Big data storage is an involving task, made more complex by the current data explosion. Two key methodologies deal with this kind of storage: data lakes and data warehouses. Often confused with each other, data warehouses and data lakes are distinct in structure and purpose. For enterprises to make the most of their data, they must know which of the two they need and when each is used.
Data Lakes and Data Warehouses
A data lake is a storage repository that can store large amounts of raw data, whereas a data warehouse is a combination of technologies for transforming data into information.
Both are data storage repositories that are designed to store vast disparate data. They both provide actionable insights and aim to help enterprises make better, data-driven decisions.
- Data. Data lakes contain raw data. They store all data types. On the other hand, data warehouses store processed data. The data types of a data warehouse are predetermined.
- Processing. Data does not need to go through a transformation process in a data lake. However, with data warehouses, data needs to be processed and manipulated before storage.
- Storage. Data storage in data warehouses is relatively cheaper than in a data warehouse. With data lakes, it is possible to separate compute and storage to optimize costs. On the other hand, the processes and manipulations on data before storage show that compute and storage aren’t separable in data warehouses. As a result, storage becomes not only more time-consuming, but also pricier.
- Agility. Data lakes are not structured. They are easy to modify due to great agility. Data lakes can easily be configured and reconfigured, unlike data warehouses. Data warehouses are highly structured. Even though this makes data easy to access, their configuration is fixed. It is challenging to reconfigure data warehouses.
- Users. Data lakes are not ideal for users who are not familiar with unprocessed data. Data scientists are well suited to used data lakes. Data warehouses provide self-service access to data since they are easy to understand and use. They are suitable for operational users.
- The unpredictable nature of data makes it difficult to deal with data. Data varies in value, quality, and consistency. It is difficult to control the quality of data.
- A data lake may become a data swamp — the destination for data that has little value. A data lake may also contain data that may never be analyzed for insights.
- Inconsistency of data can be an obstacle to data analysis unless handled by skilled data analysts.
- The scope of data lake datasets creates a higher likelihood of having data governance, privacy, and access control issues. It becomes increasingly challenging to determine who can access what data and for what purpose.
- Data lakes are not the most suitable method to integrate relational data.
- Ensuring data quality is acceptable is a challenge since the integration of data from disparate sources may introduce issues, including semantic conflicts, data inconsistency, and repetitive, and incomplete data. Furthermore, unstable data source systems impact the quality of data. For example, if a bug exists in the source system it could be responsible for defects in the data warehouse.
- Guaranteeing acceptable performance. It is difficult to tune the performance of a data warehouse after it goes live. When designers forget to draft performance goals during planning for the warehouse, it limits the usability of the data warehouse after it is created. Additionally, the set performance goals are sometimes unrealistic.
- Data reconciliation. This is the process of ensuring data in a warehouse is correct and consistent. This is a complex process since it often imitates the entire transformation logic of the warehouse. Plus, developing the warehouse itself is complex.
- User acceptance. However promising a data warehouse may be, unless users fully accept it, it is considered to be a failed project. Users may be reluctant to accept a data warehouse for various reasons. For example, preference of legacy systems and processes or just a sheer lack of interest.
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Data Lakes Use Cases
- Internet of Things. Data lakes are useful in an IoT context because they are capable of handling large volumes of raw data. This data yields low latency because data is handled without transformation.
- Identify business opportunities and competitive advantages. Enterprises can achieve this by centralizing disparate data and data sources then deploy machine learning models and analytics tools to get predictions on market gaps and opportunities.
- Providing valuable insights from raw data. Data lakes can provide actionable insights from data sources such as social media content to rapidly understand consumer patterns to improve sales.
- Improving research and development. Research and development departments can take advantage of the data assets available to power advanced analytics tasks. The result is better decision making.
Data Warehouse Use Cases
- Data modernization. Data warehouses keep organizations at par with the evolution of business and technological requirements. The evolution helps support current technologies as well as data storage systems and solutions.
- Integration with other systems. Data warehouses allow organizations to seamlessly integrate systems like business intelligence and visualization as well as offer easy integration of big data systems.
- Separating historical data from source transactional systems. Data warehouses use common data models and formats, which enable organizations to easily access historical data from diverse locations.
Storage and Infrastructure
Data lakes require a cost-effective and reliable storage mechanism. The storage solution should be scalable and cater to both structured and unstructured data. A popular solution is the Hadoop Distributed File System (HDFS). The HDFS layer is one of the key layers of the architecture of most data lakes. It is a landing zone for all data resting in the data lake. Hadoop has a fundamental goal of storing data in whichever form it encounters it and stores data by dividing files into small fixed-size data blocks.
HDFS uses block storage. A newer approach is the use of object storage instead. Object storage is the bundling of data with a unique identifier and customizable metadata to create objects. It gets rid of the hierarchical file storage structure and addresses everything in a flat address space. This makes it infinitely scalable. Storing the same amount of data in a HDFS data lake could cost three to five times more than using object storage. Enterprises can modernize their information architecture using object storage.
Defining the storage of a data warehouse means defining where a warehouse lives. Depending on an organization’s needs, there are two approaches. A warehouse can be in the cloud or an on-premise server. A cloud server is particularly appealing to enterprises seeking a solution with more flexibility and scalability. Management of data is eased as great responsibility is put on the cloud providers. Since there is no initial hardware investment, it is cheaper for enterprises. However, security is controlled by cloud service providers and data egress charges are applicable.
Data warehouses of today are meant to give the user a seamless experience between cloud and on-premise setups. They are increasingly blurring the lines between the cloud and on-premise. Enterprises can enjoy the best of both worlds while assuming more control over where their data lies. Furthermore, data warehouses are evolving to offer end-to-end solutions. Previously, a data warehouse would have to be subject to numerous integrations, such as analytics tools, lengthening the steps of the data journey. Considering the ever-increasing volume of data, artificial intelligence operations in data warehousing will be increasingly used to optimize warehouse operations and increase efficiency.