Companies traditionally deal with two main types of databases. They manage data from OLTP (Online Transactional Processing) and OLAP (Online Analytical Processing) databases. OLTP databases store row data generated from current transactions. The data is consistently updated in real-time by modifying each request made. Check stubs are official documents issued by an employer to its employee. It contains information about the amount paid to the employee during the previous period.
OLAP stores historical data in batches that lower concurrency due to the high number of rows involved. Due to the different nature of data in OLTP and OLAP, they require different technologies to make requests. HTAP technology is a hybrid database that processes requests from both OLTP and OLAP and benefits a business in different ways.
Using in-memory HTAP computing to create a seamless learning process
Businesses today are recording the highest competition ever experienced. To maximize return on investment, companies use various technologies to attain real-time information for quick decision-making. For example, a trucking company’s productivity can be affected by multiple factors. There can be congestion at the port, the weather may suddenly change and slow movement on the road, or there could be accidents/mechanical breakdowns.
Most of these factors cannot be predetermined if they will happen or not. At one time, there could be a long queue at the port, which might cause delays in cargo loading and delivery. At other times, a road might be closed and traffic diverted to other roads due to an accident.
These are issues that require tracking companies to adopt a continuous learning system that updates the current situations in real-time. The company can then use the information to advise drivers to use alternative routes or update customers on any new changes. The system can suggest the shortest routes to use, saving fuel and time. Quick deliveries avail more trucks for optimized customer service.
The company must implement a way for processing both OLTP and OLAP, which is adequately provided by HTAP. On the other hand, HTAP cannot function effectively if there is no system in place to allow the handling of huge data in real-time. The best way to allow cost-effective continuous learning is through in-memory computing. Through this system, a company can continuously update its deep learning and machine learning models as data gets generated.
How HTAP work
High-level technology is required for growing online businesses amid stiff competition. There is a need to process data fast to achieve rapid response times to every business opportunity or issue that presents itself. The company’s current data is critical in providing insights for timely decisions.
It may not be necessary to process all the available data if the insights are required from a specific set of data. An HTAP architecture is required to hasten the process of handling data from both OLTP and OLAP databases.
The architecture brings together both current and historical data and processes it concurrently. HTAP creates a shared database layer that is scalable and has low latency. It pulls different chunks of data from multiple sources and makes it readily available for retrieval and processing. Its data aggregation capabilities are almost similar to the capabilities of a digital integration hub.
How businesses can benefit from HTAP architecture
In-memory computing provides scalable data handling solutions with the highest speed by eliminating latency issues. HTAP, on the other hand, eliminates the need for using different systems to process data stored in OLTP and OLAP databases. It combines the two architectures and processes queries from the two databases at super speeds. Businesses benefit in various ways.
HTAP eliminates the need to move data to data warehouses
A company’s operational data is stored within the enterprise to make it readily accessible. It contains data such as client information and employee and payroll data. Data generated from online transactions are first stored in an operational data store from where it can be modified or removed in real-time.
The data is then moved to data warehouses or other big data storage solutions. Whenever a company needs to gain business intelligence, it retrieves chunks of data from a data warehouse to perform queries and analyze them. It is more challenging when there is a need to process data from both the warehouse and operational data store.
Since the process requires two different systems to perform, the company requires more time for retrieval and analysis. HTAP combines both OLTP and OLAP databases which eliminates the need to move data to warehouses for retrieval at a later date. HTAP makes the data readily available by providing simplicity and speed.
HTAP eliminates the need for making multiple copies of similar data
Operational data stores avail data to a data warehouse. It is often possible to find the same copies of data stored in operational data architecture in a data warehouse. In-memory computing enables the processing of both operational and historical data on the same platform. This makes it possible to access data from the data warehouse and ODS from the same place. Elimination of the need to move data to a data warehouse also eliminates the need to store multiple copies of the same data.
HTAP makes data readily available for processing
Latency challenges make processing data for analytics a slow and tedious process. The data must be retrieved in batches depending on where it is stored. When it is availed on HTAP, data can be modified, added, or removed in real-time. It becomes readily available for processing and analysis whenever it is needed. The data is stored within the business, which increases the speed of querying.
Data available in HTAP is flexible and easy to compress if there is a need to make changes. Data within an HTAP hybrid environment uses the entire available RAM and CPU power required for in-memory processing. Scaling the data simply requires the addition of new nodes to the available cluster.
In-memory processing is no longer a luxury today but a priority for businesses. The use of HTAP architecture is more cost-effective because it consolidates both OLTP and OLAP databases. Many organizations are adopting HTAP systems due to the vast benefits they provide. The system readily avails data for analytics, and organizations no longer need to move data to data warehouses. An in-memory HTAP architecture provides enhanced data processing speed.