In today's data-driven world, businesses produce colossal amounts of data and desire to thoughtfully use it to acquire customer and business insights that will help them gain a competitive advantage. Data that has been collected often comes from a variety of source systems in order to better understand the dynamics of the core business, consumers, and market. Due to the ever-growing volumes of data, businesses constantly need to come up with new and more effective ways to combine and transform various sources of information in order to make better and more informed decisions. The two most common solutions for storing large amounts of data are data lakes and data warehouses. In this blog post, we'll examine data lakes vs. data warehouses in a more detailed manner.
Data Lake: Explained!
A data lake is a massive storage repository or facility where a substantial volume of structured, semi-structured, and unstructured data is stored in its unprocessed or raw form. With no set restrictions on account size or the size of the file, the data lake offers a place to store all sorts of data in their original formats. In data lakes, the data is stored using a flat architecture method, allowing users to query or process data whenever it is necessary. A data lake offers a practical solution for businesses that need to gather and retain colossal amounts of data but do not always need to process, manage, and analyse it all at once.
Characteristics of the Data Lake
1. Centralization
One of the fundamental concepts of a data lake design is the centralised storage of segregated data. It is simpler to control and administer because of this centralization, and it also makes it easier to innovate or experiment using different data sets without disturbing the status quo.
2. Collect, store, and manage data at any scale.
Any size of data collection and storage is possible with data lakes. Nowadays, cloud-based object storage services offer almost infinite space at extremely low prices in terms of the three V's of big data. If necessary, with data lakes, data for your company can be gathered quickly and efficiently using streaming technology in real-time. These platforms also handle the challenging work of revealing value in unstructured data, including the automated transcription of audio recordings, thereby supporting a variety of contemporary data sources.
3. Data Traceability
In a data lake, all of an organisation's or enterprise's data is stored and managed throughout every stage of its lifecycle—from data definition and access through storage and processing to analytics and application.
Data Lakes: Use Cases
1. Machine learning
With the use of data lakes, businesses will be able to provide a variety of insights using data, such as historical data reporting and machine learning. These ML models are further used to predict outcomes and recommend a number of recommended actions to get the best conclusion.
2. Business Intelligence
With data lakes, you can significantly increase the speed of reports, dashboards, and ad hoc inquiries. You can also utilise current BI tools on top of data lakes without sacrificing performance or the quality of the data.
3. Analytics
Data lakes make it possible for different roles inside your organisation, such as data scientists and data engineers, to access data using their preferred analytics tools and platforms. You can use data lakes to seamlessly implement BI and analytics without migrating your data to a different analytics solution.
4. Data indexing and cataloguing
Data lakes let you store both relational and non-relational data, including data from social media, IoT devices, operational databases, and line-of-business applications. Through data crawling, manipulation, cataloguing, and indexing, a data lake also enables you to know what data exists within the lake.
Data Warehouse: Explained
A data warehouse is a centralised repository that enables the storage, analysis, and interpretation of filtered and organised data in order to assist in improved decision-making. Data warehouses frequently receive data from relational databases, transactional systems, and other sources. Furthermore, a data warehouse is intended to facilitate and promote business intelligence (BI) activities, particularly analytics. The data present in data warehouses is accessible to data engineers, data scientists, and decision-makers via business intelligence (BI) applications, SQL clients, as well as other advanced analytics applications.
Characteristics of Data Warehouses
1. Subject-Oriented
In order to satisfy the business needs of individual department-specific users, data warehouses are subject-oriented databases that often give information on a topic such as sales inventories or supply chains rather than overall business operations. In other words, the data in a subject-oriented data warehouse is organised based on user-defined themes, where a topic is a group of linked facts that pertain to a particular business user.
2. Integration
The integration capability of data warehouses brings together information from several sources into one central warehouse. Any department within a business can access the data warehouse, and the data can be easily organised into spreadsheets or tables for analysis and research purposes. Additionally, it has the ability to link with other corporate software and phone systems, giving employees direct access to information without switching between programs.
3. Time Variant
Data is maintained in data warehouses at various time intervals, including weekly, monthly, and yearly. The historical perspective that the data warehouse offers is one of its key characteristics. According to the factors of time, it keeps the enormous volume of data from all database sources saved. In other words, a data warehouse is a time-variant database that helps business managers analyse the company's operations and compare them to those of other time periods, such as the year, quarter, month, week, and date.
4. Non-volatile
The data present in a data warehouse is non-volatile in nature. It means that the previous data is not altered or changed once the new data is fed. Since it is non-volatile, the data is read-only, and users can refresh the data at predetermined intervals. This aids in the analysis of historical data and the comprehension of historical events.
Data Warehouse: Use Cases
1. Strategic reporting
The storage of data for reporting needs is excellent with data warehouses. They are ideal for ad-hoc reporting since they are optimised for high-performance queries. Data warehouses are frequently used to combine data from several source systems to acquire a comprehensive picture of the data or to determine how specific elements are influencing various facets.
