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Benefits Of Using A Data Warehouses in Higher Education

Data warehouses is used by higher education IT Systems to store information and other aspects of their operations. Learn how it can help you

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snowflake cloud services

What is Snowflake data cloud services ? 

The Snowflake on data platform is built as a layer over Amazon Web Services, Microsoft Azure, and Google cloud infrastructure. The business does not have to install specific hardware, and software instead it can use the support of in-house services. This makes it scalable and functions on a powerful architecture and data-sharing capabilities. Now since the platform separates the function of storage and computing, businesses can use and pay for both independently. Organizations with high storage demands but fewer CPU cycles and vice versa can thus tailor the platform to meet their needs accordingly. The data-sharing functionality also allows organizations to quickly share governed and secure data in real time.

The Snowflake architecture comprises: 

Database storage layer that holds both structured and semi-structured data which is organized as per file size, structure, compression, metadata, and statistics.

The compute layer, is a set of virtual warehouses that execute data processing tasks required for queries. Each warehouse or a cluster of them can access all the data in the storage layer without competing for computing resources.

The cloud services layer uses ANSI SQL and coordinates the entire system eliminating the need for manual data warehouse management and tuning. It includes services such as authentication, infrastructure management, metadata management, query parsing, and access control.  

Click on Snowflake Cloud Service to learn more.

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Data fabric Architecture

Modern Data fabric architecture consolidates knowledge graphs, AI, and metadata capabilities to enable data integration and ensure consistent access and exchange of data across the organization. This blog is in continuation to our previous blog on understanding data fabric and outlines the following points:

How is data fabric architecture applicable to businesses across domains?

What are its components?

Best practices.

Data fabric architecture is an industry-agnostic concept which means it is relevant across domains and can help achieve-

Enterprise intelligence- a birds-eye view of organizational performance with the help of intuitive tools.

Operational intelligence- shift from scheduled to requirement-based maintenance (still pro-active) activities for key operations.

Complete focus on obtaining a 360-degree view of customers by tracking customer activities in customer-touch points.

Regulatory compliance using AI-enabled data governance policy enforcement, automatic classification of data assets, sensitive data detection, and masking of data.

Transforming data fabric into an internal search system for relevant access to authorized parties.

Click on data fabric architecture to learn more :

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hybrid cloud data management

Choosing The Right Data Management Approach for a Hybrid and Multi-Cloud Strategy

A quick word on hybrid/multi-cloud solutions- 

Hybrid IT solutions combine private cloud architecture, public cloud solutions, and legacy in-house infrastructure to improve the overall cloud architecture. A hybrid cloud connects all these components to create a unified contained environment. 

Multi-cloud solutions on the other hand use at least two similar clouds of similar types. Some may include multiple private clouds, multiple public clouds, or even in some cases multiple hybrid clouds. Multi-cloud solutions have mostly sourced a variety of cloud service providers – such as AWS, Azure, or Google Cloud. 

The new-found HMC or a Hybrid Multi-Cloud strategy is slowly gaining impetus with enterprises adopting it quickly; this drives agility and brings about cost efficiency with innovation taking the center stage. Hybrid Multi-Cloud utilizes various cloud computing services from multiple different cloud vendors, and private cloud deployment systematically distributes computing resources. It minimizes the risk of data loss since it accommodates an all-public, all-private, or a combination of both. 

Click on Hybrid Cloud Data Management to learn more.

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data protection threats

Top 5 data security threats are :

Password reuse for emails and applications: Weak password strength is bait for hackers who can exploit the account. Business data is stored across many different accounts and services only protected with login credentials. Strong unique passwords must be created; one may also make use of password manager applications for the same.

Improper data access control: It is essential to follow the principle of least privilege to ensure that employee access to data is based on their role and responsibilities only. Allowing everyone in the company to access all data may spill out critical and sensitive information like customer information, financials, acquisition plans, etc.

Skipping data backup: Many businesses, large and small, often overlook the need for periodic data backup. A frequent backup strategy is essential, particularly to protect financial data, intellectual property, source code, and email. A prudent plan will be to start by backing up mission-critical data first.

