Most companies work with more data than they actually realize. Up to a certain point this works fine, but as your company grows, so does your data stack, and likely also the number of sources generating this data.
From website visitor data, your sales dashboard, your customer service, all of them provide information about a specific part of your company. But how do you ensure that you can see all this information in relation to each other? And how do you make sure this data is stored in the right place?
In processing and storing data, Data lakes, Data warehouses, or a combination of both are used. The terms are often used interchangeably but are different in application, depending on the data and the purpose of storage and processing.
What is a Data warehouse?
A data warehouse is a central place where the data that comes from different sources is linked by, for example, creating a connector between an application and the data warehouse. This data is structured and cleaned up so that data from different sources corresponds to each other and can be brought into relation to each other. This is an ideal basis for creating analyses and reports, which you can make visual in a dashboard.
Common tools for a data warehouse are AWS Redshift, Google BigQuery, and Snowflake.
What is a Data Lake?
A data lake is designed to store data that does not have a specific structure or schema, usually in large quantities. Think, for example, of log files and data from social media channels. Interesting information from this raw data can then be structured and loaded into a data warehouse for analysis or dashboarding. In addition, the raw data can sometimes be used for machine learning or predictive models. These can draw more information from the data lake than from a data warehouse where the data is already filtered and structured.
Common tools for a data lake are AWS S3, Google Cloud Storage, and Azure Data Lake Storage.
What are the main differences between a data warehouse and a data lake?
While a data warehouse is all about structure and storing data with the purpose of doing something with that data, such as analyzing and reporting, a data lake is much more focused on storing large amounts of raw data. It’s common to use both in your company. Think of storing data first in a data lake, after which the data is moved to a data warehouse for analysis and connection with dashboards.
NucleusBI helps companies grow by creating insights into their data. We build the links between data sources, data lakes, and data warehouses and eventually enabling you to make decisions based on the right data through your dashboard. We process data according to the guidelines of the ISO 27001 standard, so you can assume that your data is secure.
Curious how we can help you grow your business, save time and energy, and get more return from your team? Contact one of our BI specialists!