In the world of big data, there are many good reasons why to use a data lake. After all, where else can you store both raw and organized data until you need it? In a world bombarded by information, a data lake is a great thing to have.
That’s something important to keep in mind as you are developing a data strategy for your organization. As you build this strategy, it’s also important to understand the difference between a data lake and data warehouse. The operative word is structured.
A cloud data warehouse stores only structured data in a standard format. Structured data is easily analyzed and accessed frequently, as opposed to raw data, which must be collected and processed before it can be stored in a warehouse. Data warehouses store only data that has been processed to use for specific purposes.
A data lake, on the other hand, accepts data in all formats. Raw, unstructured files, semi-structured ones and structured data. Some data stored in your lake might have a purpose in the future. Other data remains there for storage until a need arises.
A newer concept, data lakes can better address the needs of the big data world thanks to their “store-everything” approach and ability to handle heavy volumes of different data types. This data might not yet have a determined purpose or schema, but it still holds the key to valuable insights and analysis.
Why Should I Use a Data Lake?
When developing a data strategy, it’s worth diving deeper into the data lake concept. While data warehouses are great for many organizations, if you aren’t sure how you’ll be using your organization’s data, you should take a closer look at a data lake, especially if you need big-batch storage and processing.
It’s cost-effective
Because a data lake’s content is unstructured, storage costs are low compared to a data warehouse. Storage in a data warehouse requires time and effort to identify pertinent data, creating a data model and identifying the database structures and programs to use the data for a specific purpose. It’s costly to do this. Raw data is cheaper to store and extract as needed from a data lake.
It’s flexible
Thanks to its “store-everything” approach that keeps information out of silos, a data lake gives users scalable storage that can easily adapt to growing amounts of data of all types. It can accommodate everything from .xml files to multimedia, binary data to logs, to mention only a few kinds of data you can drop into your lake. It even will accommodate high-speed data.
All of it is stored in its native format and is easy to access and refine as needed. You can crawl, catalog and index all of it. Users can access data through dashboards, mobile apps and more.
It can surface valuable insights
Flexible data plays a big role in analytics. Data lakes hold a large amount of data from many different resources. While a data lake might seem like it’s a murky swamp full of data bits and pieces, it’s actually a powerful tool for analytics and forecasting because the data is easier to access and there is more of it, and when you want to gain deeper insights from your data the data lake makes creating a purpose-built data warehouse and analytics layer much easier.
It plays nicely with machine learning
Machine learning has a growing place in today’s business world, driving predictive insights. A data lake is a must-have tool when it comes to using machine learning to do predictive forecasting. Data lakes leverage a cloud platform’s machine-learning capabilities by providing easy access to all of your organization’s data so you can effortlessly create data sets to better your machine-learning models. This yields more intelligent, accurate results and decisions.
We want to be sure you understand how to most efficiently and effectively use your data. Be sure to check out our other data blogs:
5 Important Steps in Developing a Data Strategy
What’s the Difference Between a Data Lake and Data Warehouse
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