Data lake or data warehouse? Choose the right one  

Data-driven decision-making is popular among businesses, but many struggle to turn insights into a sustained competitive advantage. The problem is that data is often difficult to access and use effectively. Data warehouses and data lakes allow the storage of large amounts of data. They are combined with business intelligence and analytics tools to help businesses make better decisions. Data warehouses and data lakes help organizations to make faster, timelier, and more accurate decisions. Let us find out the core difference between these two remarkable tools and how Sapper can help you to choose the right one.    

Define Data Warehouse    

A data warehouse is a repository of data used to make better decisions. This data comes from different sources, such as transactional systems, relational databases, and other sources, regularly. It is a large, centralized repository of carefully organized data. You can store data from different sources to produce reports and analyze data.   

The function of a Data Warehouse    

A data warehouse is a type of database that helps organizations collect, analyze, and store data in a standardized way. The function of a data warehouse can conduct in two ways,  i. Database operations, & ii. system transactions.   

  • Database operations    

Operational databases are examples of databases that help to operate day-to-day business operations. These databases include Microsoft SQL Server, AWS Dynamo, Apache Cassandra, and MongoDB.   

  • System transaction    

A database allows the storage of data in a transactional system. It is a type of software that executes transactions that produce structured data. A data warehouse is a type of software that uses a schema-on-write model, which means the core function of a data warehouse is to prepare and clean data before it is stored.   

Reason to choose a data warehouse?   

A data warehouse stores data ready for analysis and can be used by technical and non-technical users to create reports and dashboards. Reports and dashboards are usually automated and delivered periodically, so business users can access them as needed.   

Is data lake and warehouse complementary tools?   

Data lakes and data warehouses are complementary tools that help to store and analyze large volumes of data. A data warehouse designed for storing data from important businesses that make it easy to find and use. On the other hand, a data lake allows you to store vast amounts of unstructured data before it is processed and used in a data warehouse.   

Define data lake.   

A data lake is a repository that stores and processes a large amount of data in different formats. It is helpful for data discovery, advanced business intelligence, and machine learning.   

Process of the data lake    

A data lake is a data collection for accessing different applications without a data warehouse. It is structured data to make it easy to use. A data lake is a large storage area for data used for analytics. This data can come from different sources, including user, research data, video, media files, and application data. Before this data process for analytics, it should be standardized, cleaned, and organized.   

The benefit of the data lake   

A data lake is a valuable tool for businesses that needs to share large amounts of data with diverse individuals. These lakes provide many benefits, such as making data easily accessible and facilitating collaboration.   

Data reduction means that a data warehouse would only store valuable data. By breaking down data into different formats, data can be stored in a data lake and accessed by any business process. It eliminates the need for data to organize under one schema and makes data pipelines faster. In addition, data lakes are accessible to all stakeholders, which makes them more performance efficient.   

Reason to choose a data lake?   

Data analysts use data lakes for storing and analyzing large amounts of data. Data lakes consist of vast collections of data. They are used to find hidden patterns and correlations in the data. However, data lakes can be hard to search and questionable if poorly managed.   

Data warehouses and data lakes are crucial tools used in data integration, which is necessary for analytics. A data warehouse is the best repository for structured business data used in analytics. The simplest data integration approach Sapper recommends to most organizations is to use a data warehouse as a data repository. The data stack consists of various components that allow you to store, retrieve, and analyze your business data. Below is the process that needs to follow:  

  • Sources   
  • Data Pipeline   
  • Data warehouse   
  • Data Lake   
  • Business intelligence tool  

A business intelligence tool that accesses data in a data lake would likely be a custom solution built by a data engineer. This solution would have a higher price tag than a business intelligence tool that accesses data warehouses.   

On the other hand, a data lake is a central repository for both structured and unstructured data. However, it is not advisable to do this because it will slow down your business intelligence process. To get business insights quickly, you should use a custom solution that receives data directly from the lake.   

How does Sapper support data connectors?  

Data warehouses and lakes are capable of storing many data. However, it’s challenging for data integration to convey the information accurately. Transforming data from multiple sources can be complex and time-consuming, and in-house solutions are expensive to develop and maintain. You can use automation and outsourcing to bring in a provider specializing in real-time data movement to improve data management. Sapper can help you to do this process. Know more about how Sapper can simplify your data sync in real-time.    

To know more about Sapper, book your demo now!