It is a fact that yesterday’s ‘Data Architecture’ cannot match up to ‘Today’s’ need for speed, increased flexibility, and innovation. And so, it is obvious that some form of an upgrade especially with regards to ‘Agility’ is required. Now, due to the advancement in technology that the world has seen in the past few years, many organizations have been forced to make use of new data technologies. They mainly include the use of a few innovative concepts and components. They are as follows:
1. The use of Data platforms devoid of servers:
Giant tech companies such as Amazon S3 and Google Big Query have now started enabling various business organizations to build and operate data-centric organizations within an infinite scale. This they do by installing and configuring unique solutions or even managing workloads. Furthermore, what these offerings have proven is that when used it can lower the need for the expertise required, and reduce the speed of deployment ranging several weeks to as little as few minutes. More importantly, using such serverless platforms requires no operational overhead whatsoever.
2.Data solutions that have been ‘Containerized’:
The availability of ‘Kubernetes’ (provided by cloud companies) is enabling many organizations these days to decouple and automate the deployment of additional compute power as well as data storage systems. This new type of technological capability, in turn, ensures that complicated data platforms, such as those that require to retain data from one application session to another can scale up to meet the demand.
3.The use of effective Messaging Platforms:
Innovative messaging platforms such as ‘Apache Kafka’ provides for a very efficient, extremely durable, and fault tolerating publish/subscribe services, which can process and store millions of messages that can then be consumed immediately or later as per the consumer’s need. Furthermore, the use of such platforms helps in supporting real-time use cases by bypassing existing batch-based solution. It also provides for a much lighter footprint than traditional enterprise messaging queues.
4.Effective streaming processing and analytic solution:
The availability of very efficient streaming processing and analytic solution such as Apache Kafka Streaming, Apache Flume, Apache Storm, and Apache Spark Streaming means that messages now can be analyzed directly in real-time. Additionally, this analysis used can either be ruled based or could also involve advanced analytics that then would enable extraction of events or signals from data. However, it must also be noted that the use of such processing system often leads to analysis integrating with historic data to compare patterns, which essentially plays a very vital role in the functioning of recommendation and prediction engines.
5.The invention of useful Alerting platforms:
‘Graphite or Splunk’ are a few very innovative digital platforms available today, which can be used to trigger business actions to the consumer. For example, a few inventive technology-driven platforms can be used to notify the sales representatives in case they are not reaching their targets. This can be done by then integrating actions required to be taken into existing processes such as enterprise resource planning (ERP) or Customer Relationship Management (CRM) systems.
6.The increased use of Data pipeline and API based interfaces:
In the digital world that we live in today, there are several useful Data pipeline and API based interfaces. Now, when used they help simplify integration between disparate hi-tech driven tools and platforms. They do this by shielding data teams from the complexity of different layers ranging from speeding to marketing time, thereby reducing chances of new problems arising in existing applications. More importantly, these interfaces allow for easier replacement of individual components that too following change in requirements.
7.Analytics workbenches and their increased usage:
High tech companies such as Amazon, Sagemarker, and Kubeflow function as what is called ‘Analytics Workbenches’, wherein they help simplify and build end-to-end solutions within a highly modular architecture. Such ‘Workbenches’ when used helps in connecting with a large number of underlying databases and services. It also allows for a highly modular design.
8.The development of an API management platform:
Often referred to as ‘Gateway’, the API management platform available nowadays has proven to be a very useful tool, since it has helped in creating and publishing data-centric API’s. It has also enabled the implementation of usage policies, controlled data access, and also measured usage and performance. This wonderful platform has also allowed developers and users to search already existing data interfaces and reuse them rather building new ones. It has also often been found that an API Gateway is embedded as a separate zone within a data hub. Moreover, it can also be developed as a standalone capability outside of the hub.
9.Use of a data platform to ‘Buffer’ transactions outside of the core system:
Many a time a data platform as a ‘Buffer’ becomes the need of the hour. And so, under such circumstances buffers could be provided by central data platforms such as a data lake or even in a distributed data mesh, which is an ecosystem that consists of only the best fit platforms. These have been specifically been created for every business domain’s expected data usage and workloads. The best example is the one bank built on a columnar database to provide for customer information such as the most recent financial transactions that too directly to online and mobile banking applications. The use of such a platform then only serves to reduce workloads on its mainframe.
10.Use of data architecture itself as a platform:
It is also possible to use data architecture and its tools and capabilities as a platform of storage, also to manage and speed up implementation as well as remove from data producers the burden of building their data asset platform.
11.The usefulness ‘Data Virtualization’ techniques:
Data Virtualization which was once used commonly in niche areas such as customer data can now be used across all enterprises, to organize not only access but also to integrate distributed data assets.
12.The Data Cataloguing tools and their utility:
The availability of data cataloguing tools has allowed enterprises to search and explore data without needing to acquire full access or preparation. It (catalogue) also functions by providing for metadata definitions as well as the end-to-end interface, which eventually helps simplify access to data access.
13.The benefits of using Data Vault 2.0 techniques:
Datapoint modelling is a form of what is known as ‘Data Vault 2.0 techniques’. This type of technique when used ensures that data models are extensible, which only means that data elements can be added or subtracted in the future with limited disruptions.
14.Graph databases and their usage:
Databases such as a form of NoSQL have become very popular these days. This is mainly because when used these type of databases prove to be ideal for digital applications, which mainly require massive scalability and real-time capabilities. It also is effective for data layers as well as serving AI applications. This is all thanks to the ability of these databases to tap into unstructured data. Also, it offers the capability of modelling relationships within the data in a very powerful and flexible manner. Many companies have begun building master data repositories using graph databases. This is mainly being done to accommodate changing information models.
15.The availability of technology services:
The development of many technology services such as ‘Azure Synapse Analytics’ has allowed for ‘querying file-based data’ which is similar to relational databases by dynamically applying table structures on the files. This, in turn, enables the user to flexibly continue using common interfaces such as SQL while accessing data from the file.