Successful organizations continue to derive business value 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 is a database, data lake, or data warehouses? What are major differences between them, and how do they work? We’ll also cover which to choose based on your current 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:
Structured vs Unstructured vs Semi Structured Data: How many data sources, what format the data comes in, how predictable or consistent or known is the structure ahead of time are important considerations. Data lakes accept unstructured data while Data warehouses only accept structured data from multiple sources. Databases perform best when there’s a single source of structured data and have limitations at scale.
Data Processing Requirements: Included in the data management strategy is the process of understanding what the data model is and when it needs to be defined. Data lakes offer the flexibility of storing raw data, including all the meta data and a schema can be applied when extracting the data to be analyzed. Databases and Data Warehouses require ETL processes where the raw data is transformed into a pre-determined
structure, also known as, schema-on-write.
Data Storage and Budget Constraints: Big data offers business value to organizations which is hopefully reflected in the budgets for its data management plan. As data continues to increase in volume and velocity, storage costs increase accordingly. Data lakes are the most efficient in costs as it is stored in its raw form where as data warehouses take up much more storage when processing and preparing the data to be stored for analysis. Databases can scale up and down depending on the need.
Consider Who is Using the Data: Whether or not the end user is a business analyst, a data scientist or a business operations will determine what makes sense for the organization. If the primary use case is business insights and reporting for the operations team, a data warehouse will meet their needs but at a higher cost to set up and store the data. The data scientist may prefer data lakes because he/she wants to deep dive into new artificial intelligence and machine learning algorithms and appreciate access to a mix of structured and unstructured data. A business analyst might be proficient in SQL and only need to create a trends report on one part of the business and thus, a relational database is best.
Technology & Data Ecosystem: Organizations differ on their stance on trusting open source software or proprietary software and the community behind it. Data lakes are popular because of the widespread adoption of Hadoop and the rise in unstructured data from various systems used across the company and real-time data streams. Another aspect of technology to consider is the accessibility and fidelity of updating the system when data sources and structures change. It is more costly to update the relational database and data warehouse where as changes are simple with a data lake.
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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