice between the two storage architectures depends upon business requirements. If you still can’t decide which architecture works best for your business and how to get started with them, set-up a free consultation session with our experts.