Enterprise data management must adapt to the exponential growth of structured, semi-structured and unstructured data. By implementing efficient data lakehouse, organizations can reduce costs and gain meaningful insights from their data, regardless of format.
Data warehouse systems benefit organizations by performing several essential functions in data management. One of the most important of these is that they facilitate the discovery and analysis of data, which is critical for organizations to make informed decisions.
We live in an era of data-driven organizations, where large amounts of data are being generated faster than ever in various formats, supported by the storage solutions required for big data, artificial intelligence and machine learning. Organizations can determine their ability to compete and control costs by adopting data-driven workflows, processes, and insights.
Data lakes and data warehouses used to be very useful, but today, they are costly and often do not provide the functionality organizations need. Modern data management requires new ways of storing, retrieving and analyzing data.
Limitations Of Data Warehouses
Let us first look at the data warehouse. This technology is ideal for addressing information gaps in organizations and simplifying data management, as relational databases store data centrally. Organizations can use the data warehouse to run various applications, such as business intelligence and reporting, with relative ease. This has contributed to the significant success of this technology: today, 35% of business data is stored in fixed data warehouses and 37% in cloud data warehouses.
However, data warehouses have two main limitations. First, it isn’t easy to process and store unstructured data. Traditional data warehouses are designed to process structured data arranged in tables with columns and rows. Text, images, videos and other non-relational data types are unstructured data that do not fit into traditional row and column models. The main barrier is cost. In other words, data warehouses are not economically viable in a world where data volumes are growing exponentially. Data warehouses often come at a higher price because they require structured storage, complex data processing and additional analytical and reporting capabilities.
Data Lakes Are Not a Perfect Solution
Data lakes are designed to solve these problems. They are designed to store different data types, such as unstructured and semi-structured data, in their original form. Considering the lifetime of the data warehouse compared to data warehouses, data warehouses offer more flexibility and lower costs. However, they can also be challenging regarding data management and the speed at which new data can be acquired. However, many organizations adopt a hybrid approach, where structured data is stored in a data warehouse and unstructured data in a repository, negating data lakes’ economic benefits.
In addition, the team needs to be highly experienced and meet strict criteria for data lakes. Data lakes are structured very differently from data warehouses, as only staff with sophisticated tools and techniques and in-depth knowledge can extract meaningful information about their organization from the unstructured data stored in data lakes. In contrast, users with little computer knowledge can query data lakes with simple SQL commands.
Improving Operational Data Management with Data Lakehouse
These various constraints drive the development of new technology for enterprise data management, such as data lakes. With data lakes, companies can combine structured search and management of data in a data warehouse with efficient and cost-effective data storage in a data lake. Data lakes achieve this by using artificial intelligence and schemas to structure unstructured data. Simple SQL queries can then be used to search this semi-structured data.
This strategy involves building a large data warehouse on top of the data warehouse, allowing companies to eliminate expensive data warehouses. Enterprises have immediately recognized the potential of data lakes, and around 70% of organizations are already using, testing or planning to implement data lakes in the coming year.
Starting A Data Lakehouse Project
As with any other technology, not all data lakehouses are created equal. To ensure the best possible implementation, ask yourself these three key questions when considering introducing this technology into your organization:
1. Does the data lakehouse offer easy-to-use self-service SQL analysis? Even people with little technical knowledge should be able to run queries without the help of IT experts. This saves time, reduces costs and increases operational flexibility.
2. Are open standards the basis of the data lakehouse? Organizations can use open-source solutions to use the best processing modules and remain independent of a single vendor.
3. Does the system offer flexible deployment options? Data lakehouse must be able to be deployed both on large cloud platforms and in their own data centers, as all IT systems are different.
Traditional data processing and storage models continue to evolve as organizations treat data as a critical resource for innovation, competitive advantage and operational efficiency. This is the only way to keep pace with the massive growth in structured, semi-structured and unstructured data. This drives the adoption of scalable, flexible and efficient enterprise data management systems, and data lakehouses are at the forefront of this evolution. Businesses that follow this trend and use the latest technologies can gain the deep insights they need to grow profitably.
