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A Flurry of New Technologies for Integrating New Data With Your Old Database

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Ever since the dawn of big data, cloud-based software and technologies have been taking over the realm of RDBMS. It is still bringing about a profound change in the way we perceive and handle data. The new software applications pay much attention to UX and UI, as they do in data architecture, data collection, and delivery. This new trend of data management has lead to the development of new technologies that usher in a new generation of data integration systems.

Here’s a brief glimpse at the technological requirements of the new era of database management and data integration:

REST/SOAP APIs for application integration

As the days go by, more software applications are becoming a part of cloud-based services. These depend on SOAP/REST APIs for all kinds of metadata management. Unlike ancient on-premise software applications, the new cloud-based, SaaS apps do not allow users access to the root database behind their cloud servers. They do not have the relational client-server interface, which their previous generation of SaaS had. As a result, they must compensate by providing reliable and consistent ways to expose REST and SOAP. These apps must be able to leave out the complexities and enable their users to integrate them with the rest of their business operations.

The rise of data lakes from data warehouses

Most IT businesses and DBMS companies are moving away from conventional data warehouses to data lakes. It is common for those IT organizations working with HADOOP frameworks. These data lakes are data reservoirs of all the data based on Hadoop Clusters. Amazon Redshift and Microsoft Azure Data Warehouse are both cloud data repositories, which are currently providing low-cost alternatives to specialized data warehouse apps. As a result, the knowledge of new data integration technology should at least include the basics of HDFS and Spark.

Engines should accommodate varying data velocities and sizes

An increase or decrease in data velocity should not call for a change in engines. The last-gen data engines were more likely to conduct batch processing of large volumes of data, or they could only handle low amounts of data. They were not much flexible. The modern data integration options provide the freedom of flexibility. New platforms should provide the necessary velocity irrespective of data volume. It just means the data engine is capable of processing large bulks of data like that continuously flowing in from IoT and small fractions of data like the removal of a product from the website listing.

Big data technologies linked to existing data

Most tech companies and IT companies already have virtual chests full of big data. Since everything generates big data, from customer feedback to the introduction of new products; each action generates some bytes of interpretable data. New big data can give you a new perspective on an old problem, but blending the new data with existing data will provide you with the in-depth insights you already need.

Integration of data depends on a reliable big data system like Hadoop and Spark, and the availability of an RDBMS along with a competent DBA. These databases can be remotely managed as well by a remote DBA. They can provide you with safe access to your data on a remotely hosted server. Remote database management offers instant access to old data, and Oracle has its own SQL query-based systems that allow running a vast number of queries across all relational databases on Hadoop and NoSQL data.

Data integration becomes user-oriented

The new methods of data integration are not clock-driven anymore. In fact, the new methods are also dependent on SOAP/REST APIs. It is more of a transitional phase, where new software is cloud-based, and there are elements of on-premise integration as well. It poses a new multi-cloud hybrid environment for the users. The new data integration technologies can manage both on-premise and cloud-based operations. Very soon, it is all going to be about sharability and connectivity. Even data integration is going to be about connecting all disjointed APIs in the business system and create an integrated API for a better UX. Most of the new data integration technologies for modern RDBMS come with pre-built connectors that speed up the implementation and connection process.

Special mention: software embedded microprocessors

It is something entirely fresh from Oracle. The new technology allows the integration of software directly into microprocessors. This “Software in Silicon” technology offers better speed and performance. It is compact, and it can perform core tasks like encryption at a lightning-fast speed. More importantly, the speed and performance come from integrating the algorithms directly into the chip.

This one offers the following advantages:

  • Better database in-memory performance.
  • Higher capacity in-silicon by performing real-time actions like data decompression.
  • The direct encryption in-silicon provides better protection and security to users.

We are expecting a boom in the software in silicon technology. More vendors are soon going to come up with something similar and more advanced to make the process even faster and more secure.

From all the recent reports of new data management software and SaaS Apps, we have come to understand that integration technology needs to be a SaaS as well. Anyone who needs the new integration technology should be able to access it as a Software as a Service. The service needs to be always on and always elastic, to accommodate the demands of modern data integration systems. On-premise data integration usually cannot handle the elasticity required to switch between the integration of the bulk of data and small amounts of data at the same speed. The “Citizen Integrators” or the new class of users who like to maintain their own database have made SaaS almost indispensable. As the number of citizen integrators continues to grow in number, so does the demand for self-service with a simplified design, management and scrutinizing.

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