Successful organizations not only collect, but derive valuable insights from their data. One of the first steps towards a successful big data strategy is choosing the underlying technology of how data will be stored, searched, analyzed, and reported on. Here, we’ll cover common questions – what a database, data lake, or data warehouse is, how it works, and which to choose based on your data strategy, infrastructure, and business goals.
A database is used to store, search and report on structured data from a single source. They are the simplest to create and SQL can be used to query and report on the data. There are both open source and proprietary databases, making it widely accessible to install and start using on premium or on the cloud.
A relational database require schemas and are not a fit for unstructured or semi-structured data. Because of this rigid schema, they are not suited to be the centralized place to store data from multiple sources where the raw data varies in format and structure. However, they are popular for data analysis and monolithic applications.
A data warehouse is used to store large amounts of structured data from multiple sources in a centralized place. Organizations invest in building data warehouses because of its ability to deliver business insights from across the company, and quickly.
The process of creating a data warehouse requires some heavy lifting in the planning and design stage of examining data structures. Data warehouses are preferred by the business and operations decision makers of the company and a good system justifies its often high costs in proprietary software and storage.
A data lake stores structured, semi-structured and unstructured data, supporting the ability to store raw data from all sources without the need to process or transform it at that time.
Only when the data needs to be retrieved, will some structure need to be applied, which is ideal in the hands of data scientists and data analysis developers who can create new data models on the fly but does not provide the same reporting capabilities and ease of use for business users. Storing data in data lakes is much cheaper than in a data warehouse. Data lakes are very popular in the modern stack because of its flexibility and costs but they are not a replacement for data warehouses or relational databases.
The key differences in selecting how to house all the data in an organization comes down to these considerations:
Data lakes and data warehouses are used in organizations to aggregate multiple sources of data, but vary in its users and optimizations.
Think of a data lake as where streams and rivers of data from various sources meet. All data is allowed, no matter if it is structured or unstructured and no processing is done to the data until after it is in the data lake. It is highly attractive to data scientists, applications that are leveraging the data for AI/ML where new ways of using the data are possible.
A data warehouse is a centralized place for structured data to be analyzed for specific purposes related to business insights. The requirements for reporting is known ahead of time during the planning and design of a data warehouse and the ETL process.
It is best suited for data sources that can be extracted using a batch process and reports that deliver high value to the business.
Another way to think about it is that data lakes are schema-less and more flexible to store relational data from business applications as well as non-relational logs from servers, and places like social media. By contrast, data warehouses rely on a schema and only accept relational data.
Data warehouses and databases both store structured data, but were built for differences in scale and number of sources.
A database thrives in a monolithic environment where the data is being generated by one application. A data warehouse is also relational, and is built to support large volumes of data from across all departments of an organization.
Both support powerful querying languages and reporting capabilities and is used by primarily the business members of an organization.
Typically an organization will require a data lake, data warehouse and database(s) for different use cases.
All three focus on centralizing data into a place to sit and enable different parts of the business to analyze and uncover insights.
In fact, today, there are modernized tools that help integrate various types of data and architectures together so regardless where your data sits, you can connect the dots across your entire organization.
They extend data between data warehouses and data lakes and vice versa, supporting data science analysis and a shift from an extremely large passive data lake, to actioning real-time data for massive scale.
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