As web applications become more complex, they require an efficient and robust solution to prevent software and applications from becoming compromised. When combined with the Micro Fronted web application, the Single-SPA open-source library allows teams to work from multiple locations. In addition, it has a larger bandwidth to incorporate various applications in one open-source library.
Understanding and implementing Micro Fronted applications can be a cost-effective and time-saving solution. Read about the root applications, management, implementation, and best practices for Micro Fronted integration with Single-SAP.
With the impact of the pandemic and changing digital age, brands need to evolve to create an omnichannel experience for their customers. If they resist this process, more brick-and-mortar businesses will fall to the wayside. By incorporating technology such as smartphones and tablets, AI, computer vision, and the IoT, they can evolve their strategy and meet customer demand.
Customers want a seamless and personalized experience whether they are purchasing in-store or online. Not only are customers becoming more interested in technology integrated into their shopping experience, but they are looking for brands making sustainable efforts.
Check out this article to learn five ways business can improve not just their customer experience but the efficiency of their workforce.
There are numerous edge computing use cases across various industries. This includes industrial manufacturing, transportation, healthcare, and throughout smart cities. When incorporated correctly, it can be a reliable and cost-effective way to manage and store data across numerous smart devices.
Unfortunately, if businesses don’t plan for and manage the integration of edge computing properly, they may face several challenges within software frameworks and from a security standpoint. Read all about edge computing, its benefits, and potential challenges companies may encounter when implementing it into their data management architecture.
To create a valuable and lasting token, businesses need to include tokenomics in their strategy. Within this strategy, there should be incentive and distribution models to ensure that the token is successful and generates traffic to create a foundation of active users.
In addition, businesses must incorporate an evaluation process that includes tokenomics to create a truly self-governed decentralized network. This is because tokenomics can not only help make the previously mentioned models a reality but also help to automate data collection and set the price of a token.
To understand the principles that support tokenomics, the challenges you may face incorporating it, and the methods and levers of tokenomics, check out this article.
A distributed cloud is an influential infrastructure that companies can use from multiple locations, allowing easy accessibility. Although, not understanding how to properly manage a distributed cloud can have considerable adverse effects on a company. Additionally, it’s crucial to understand the difference between logging and monitoring as they are practices used to control a distributed cloud.
Several challenges can arise when monitoring a distributed cloud, such as inconsistencies with different log formats, connectivity, and security. However, numerous tools exist that can help alleviate these issues, such as Microsoft Azure and Google Cloud. Learn about the best practices and tools to manage a distributed cloud.
The recent pandemic underscored the healthcare industry’s need for less invasive, timelier methods of diagnosing severe diseases. As wearable devices of all kinds become widely adopted by healthcare-conscious consumers, we find ourselves with the perfect conditions for the increased use of digital biomarkers to predict future health outcomes and explain diseases in a more data-driven manner.
In this paper, we explore the current state of the wearable diagnostics market and learn about the possibilities of using human perspiration to monitor patients for potentially life-threatening diseases. We’ll study the use case of using perspiration to detect cytokine storms via the specific biomarkers that are evident in both blood and sweat.
Several big data and analytic architectures, such as Lake House and Data Lake paradigms, are widely used by organizations to implement data and analytics platforms to uncover deeper insights into their data.
Newer architecture patterns are also evolving and have come to the foreground as organizations look for meaningful outcomes for their investments in big data, analytics, AI, and machine learning (ML) technology.
Among them, data fabric is one of the most interesting archetypes addressing data usage to incentivize businesses and help them extract value from their data.
With data fabric, it’s essential to leverage data and metadata to connect the dots and make it accessible across the organization. In this post, you’ll learn how a business can use a knowledge graph engine to power the data fabric architecture and help extract valuable data.
Data Fabric
Data fabric was defined by Forrester in 2016 as “a unified, integrated, and intelligent end-to-end data platform to support new and emerging use cases.”
Data fabric is an architectural concept that interweaves the integration of data pipelines and data assets to lay out a discoverable data landscape through automated systems and processes across environments. A data fabric doesn’t move data but provides an abstraction to make data available across the organization.
Below are the key components of the Data Fabric architecture:
Data Cataloging
Metadata Collection & Analysis
Metadata Activation
Data Integration
Data Enrichment with Semantics through Knowledge Graphs
Knowledge Graphs
To unlock the value of data, you must understand all data formats, whether structured or unstructured. Even though data may be present in a single place, finding the hidden relationships and embedded knowledge for further analysis can still be challenging.
Having vast amounts of data has furthered the need for a representation of data that reflects a human understanding of the underlying information. To make information more easily digestible, data analysis needs to bring out relationships similar to the actual relationships in our world and not be bound by defined data schemas.
This is where knowledge graphs come into play. Knowledge graphs, an integral component in content engineering, can integrate billions of facts. Facts can come from disparate sources and formats, which can then be stored in graph databases and used for extracting further insights.
A knowledge graph represents a collection of connected descriptions of different entities, relationships, and semantic definitions. Entities can be actual objects, events, or even notional or abstract concepts.
Knowledge graphs combine database characteristics as the data can be queried, a graph as it forms a network, and a knowledge base as the data supports formal semantics. These combined characteristics can help interpret the data and help infer new insights.
The KG Engine of the Data Fabric
A data fabric relies on a set of tools and services to keep the components going and pull together the information of the fabric. For example, metadata can be collected as data is ingested or transformed and pushed to a metadata store or pulled directly from data stores or sources through data discovery tools.
Data is stored in various types of data stores post ingestion and processing. Data can also sit in different environments on-premises, on the cloud, or in multi-cloud and hybrid environments.
The Knowledge Graph (KG) Engine provides a mechanism to abstract the complexities related to data ingestion, data processing, and data storage and movement to provide a unified view of the data.
Below is a representation of the KG Engine setup to power the enterprise-wide data fabric:
The metadata collected and the data available across systems and environments feed into a conformance layer for quality checks and extracting information in case the data is unstructured.
The conformed information undergoes entity resolution and semantic integration to create the semantic data models based on ontologies, which can then be questioned and inferred as part of the data fabric.
The KG Engine can be automated to ensure that the knowledge graph continuously evolves as more data sets and metadata becomes available. As a result, applications, algorithms, and teams can use the information.
In conclusion, knowledge graphs enable semantic data modeling and make it easier to understand the data. It also helps translate disparate data into information that can be consumed (through queries or visualization) for different decision-making purposes by organizational actors using the data fabric.
Learn more about activating the value of your organization’s data:
