The Rise of the Unified Database

We are living in the days of ever-expanding data sprawl caused by many hard to control factors. Data sizes are monstrous, the speed of data ingestion is ever increasing, the demand for better latencies is insatiable, and the complexity of the data is increasing. All of this while the need for custom views accessing the same information is exploding.

There are almost too many options that require specialized technical skills to create complex integrations that produce performance bottlenecks when working. It becomes even more nightmarish when testing and troubleshooting. Is the problem hopeless for organizations trying to manage and utilize their data? The answer lies in unified data and unified databases. Let's dig a bit into unified databases and how they relate to the holy grail of an effective data mesh.

What is Unified Data/Database?

A unified data model is a crucial piece to simpler data architecture and supports the data mesh. A unified data model offers an opportunity for organizations to analyze and operate on data from multiple sources in the context of shared views supporting shared business initiatives. The unified data model bridges different ecosystems, allowing organizations to contextualize data sources across various services. The task is statically or dynamically mapping each dataset to a more singular schema or view.

The Benefits of Unified Data/Databases

The benefit of data unification is that it provides a more holistic and accurate view of your many data sources while operating on them in different modes with different performance characteristics. The unified database can simplify the multiple tool and database environment and create a common denominator for data use without worrying about the scale. Unified databases can make data more practical and actionable while helping the data accuracy with the least energy by users and the data architecture keepers.

Downsides of Todays Data Architectures

Today organizations most often run-on specialty databases that have to be synchronized periodically. With the demand for more real-time management, this becomes a copy carousel that gets in the way. It leads to high-latency data makes always-on systems a nightmare. Because different data sources on different data architectures will require redundant transformation logic during the copying and transformation processes, often it isn't easy to guarantee consistency. It also becomes a challenge for data security and privacy programs. What's worse is that some of these copy/transformation processes fail, creating cascading delays and errors. Because our databases do specialized work enumerated below, the copying process is a never-ending nightmare. A unified database that can nearly do all the work of these specialized databases makes for a simpler world.

Generalized Databases

Typically generalized databases are for analytical purposes and rarely scale for transactional purposes. They are often easy to use and handle lower scales of data. Unfortunately, the complex uses and monster data tend to bog these analytically focused generalized databases.

Transactional Databases

Typically, transactional databases are focused on high-end operations where volume and efficiency are often desired. They can stand up to volumes generated by nano-second demands and work well in long logical work units for tasks and processes. However, they aren't for end users who often desire analytical feats leveraging siloed operational data where lateral views are not easily supported.

Analytical Databases

These databases are perfect for high-end analytical work where many sources are mainly focused on reading data but not necessarily creation, updates, or cascading deletes. However, they aren't as easy to use as generalized databases, so they are their own breed

Net; Net:

The unified database does a great job of spanning all these specialized databases. It often ends up as a hub through which many divergent views are connected to many divergent data sources of various types. It simplifies work and gets organizations off the copy carousel. The Unified database is an obvious choice as an infrastructural centerpiece for an organization's data mesh. It makes the shift from inflexible centralized data infrastructures to a data mesh that supports distributed “data-as-a-product” This is particularly important with a distributed data mesh that spans the clouds and on-prem.



Additional Reading:

Get Ready for the Big Shift to the Data Mesh

Convergent Data is Here to Stay

Real-Time Data Mixes Well with Archival Data Now

Leveraging Hybrid Cloud & Multi-Cloud

Speed, Scale & Agility Delivered with Distributed Joins

What’s Driving Data-Intensive Applications?

Reducing Data Sprawl