In today's data-dependent world, companies always use millions of streams of data as the basis of business decisions. How do companies store and process this data? This is the exact place where data lakes and data warehouses come into action. Both have formed an essential part of managing data, and there is a very fine line when it comes to their purposes. Read on in the blog as we discuss the difference between data lakes and data warehouses and help you decide which of them best suits your usage.
What is a Data Lake?
A data lake is a centralized repository holding the raw and unprocessed native-form data. This data lake can hold voluminous volumes of structured, semi-structured, and unstructured data. This is quite highly scalable and, in most cases, highly cost-effective, making it quite suitable for any organization handling big data. It also provides the option of storing data without first organizing or structuring it.
Data is stored raw in a data lake. The type of data varies, from text files and images to videos and logs. Data isn't pre-processed. Hence, the flexibility that is associated with its use is immense. The companies can then use advanced analytics on this raw data to dig up insights hidden from them so far.
What is a Data Warehouse?
A data warehouse is much more structured storage. A data warehouse contains structured data that have been cleansed, transformed, and organized. Data mostly come from operational systems and are also inserted into the warehouse for reporting and analysis.
Data warehouses are designed for speed and efficiency. The optimized area of data warehouses lies in complex queries and reporting. Therefore, the main application of BI concerns business intelligence. Additionally, the structured data is dealt with in a data warehouse, but a data warehouse avoids unstructured data such as images or videos. Data lakes do not have any such restrictions.
Key Differences Between Data Lakes and Data Warehouses
Knowing the difference between a data lake and a data warehouse will help decide which one best fulfils needs. But let's take a closer look at some of the key differences.
1. Data Structure
Though the two systems, data lakes and data warehouses, handle data in a quite different manner, it is that difference that will decide which of these products is more flexible and efficient for various purposes. A data lake holds raw, unprocessed data; whereas a data warehouse holds well-structured and organized data.
2. Data Types
Data lakes will support structured, semi-structured, and unstructured data. It will be a place for organizations with diverse sources of data. A data warehouse, however, will support only structured data. In many cases, such data come in the form of tables and columns.
3. Processing
Data lakes allow processing at the time of analysis. Data warehouses need to be cleaned and transformed data before loading them, which makes them much more rigid. So, if you need flexibility, a data lake may be a better option.
4. Use Cases
Data lakes are more apt for big data analytics, machine learning, and predictive analytics. They help the data scientists experiment and explore raw data. In contrast, data warehouses are better suited for business intelligence, reporting, and dashboards.
Which One Do You Need?
The choice between a data lake and a data warehouse comes down to if that is exactly what your organization needs. For example, when you have huge chunks of raw, unstructured data and are in dire need of flexibility, then the direction would be the data lake way. But in general, if you want insight generation with more structured data and reporting, then the logical move is to create a data warehouse.
Furthermore, businesses might sometimes need both of them. For example, an organization might store raw data in a data lake and derive structured data from it for usage in reporting, which will reside in a data warehouse. A hybrid like such is becoming common as more and more organizations wish to exploit the best of the two systems.
The Rising Demand for Data Skills
Businesses have increasingly been depending on data in making decisions, and the management, analytics, and science of data are currently at an all-time high, so if you are interested in pursuing a career in the field, then doing a data analytics course in Indore will do the trick, as proper training will help acquire the necessary skills to work on both data lakes and data warehouses.
This further extends into machine learning and big data knowledge if someone wants advanced training. A placement training institutes in Indore, such as IOTA Academy, has courses to deal with huge amounts of data, handling big datasets, and effective utilization of analytics tools. Therefore, if an individual is following the right form of training then it can indeed be of prime help to every individual moving on in this incredibly fast-growing zone of data science.
Conclusion
Data lakes and data warehouses differ in their uses in data management. A data lake is essentially a raw and unstructured data store, which has flexibility and scale. However, a data warehouse is meant for structured data, with a focus on efficient reporting. Knowing this difference will let you choose the right solution for your needs. Take your skills in data science and analytics to the best it training institute in Indore.
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