Today, many companies have prioritized digital transformation as a critical component of their long-term growth plans. It goes without saying that data drives digital transformation; however, many companies find it difficult to harness mountains of data assets in order to expose business value and distil actionable perspectives for decision making. A strategic information management system is needed for efficient data handling. Simply stated, modern data infrastructure is needed to allow teams to provide actionable insights.
Modern Data Platform – What is it?
This can be illustrated as follows:
A Technology lens: evaluates the technology needed for optimal data processing and control, resulting in business benefits. It encourages the evaluation of software and applications such as Cloud Data services, DevOps, Data Visualization, API management, and so on.
A Market lens: This calls for conversations on how data will have a real effect on the business – the business use case, the indicators to be tracked, the forecasts to be made, and more, all backed by data features such as data sources, types of data, data volumes, integration criteria, data storage & governance, data catalogue, and more.
An optimal solution necessitates these two forces cooperating while keeping market gains and differentiation in view.
Characteristics of a Well-Architected Modern Data Platform
The core capabilities mentioned below help to distinguish a modern data platform. These are in addition to the standard capabilities provided by organizations for the provisioning, management, and consumption of high-quality data:
Open Data Access: It is important to avoid using proprietary GUI or connectors and instead use data in accordance with industry protocols wherever possible. As a result, it is future-proof.
Comprehensive Data Security: To deter data leakage, it must authenticate and approve data objects. At the very least, it must be deeply aligned with a company’s directory and security services. For more sensitive data properties, additional data protection protocols will need to be activated.
Virtual Data Consolidation: Data is transmitted through many locations, infrastructure, and platforms. A good data network integrates virtually fragmented and disparate data by providing a structure for routine management, processes, and seamless portability.
Adaptable Data Indexing: A solid data platform manages metadata effectively by embracing complex, personalized attributes programmatically in order to glean smarter insights from the data.
Lifecycle Data Services: These present an ideal opportunity to minimize costs, mitigate risk, and improve organizational simplicity. A digital data platform automates the lifecycle and regulation of data through different forms of applications, infrastructures, containers, and formats.
Data Value Delivery: A data platform is often assessed, based on the value it provides to its customers. It must match information with customer expectations in order to have better precision and value.
Microsoft Azure – A Rational Investment
Organizations are rapidly investing in building data platforms on Microsoft Azure in order to improve efficiency and security. Various Microsoft Azure offerings serve different purposes and form a stable basis for a data base, assisting in the managing of data properties and the generation of market value through insights. Let’s take a closer look at this.
Azure Cosmos DB: A completely managed, multi-model service portal for Microsoft that allows you to govern obsolete data across several Azure regions with only a few clicks. It’s a NoSQL serverless database provider with 99.9% accuracy, availability, and throughput.
Azure Data Catalog: An enterprise-wide metadata catalogue created specifically for you to allow self-service data exploration from various sources. It enables you to provision, log, annotate, discover, link, and protect your data properties.
Azure Data Factory: A cloud-based solution offered by Microsoft that allows organizations to retrieve data from various channels, simplify workflows, and transfer data across pipelines before connecting with third-party applications that perform data analytics and visualization.
Azure Data Lake: A multi-layer, enterprise-grade data management infrastructure that holds both organised and unstructured data in a variety of forms, sizes, and speeds. It is scalable and has infinite storage. It integrates smoothly with data warehouses and fits well with existing IT networks.
Azure Synapse Analytics: An analytics service that combines corporate data warehousing and big data analytics. It allows you to query data on your own terms, using either serverless or provisioned resources – at scale, with a seamless experience to ingest, plan, process, and serve data for BI and machine learning needs.
Azure HDInsight: A Microsoft HADOOP and cloud-based service with broad customization capabilities to instal modules, languages, and create clusters using various virtual machines.
Azure Stream Analytics: A serverless analytics service that can be used on-demand and does not need any infrastructure. Because of the pay-as-you-go scheme, companies will benefit from low TCO. It allows companies to extract actionable information from a pool of live streaming data in a variety of formats, including CSV, AVRO, JSON, and others, in order to improve operating performance.
Microsoft Power BI: A Microsoft cloud-based collaborative environment that combines with Azure analytics to link, track, and exchange BI on smart dashboards for customers, assisting companies in developing a data-driven community.
Azure Databricks: A cloud-based analytics service based on Apache Spark that uses machine learning and data compression to batch process unstructured data. This flexible and highly collaborative service, which connects effortlessly with open-source libraries, lets companies streamline the data analytics process and has reliable data integration capabilities.
Azure Machine Learning: Provide developers and data scientists with a variety of productive experiences for designing, teaching, and deploying machine learning models more quickly. Boost team coordination and accelerate time to market with industry-leading MLOps—DevOps for machine learning.
If you want to know more about Azure Cloud in an hour. Check out the file below. A Microsoft Azure Handbook. This is a file from Microsoft. We don’t bear any copyright for this.
Spinning in Circles, Uncertain Where to Begin?
Platingnum will assist you in being a modern data-driven enterprise. How so? We’ve worked with customers to develop Modern Data platforms that can help them stand out in the market. We allow convenient access to large volumes of storage and analytic computing capacity to help accelerate market insights and outcomes. Contact us now.