Most enterprises today have a data warehouse in place that is accessed by a variety of BI tools to aid the decision making process. These have been in use since several decades now and served the enterprise data requirements quite well.
However, as the volume and types of data being collected expands, there’s also a lot more that can be done with it. Most of these are use cases that an enterprise might not even have identified yet. And they won’t be able to do that until they have had a chance to actually play around with the data.
That is where the data lake makes an entrance.
We took a brief look at the difference between a data warehouse and lake when defining what is a data lake. So in this blog, we’ll dig a little deeper into the data lake vs data warehouse aspect, and try to understand if it’s a case of the new replacing the old or if the two are actually complementary.
Data lake vs. Data Warehouse
The data warehouse and data lake differ on 3 key aspects:
A data warehouse is much like an actual warehouse in terms of how data is stored. Everything is neatly labelled and categorized and stored in a particular order. Similarly, enterprise data is first processed and converted into a particular format before being accepted into the data warehouse. Also, the data comes in only from a select number of sources, and powers only a set of predetermined applications.
On the other hand, a data lake is a vast and flexible repository where raw, unprocessed data can be stored. The data is mostly in unstructured or semi-structured format with the potential to be used by any existing business application, or ones that an enterprise could think of in the future.
The difference in data structure also translates into a critical cost advantage for the data lake. Cleaning and processing raw data to apply a particular schema on it is a time consuming process. And changing this schema at a later date is also laborious and expensive. But because the data lakes do not require a schema to be applied before ingesting the data, they can hold a larger quantity and wider variety of data, at a fraction of the cost of data warehouses.
Data warehouses demand structured data because how that data is going to be used is already defined. As the cleaning and processing of data is already expensive, the aim with data warehouses is to be as efficient with storage space as possible. So the purpose of every piece of data is known, with regards to what will be delivered to which business applications. That ensures that space is optimized to the maximum.
The purpose of the data flowing into the data lake is not determined. It’s a place to collect and hold the data, and where and how it will be used is decided later on. It usually depends on how that data is being explored and experimented with, and the requirements that arise with innovations within the enterprise.
Data lakes are overall more accessible as compared to data warehouses. Data in a data lake can be easily accessed and changed because it’s stored in the raw format. On the other hand, data existing in the data warehouse takes a lot of time and effort to be transformed into a different format. Data manipulation is also expensive in this case.
Does the data lake replace the data warehouse?
No. A data lake does not replace the data warehouse, but rather complements it.
The organized storage of information in data warehouses makes it very easy to get answers to predictable questions. When you know that business stakeholders need certain pieces of information, or analyze specific data sets or metrics regularly, the data warehouse is sufficient. It is built to ingest data in the schema that will quickly give the required answers. For example: revenue, sales in a particular region, YoY increase in sales, business performance trends - all can be handled by the data warehouse.
But as enterprises begin to collect more types of data, and want to explore more possibilities from it, the data lake becomes a crucial addition.
As discussed, schema is applied to the data after it’s loaded into the data lake. This is usually done at the point when the data is about to be used for a particular purpose. How the data fits into a particular use case determines what schema will be projected onto it. This means that data, once loaded, can be used for a variety of purposes, and across different business applications.
This flexibility makes it possible for data scientists to experiment with the data to figure out what it can be leveraged for. They can set up quick models to parse through the data, identify patterns, evaluate the potential business opportunities. The metadata created and stored alongside the raw data makes it possible to try out different schemas, view the data in different structured formats, to discover which ones are valuable to the enterprise.
Given these characteristics of the data lake, it can augment a data warehouse in a few different ways:
- Start exploring the potential of the data you collect, beyond the structured capabilities of your current data warehouse. This could be around new products and services you can create with these data assets, or even enhance your current processes. For example: leverage data lake to gather information of site visitors and use that to drive more personalized buyer journeys and evolving marketing strategies.
- Use the data lake as a preparatory environment to process large data sets before feeding them into your data warehouse
- Easily work with streaming data, as the data lake is not limited to batch-based periodic updates.
The bottomline is, the data warehouse continues to be a key part of the enterprise data architecture. It keeps your BI tools running and allows different stakeholders to quickly access the data they need.
But the data lake implementation further strengthens your business because:
- You have access to a greater amount of data that can be stored for use, irrespective of its structure or quality
- Storage is cost effective because it eliminates the need for processing the data before storage
- Data can be used for a larger variety of purposes without having to bear the cost of restructuring it into different formats
- The flexibility to run the data through different models and applications makes it easier and faster to identify new use cases
In a market where the ability to leverage data in novel ways offers a critical competitive advantage, the focus should no longer be on data lake vs data warehouses. If enterprises want to stay ahead, they will have to realise the complementary functions of the data warehouse and the lake, and work towards a model that gets the best out of both.
Interested in exploring how a data lake fits into your enterprise infrastructure? Talk to our expert team, and let’s find out how Srijan can help.