Today, businesses use multiple systems and software to manage decision-making processes more consciously, to provide better quality service to customers and to be successful. This software causes businesses to collect, transform, store and use more data than ever before. However, businesses need to be able to use this data in the best way. For this, they need to choose the right architecture for the data storage layer. Data warehouse, data lake and data lake house are different cloud storage options. In this blog post, we will discuss different data storage techniques that you can evaluate according to your company’s needs.
What is a Data Warehouse?
A data warehouse is a storage unit and data processing centre developed to store large amounts of data from multiple sources within a company. Teams using data warehouses often use SQL queries for analysis.
Data warehouses extract, transform and clean data from multiple sources. It then transfers this data to storage. Thus, it offers a core reporting and business analytics component by providing a single source of data truth.
These features of the data warehouse make it the most logical data storage option for data platforms whose intended use is data analysis and reporting. With extensive SQL support and predefined functions, data warehouses are designed to provide fast and actionable querying for data analytics teams working with structured data. These systems are specially developed to accelerate analytical processes.
Advantages of Data Warehouse
Data warehousing provides many advantages to a company. Some of them are as follows:
- Data Consistency and Data Quality
Businesses obtain data from many sources such as applications and software. The data warehouse transforms corporate data into a single source of truth, allowing data to be used in a consistent and standardised format. Thus, by improving the quality and consistency of data, it enables businesses to rely on their data for their business needs.
- Advanced Business Intelligence
Data warehouses act as a bridge between the voluminous raw data obtained automatically as required by the application and the organised data that offers insight. Thus, it helps businesses to carry out business processes more consciously and make more accurate decisions.
- Informed Decision Making
The data warehouse provides a single, accurate repository for historical and current data. In this way, teams make much more accurate and informed decisions by utilising the right data.
- Efficiency and Speed
The data of the data warehouse is consistent and accurate. This makes it easy to connect to data analytics and business intelligence tools. Thanks to data warehouses, data collection time is shortened. Thus, teams can accurately utilise data for their analytical needs.
Disadvantages of Data Warehousing
- Data warehouses perform well with structured data. They do not perform well with semi-structured or unstructured data formats.
- Implementation and maintenance of the data warehouse may require high costs.
What is a Data Lake?
A data lake has the ability to store large amounts of structured and unstructured data in raw and unformatted form. This highly flexible and cost-effective storage option helps businesses understand unstructured data and extract information. For example, it enables better analysis of various types of data from social media platforms, IoT devices or different data sources with tools such as machine learning.
Advantages of Data Lake
Data lakes provide some advantages to businesses as they have the ability to store structured and unstructured data:
- Data Consolidation
The data lake eliminates the need to store different structured and unstructured data formats in different environments. It stores all corporate data of businesses in one place.
- Data Flexibility
Data lakes are flexible. This makes it possible to store data in any format, without a predefined schema as in a data warehouse.
- Cost Savings
A data lake is more affordable than a data warehouse. Typically, it is priced per GB stored.
- Deep Understanding of Data
Data in data lakes is stored in its raw form. This helps to apply machine and deep learning methods to gain a deeper understanding of the data.
Disadvantages of a Data Lake
- If data lakes are not managed properly, the data becomes disorganised, making it difficult to connect tools such as business intelligence and analytics.
- Data lakes contain different data formats. This makes it difficult to implement appropriate data security and management policies for sensitive data types.
What is a Data Lake House?
A data lake house is a combination of a data warehouse and a data lake. A data lake house integrates the characteristics of both data warehouses and data lakes. Thus, it combines traditional data analytics technologies with capabilities such as machine learning. In other words, a data lake house provides a single storage space for all structured, semi-structured and unstructured data, as well as business intelligence, streaming capabilities and machine learning capabilities.
Advantages of Data Lake House
The data lake house architecture incorporates the features of both a data warehouse and a data lake. In this way, it offers many benefits to businesses:
- Reduced Data Redundancy
Data lake houses minimise data duplication by providing a comprehensive data storage platform to meet business data needs. Due to the advantages provided by data warehouses and data lakes, many businesses favour hybrid solutions. However, this strategy can result in data duplication, which can increase costs.
- Cost Optimisation
Data lake house provides low-cost, flexible and fast storage options.
- Support for Various Workloads
The data lake house provides direct access to some business intelligence tools for data analytics and machine learning workloads. Python uses open data formats with various machine learning libraries such as APIs. In this way, it greatly facilitates teams to use data.
Data Warehouse, Data Lake, Data Lake House: Which One Best Suits Your Business Needs?
- Data Warehouse
Suitable for businesses looking for a storage option that includes data warehousing, business intelligence and data analytics use cases and works well with structured data.
- Data Lake
It is a good choice for businesses that want a cost-effective and flexible data storage solution for managing AI and machine learning workloads on semi-structured and unstructured data.
- Data Lake House
Enterprises that want to store structured, semi-structured and unstructured data and implement machine learning and advanced analytics workloads should choose a data lake house.
