Information is the oil of the 21st century, and analytics is the combustion engine” – Peter Sondergaard, Senior Vice President, Gartner
Companies have always been driven by data. The proliferation of the internet resulted in more information being collected than ever before. This gave birth to the term, Big Data.
With data being generated at a massive scale, we needed a place to store all this data. This is where data warehouses and data lakes come in.
So, what exactly is a data warehouse and a data lake? How different are they from each other?
Well, we’ve got you covered! Let’s dive in to know more.
What is a Data Lake?
A data lake is a centralized storage repository that holds a massive amount of structured and unstructured data. According to Gartner, “it is a collection of storage instances of various data assets additional to the originating data sources.”
This can be easily remembered with the acronym ISASA.
Ingest Store Analyze Surface Act
In simpler terms, a data lake is like a real river or lake in its natural state. Just like you have multiple tributaries flowing into a lake, a data lake has all kinds of data flowing through in real-time.
What is Data Warehouse?
Data warehousing is about the collection of data from varied sources for meaningful business insights. An electronic storage of a massive amount of information, it is a blend of technologies that enable the strategic use of data!
Let’s go through a diagram, starting on the left.
You have the operational systems within the organization such as marketing, sales, and so on. You take their information and put it into the staging area.
Now, we need to work out how to get all of this into one logical framework. Right after it goes through the integration layer, it then goes into the data warehouse in a format that is standard across all that data in that data warehouse.
This warehouse will be huge, since we have taken data from across the organization and put it into one large database. However, the director of marketing or the managing director might have a set of questions. Thus, for them we create their own data marts, that will be much smaller than the data warehouse and will give answers quicker.
Now that we have an idea about both data lake and warehouse, let’s compare the two!
|Data is raw and all types—structured, semi-structured, or unstructured—is captured in its original form.
|Data is processed and only structured information is captured and organized in schemas.
|Ideal for users who carry out deep analysis such as data scientists and need advanced analytical tools.
|Ideal for operational users such as business professionals and moguls since the data is structured and easy to use.
|Storing data is relatively inexpensive.
|Storing data is time-consuming and costly.
|Updates can be made quickly thus making it highly accessible
|Costly to make changes, thereby quite complicated
|Position of Schema
|Schema is defined after data is stored, thus making it highly agile.
|Schema is defined before data is stored, thus offering performance and security.
|Uses ELT (Extract Load Transform) process.
|Uses ETL (Extract Transform Load) process.
What is the future of Data Warehouses and Data Lakes?
The crucial question is – will one of the two database methodologies overtake the other?
We don’t think so.
Data lakes are a fairly new concept and experts have predicted that it might cause the death of data warehouses and data marts.
Although with the increase of unstructured data, data lakes will become quite popular. But you will probably prefer keeping your structured data in a data warehouse.
Having said that, which one of the two you pick, depends on you.
Hence it is imperative that you ask yourself these questions before going ahead –
Am I using this tool to store a large amount of structured/unstructured data or for orderly information delivery?
Is my BI tool for just a few people or is it for the masses?
Do I need to control the query logic so that users get consistent results?
Make Data-Driven Decisions! Contact us.
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