2. Performance Evaluation
Data warehouses can be used to assess group performance throughout the company. Users can delve deeper into the team data to build custom dashboards or reports that display the performance of the team in relation to particular criteria. Customer support, sales, and marketing teams can each be evaluated using metrics produced from the data warehouse, such as usage trends, lifetime value of customers, and acquisition sources.
3. Natural Language Processing
Several firms frequently employ NLP to improve their customer service since it provides quick data analysis and gives opportunities for growth in support, sales, and marketing. Huge amounts of structured and unstructured data can be stored in a data warehouse and then evaluated using NLP tools. When these insights are analysed, company staff or automated responses—such as live chat support or suggestions based on prior experiences with customers—are provided in real-time.
Data Lake vs. Data Warehouse: A Succinct Comparison
1. Nature of the Data
Data lakes gather information from many sources in its original or raw form and make it accessible for any potential future use. In contrast to this, data warehouses contain structured and semi-structured data that has been cleaned, pre-processed, and is available for strategic analysis based on predetermined business needs.
Due to their less organised and unfiltered nature, data lakes often need substantially more storage space than data warehouses. Additionally, unprocessed raw data is pliable and suitable for machine learning. It may be easily evaluated for any purpose. However, the risk of all that unstructured data is that, in the absence of adequate data quality and data governance mechanisms, data lakes might occasionally turn into data swamps. In contrast, data warehouses only hold processed data, so each piece of information there has already been put to use within the company. This implies that data that might never be needed is not wasting storage space.
2. Purpose
Data lakes offer inexpensive storage for massive amounts of information from numerous sources. By allowing data with any structure, costs are reduced using data lakes since the data is more adaptable and scalable and is not required to fit into a certain schema. However, structured data in a data warehouse is more straightforward to review because it is cleaner and comes in a uniform format to query from. Since they restrict data to a schema, data warehouses are especially useful for analysing historical data for certain data decisions.
Only processed and clean data that has been employed for a particular purpose is stored in data warehouses. A data warehouse has the benefit of not wasting storage space on data that may never be used. Data lakes, on the other hand, store the unprocessed data in data lakes, sometimes for the sole purpose of storing it and occasionally for specified future use.
3. Users
When speaking about the user base, both data lakes and data warehouses have their own set of audiences and applications. For example, data professionals frequently use preprocessed data in data warehouses to produce high-end visualisations and reports. Businesses looking for a well-organised and specifically designed infrastructure for data analytics will appreciate it.
Since unstructured data in data lakes typically requires organisation before being put to use, highly tech-savvy individuals such as data scientists or engineers work with data lakes. Data scientists use unstructured data from data lakes in order to find patterns and important information that can be used to improve products and services based on artificial intelligence.
4. Security
The database administrators create models that only allow authorised individuals to access the data warehouse. For the purpose of preventing data flow failures, such security models also shield databases and schemas from unauthorised intrusion. Organisations must adhere to a number of international data privacy regulations, which include the security and management of data warehouses. Regarding a data lake, the same cannot be said. A data lake, unfortunately, does not fit this description.
Data Lake security is compromised by the need to grant access to numerous individuals, applications, and even outside parties. Positively, as the importance of compliance requirements for each and every type of data grows, better security controls may be enforced on data lake infrastructures, thereby ensuring a high level of security.
5. Pricing
Since data lakes are not performance-centric, they are on the cheaper end of the pricing scale. The goal of setting up a data lake is to preserve enormous amounts of data that have not yet been assigned a specific use. Years may pass before data from data lakes is used for cognitive computing or data warehousing. Data lakes are therefore optimised to lower the cost of keeping unprocessed data.
In contrast, data warehouses are made to accommodate various analytics requirements within enterprises. They must provide intensive performance in order to facilitate the development of high-quality data for insights and analysis. Due to this, data warehouses are more expensive than data lakes. However, if businesses have a great plan, the ROI from data warehouses can be enormous. Many businesses use manual labour for ETL procedures, which raises the cost of managing data warehouses. However, enterprises can streamline the entire data warehousing process to lower operational expenses by employing no-code ETL solutions.
Conclusion
This blog post offered a comprehensive overview of the two most common database storage options available today: data lakes and warehouses. This data warehouse vs. data lake blog gave a succinct summary and comparison of both methods. Additionally, the blog provided the criteria for evaluating each storage method.
The choice of which data storage system to use depends on striking a careful balance between requirements, the value gained from data analysis, and infrastructure, storage, and processing expenses. Organisations that need to be extremely agile and handle smaller amounts of data may opt for the data lake route. A data warehouse is an option for those in sectors where data volumes are much higher and where data needs to be cleaned up to be most relevant to the widest possible audience. To offer the most flexibility, some people might pick both. Overall, the decision between a data warehouse and a data lake purely depends on the company's objectives and available resources.
