In the last two years, data architecture has gone through massive changes. Thanks to the rise of AI, IoT and other developments, the market has shifted towards a focus on speed and flexibility. Data technology needs to be flexible in order to meet the ever-evolving needs of today’s users and ensure its continued relevance in this fast-paced market.
Let’s now take a look through some of those important breakthroughs.
Rise of NoSQL
NoSQL has, more or less, replaced RDBMS as the default database model now. In spite of being structurally simpler than RDBMS, NoSQL is much faster at data management. NoSQL also adds scalability to databases, which is another area where RDBMS was failing to keep up with the growing demands.
From a purely business perspective, the simplicity of architecture also meant that the new NoSQL standards could be developed much faster. In other words, as far as database architecture is concerned, NoSQL truly was an improvement in every possible way for both the developers and the customers.
Dynamic Partitioning Vs. Static Partitioning
If you are someone who is still fond of classic static partitioning, that is understandable, given how much easier static partitions are to troubleshoot. In fact, the ease of debugging is primarily why static partitions are still being used in server type infrastructure isolation today. However, static partitioning suffers from the following disadvantages.
- Internal and external fragmentation of data
- Multiprogramming limits: The number of processes can never exceed the number of partitions in RAM
Dynamic partitioning, on the other hand, is flexible and adaptable. Although it still cannot stop the external fragmentation of data, it has most certainly taken care of internal fragmentation altogether. In simple terms, dynamic partitioning provides every process or application the resources it needs and when they need it. This also is a huge advantage over traditional static partitioning, where hardly 10% of the allocated resources are ever used.
Infrastructure Isolation with Containers
Essentially, containers are virtual software systems that adapt to the specific application or process that they have been tasked to run. The comprehensive virtualized OS includes every executable, code, configuration files, and even libraries to facilitate a seamless experience across all devices. There is also overlapping between the definitions and usage of containers, as well as the aforementioned dynamic partitioning.
A container system presents viable solutions to multiple issues that developers often face with compatibility across multiple systems. The following should serve to get the point across.
- Developers can use the standard Docker software unit to specifically isolate or contain a particular app they are developing
- This adds universal shareability to the software, even while it is in progress
- They can share their applications with others if they need to, or shift it to another virtual or physical environment, without having to worry about compatibility issues
Elastic Parallel Processing
EPP or Elastic Parallel Processing is a relatively new, cloud-powered data architecture that shares almost all of the same advantages that the older Massively Parallel Processing (MPP) architecture brought with it a while ago. However, EPP does not share the disadvantages of MPP, making it a superior version of its predecessor. The advantages of elastic parallel processing can be summarized as follows, especially when compared to massively parallel processing architectures.
- In the absence of any B-Tree index, the throughput can be kept uncapped
- To improve performance, data is kept cached in the compute clusters, but skewing is avoided since free nodes automatically depressurize overloaded nodes when necessary
- The client doesn’t need to overpay for storage space that they have no use for
Ease of Availing Expert Help: The Missing Piece
All the technology we’ve discussed would have little meaning to a business user if they did not have the qualified experts available to them. Companies need employees or freelancers who can take advantage of everything that revolutionary improvements in data science have brought in to improve business performance across multiple segments. This was a particularly difficult task before DataWrk.com created a portal to connect the top data analysts, machine learning experts, data visualisation experts and data scientists to the companies that need them.
It is critical for businesses today to take advantage of data analytics, benchmarking, forecasts, etc. but hiring data scientists is not easy for smaller organisations. They either cannot afford to put highly qualified data analysts and machine learning experts on their permanent payroll, or they simply don’t know where to find the right talent.
Thanks to such platforms where talent meets business, even smaller companies can hire top-rated data data specialists for short-term projects these days.
