Data is one of the most valued assets of any organization. It can help drive business decisions, optimize processes, improve customer experience, and create new opportunities. However, data alone is not enough. To unlock its full potential, data needs to be stored, processed, and analyzed in an efficient and scalable way. This is where a modern data warehouse comes in.
A data warehouse is a system that stores highly structured data from different sources. Data warehouses usually store current and historical data from one or more systems. The aim of using a data warehouse is to combine diverse data sources in order to analyze the data, search for insights, and generate business intelligence (BI) in the form of reports and dashboards.
However, not all data warehouses are created equal. Traditional data warehouses are often limited by their rigid structure, high cost, and poor performance. They may not be able to handle the increasing volume, variety, and velocity of data generated by modern applications and devices. Moreover, they may not be able to support advanced analytics such as machine learning and artificial intelligence (AI).
This is why many organizations are looking for alternatives to traditional data warehouses. One of the most popular options is a data lake.
Data Warehouse vs. Data Lake
A data lake is a storage repository intended to secure and store a large amount of all types of raw data. The data can be structured, semi-structured, and unstructured. Once it’s in the data lake, the data can be used in machine learning or artificial intelligence (AI) algorithms and models for business purposes. It can also be transferred to a data warehouse after processing. Data lakes vs data warehouses have some significant differences:
- Data type: A data lake can store raw (all types, no matter source or structure) data, while a data warehouse can only store processed (data stored according to metrics and attributes) data.
- Data purpose: A data lake can store data for future use cases that are not yet defined, while a data warehouse can only store data for current use cases that are already known.
- Process: A data lake follows an extract-load-transform (ELT) process, where the data is loaded first and then transformed as needed. A data warehouse follows an extract-transform-load (ETL) process, where the data is transformed first and then loaded.
- Schema: A data lake has a schema-on-read approach, where the schema is applied after the data is stored. This offers more flexibility and agility for capturing new types of data. A data warehouse has a schema-on-write approach, where the schema is applied before the data is stored. This offers more security and performance for querying specific types of data.
- Users: A data lake is mainly used by data scientists and engineers who need in-depth analysis and tools (such as predictive modeling) to understand the data. A data warehouse is mainly used by business professionals who need operational reports and dashboards to make decisions.
Both data lakes and data warehouses have their own advantages and disadvantages. Depending on the use case, some organizations may choose to use only one of them, while others may choose to use both of them in a hybrid architecture.
Components of Data Warehouse
A modern data warehouse consists of several components that work together to provide a reliable, scalable, and secure solution for storing and analyzing data. The components of data warehouse include:
- Data sources: These are the systems that generate or collect the original data, such as databases, applications, web servers, sensors, etc.
- Data integration: This is the process of extracting, transforming, and loading (ETL) the data from different sources into a common format and structure that can be stored in the data warehouse.
- Data storage: This is the component that stores the processed and structured data in a centralized location that can be accessed by various users and applications.
- Data processing: This is the component that performs various operations on the stored data, such as aggregation, filtering, sorting, joining, etc., to prepare it for analysis.
- Data analysis: This is the component that provides tools and techniques for querying, exploring, visualizing, and reporting on the processed data to generate insights and intelligence.
- Data governance: This is the component that ensures the quality, security, privacy, compliance, and availability of the data throughout its lifecycle.
Best Practices for Building a Modern Data Warehouse
Building a modern data warehouse needs careful planning and execution. Here are some best practices to follow:
- Define your business objectives and requirements
- Choose the right architecture and platform
- Design your data model and schema
- Implement your data integration and processing pipelines
- Enable your data analysis and reporting capabilities
- Monitor and maintain your data warehouse
Conclusion
A modern data warehouse is a powerful solution for storing and analyzing large amounts of structured data from various sources. It can help you gain insights and intelligence that can improve your business outcomes. However, building a modern data warehouse is not a trivial task. It requires careful planning, design, implementation, and maintenance. By following the best practices outlined in this article, you can build a modern data warehouse that meets your needs and expectations.
