#datawarehouse

Key differences between database, data warehouse, data mart and data lake

In order to make the right decision when choosing a data organization system, it is advisable to conduct a comparative analysis.

Key differences between databases and data warehouse:

Data Warehouse

Database

Key differences between data mart and data warehouse

Data Mart

Data Warehouse

Key differences between data lake and data mart

Data Lake

Data Mart

Key differences between data lake and data warehouse

Data Warehouse

Data Lake

Key differences between databases and data mart

Database

Data Mart

Key differences between databases and data lake

Database

Data Lake

Database, Data Warehouse, Data Mart, Data Lake: main characteristics and differences

Modern organizations process data daily. However, the data may differ in type, scope and manner of use. This must be considered when choosing the best data solution. Achieving results depends, among other things, on the selected enterprise data management system, that must fully meet business needs. It can be data mart, data warehouse, database or data lake.

Database

A database is a place of related data storage that is used to capture a particular situation. For example, a point-of-sale (POS) database. In this case, the database collects, and stores data related to retail store transactions. Data entering the database is processed, systematized, managed, updated, and then stored in tables. The database is the target storage for raw transactional data and performs online transaction processing (OLTP).

The main database characteristics:

Data warehouse

The data warehouse is the main analytical system of the company. It often works in conjunction with an operational data warehouse (ODS) to store data that has been retrieved from various company databases. For example, a company has databases supporting points of sale, online activity, customer and employee information. The data warehouse will take the data from these sources and make it available in one place. The method of extracting data from the database, converting it to ODS, and loading it into the data warehouse is an example of ETL and ELT processes.

The data warehouse is an excellent tool for data analysis due to the capture of transformed historical data. Business departments are involved in organizing data, using it for reporting and data analysis. The data warehouse uses SQL to query data, and use tables, indexes, keys, views, and data types to organize and ensure data integrity.

The main data warehouse features:

Data mart

Like a data warehouse, a data mart maintains and stores processed and ready for analysis data. However, a visibility scope is limited. The data mart provides the subject data that is required to support each business unit. For example, a data mart to support reports and analysis of a marketing department. By defining data boundaries within a particular department, only relevant data is available.

Using a data mart increases security level. Visibility restriction prevents irresponsible use of data that is not relevant to a particular department. It should also be noted that less data in the data mart increases the speed of processing, and therefore increases the speed of query execution. Data is aggregated and prepared for a specific department, minimizing data misuse and the possibility of conflicting reports.

Key data mart features:

Data lake

A data lake is designed to store structured and unstructured company data. It collects all the valuable data for later use: images, pdfs, videos, etc. Just like a data warehouse, a lake extracts data from several disparate sources and processes it. It can also be used for data analysis and reporting purposes. For processing and analysis, different applications and technologies (for example, Java) are used. Data lakes are often used in conjunction with machine learning. Machine learning test results are also stored in data lake. The level of usage complexity requires serious skills from users, as well as experience with programming languages ​​and data processing methods. Data cleaning occurs without ODS usage.

Key data lake features:

Storage is the key element of data-driven process

Data analytics is an indispensable modern business tool. Data understanding and its analytics provide comprehensive answers to the business on how to set up processes for maximum benefit, who is the main business customer and what he needs, what «gaps» exist in activities. In addition, companies use artificial intelligence to offer products and services to the «right» people. The ability to increase business processes efficiency is provided by robots and automation.

All these developments are based on an ever-increasing stream of data that is collected, stored and analyzed. Using data, some companies have revolutionized new services to improve and simplify human life: search engines, communications, e-commerce, booking systems and more.

However, most of the companies did not achieve such success. The reason is that companies  struggle to manage their data and most of it is not used. Accordingly, monetization does not occur.

The first block in data management is storage. An incorrect strategy to overcome this task, or its complete absence, may lead to other problems in the future. Data volume is constantly growing, so companies need to clearly understand what data is important.

At the moment, cloud services offer to store almost unlimited information amounts. However, there are certain difficulties here. For example, data with a high level of confidentiality or regulatory burden cannot be hosted outside the enterprise; some data requires instant access from anywhere in the world; some data requires routine archiving. Also, there is a need for data auditing to determine data relevance and compliance with international regulations. To perform these functions smoothly, it’s necessary to know where the data is located, how many copies exist, and how to access it.

The process of obtaining reliable data can be achieved with fast and highly available storage systems. Modern business analytics involves moving and sorting large data amounts to provide business users and customers with flexible functionality. Including the system must be supported by encryption and security.

Intelligent data warehouse

To achieve maximum speed, stability and security, modern storage systems, including the IBM FlashSystem, use solid-state non-volatile media. Artificial intelligence technologies usage enables intelligent management of data storage and access, which can increase speed and minimize the likelihood of errors and data loss. So, the data with the most frequent predictable access will be ready for work and will be queued.

One of the key requirements for a storage system is resilience. The goal of many businesses today is to build analytics-driven internal processes and customer relationships. In such model, it is impossible to allow these processes to stop as a result of problems with the data flow or infrastructure. Modern storage systems offer the ability to quickly copy and replicate corporate data. Ensuring data integrity is often critical. In this case, 2 or more identical copies of data can be synchronized in different places, and in a situation of an unforeseen failure, it can be restored with practically no data loss.

Rapidly changing data management

The UK Met Office has tackled the challenge of implementing the infrastructure needed to handle rapidly changing data. The information is used to determine weather changes, climate research, and seasonal trends. To do this, 300 million weather-related data points are collected, analyzed daily, and made available to customers. This happens twice to eliminate the risk of interrupting the data flow.

To support this process, a hybrid cloud strategy was developed based on the IBM FlashSystem. The storage provides a high level of compression, which is a cost-effective solution. It also contributes to the creation of a high-performance data infrastructure, which is necessary to transfer information from internal servers to the public cloud and clients.

Another example is the Archdiocese of Salzburg, which needed a solution to provide services more efficiently (support, outreach to the community and parishioners, access to many historical documents and literature). The Archdiocese was able to increase response times 10 to 20 times by moving away from mechanical disk storage in favor of solid-state, non-volatile systems.

Data is an important part of business assets. All data decisions must be smart and effective. The storage process should be considered as a key element of the data management strategy, along with the collection and analytics processes.

Data Lake and Data Warehouse merging

Another one trend is data lake and data warehouse combine that promotes data stack simplification. Until recent times data lake and data warehouse subsist separately. Both objects are intended to data holding. But they are not synonymous and there is a principal difference between them.

The first object is a repository for a big volume of raw data in its original form from different sources. Data can be of different types: structured, semi-structured and unstructured. Data lake is characterized by high data flexibility and availability and a big choice oh machine learning usage.

The second object is also a repository for a big volume of data. But in this case data runs processing and gets into the storage already structured strictly regulated ways. Data warehouse is characterized by less flexibility, fixed configuration and transactional analytics and BI support.

Wishing to get the best of both sides, organizations try to combine 2 variants. As a result, they have both data lake and data warehouse (sometimes several with many parallel pipelines). Today’s data storage solution providers offer more such possibilities. For example, Snowflake – its platform allows to connect data warehouse and data lake; Microsoft Synapse – its cloud warehouse has integrated capabilities of data lake.

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