Click on the Top 5 data security threats to learn more about data protection threats.

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benefits of data governance

Implement a Robust Data Governance Platform to Understand Your Business Data Better

Data is available on multiple sources, on the cloud, and on-premises across IoT-enabled devices paving way for an unprecedented level of attacks to breach intellectual property and data. The lack of a uniform approach to tackle both structured data and fluid data (data generated on the move) adds to this predicament faced by enterprises. Businesses cannot afford to ignore the need to implement data governance controls; progress can be achieved only if a transparent, yet systematic approach is adopted to handle all kinds of data. This will slowly set the ball rolling in favour of reusing such data to generate insights and automation of processes.

The need for “Data Governance” and how is it going to help businesses handle their data better?

Enterprise data plays a crucial role in leading a fast-paced digital transformation strategy. Further-

Senior management requires accurate and timely data to make strategic business decisions.

Marketing and sales professionals need reliable data to understand the needs of their customers.

Procurement and supply-chain-management personnel need updated information to track inventory and reduce manufacturing costs.

Compliance officers are required to prove that the data handled adhered to both internal and external mandates.

Enterprises are compelled to maintain consistent data quality to adhere to regulatory compliance mandates on digital users’ data as a priority. Businesses need to ensure data security, ensure effective data masking of personal data (using SSN, and passwords), and be compliant with up-to-date data protection and privacy laws like GDPR (General Data Protection Regulation). Data Governance manages the availability, integrity, security as well as effective usage of data based on a set of standards and policies such that data is consistent and trustworthy throughout. With an increasing influx of new data privacy regulations and businesses relying on data analytics to help optimize operations and decision-making; a robust data governance solution is a must. A data governance framework regularizes control and management of enterprise data that powers advanced and reliable analytics; minimizing the risk of using sub-standard data and enhancing data security. It is not just about storing data and securing it but also being accountable for that particular data asset along with technology.

Click on the benefits of data governance to know more.

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data lineage in etl

How We Extract Data Lineage from Large Data Warehouses

Enterprises have built massive data infrastructures to capture and manage their ever-growing mountains of data. But as data stores increase, the pipelines that carry precious information to the business become murkier, making the resulting data analysis less trustworthy. Informatica’s Enterprise Data Catalog (EDC) can help you shed light on data transformations along your pipelines, and LumenData has further built tools to extract and visualize additional data lineage to extend the use of Informatica EDC. Here’s how.

More Data Leads to More Transformations

All organizations are data-driven. Complex data engineering and rapid development to accomplish this has brought a new challenge to every CDO/CIO: How do we manage and visualize the data movement across the organization? This challenge is exacerbated with the advent of massive data warehouses, where the tendency is to store all enterprise data “just in case we need it in the future.” Instead of Extract (from source), Transform (to an intelligible trustable form), and then Load (to the warehouse) — otherwise known as ETL — many organizations extract data and dump it into a warehouse with the intention of transforming it when they need it, what is known as ELT. 

Click on data lineage in etl to know more.

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master data management services

Strategy & Architecture

Regardless of your goals, we help you build a cohesive data, analytics, and infrastructure strategy to achieve success quickly and sustainably. Our expertise in cloud, on-premises, and hybrid technologies then ensures you move forward with a secure, reliable, and flexible architecture that will scale with your organization.

Data Modernization

Modern challenges require modern solutions. We match your needs with today’s best approaches to data and analytics so you can identify more opportunities. Our experience in applying innovative technologies and modern architectures enables you to stay ahead of growing data volumes and the increasing need for speed and agility.

Master Data Management

Achieve the elusive “single view” of your customer, product, constituent, and other data. Our enterprise experience helps you unify information and eliminate data silos, establish effective processes and metrics, and uncover the value hidden in complex data connections, relationships, and hierarchies.

Click on Master Data Management to learn more.

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enterprise data fabric

How Data-Fabric can Maximize the Value of Business Data and Accelerate Digital Transformation

Data has the potential to help businesses quickly adapt to change, improve access and visibility of relevant data to stakeholders and stay agile. