Data Warehousing Trends and Prospects
Over the past several decades, modern businesses, institutions, and organizations have increasingly relied on knowledge-based management systems. A significant component of these systems are data warehouses. A data warehouse refers to an integrated and time-variable collection of data gathered from operational data and mostly employed in strategic decision-making. It originated from an architectural concept to move data from operational support systems to decision support systems.
More recently, data warehousing in the cloud is quickly gaining popularity. Numerous businesses and technological drivers have also encouraged advancements in data warehousing. At this point, it becomes necessary to determine the current scenario and condition of the market. Here are some of the most important trends in data warehousing today.
Collaboration is necessary for businesses to succeed today. Rather than having separate departments, teams, and implementation systems for functions such as data mining and analysis, IT, intelligence, business, and others, a new model of data warehousing is evolving which entails cross-functional teams that participate in adaptive planning for successful development and improvement. This collaborative model is not possible if businesses were to stick to the traditional forms of data warehousing, where there is only a single server (or set of servers) where data can be stored and retrieved.
Most of the problems associated with data warehousing involve scalability, reliability, security, performance, and efficiency. These issues are primarily managed by the cloud provider when using managed services. Managed services are a form of higher-level services, where most of the challenges for a specific-use case are automatically dealt with by cloud. Using these services helps businesses reduce costs because most of them are billed on-demand by cloud providers, meaning that they don’t need to pay any of the services that they do not use.
In data warehousing, it is necessary to store data from a variety of sources in such a way that it it is more efficient to query for analytical purposes. Using columnar storage can be a great option to boost disk performance compared to row-based storage for retrieving complex analytical queries. There are already existing data warehouse services in cloud which offer these capabilities both for storage and querying. Businesses who use these services should not only expect reduced complexity when setting up a data warehouse, they should also find tighter integrations for access control, integrating multiple data sources, and others.
Data Lakes and Data Fragmentation Across Organizations
Traditional data warehouses store data in hierarchical files and folders. Data lakes, on the other hand, have a flat architecture which allows raw data to be stored in its natural form until it is needed. Essentially, data lakes define the schema of data on reading while data warehousing defines the schema on write. Data lakes are growing in popularity in the data analytics and reporting sphere.
This new model of data warehousing enables faster data collection and analysis across organizations and departments. In turn, this also promotes data agility and more collaboration among organizations as well as faster outcomes.
Data Marts for Production Lines
A data warehouse is essentially a large centralized data repository. Given this setup, it is also necessary to examine data for various production lines. Data marts offers a practical solution that businesses can look into. Data marts contain the summarized data from a particular business unit. They can be utilized as an intermediate source to the data warehouse as well as for each business unit to assess their own data separately.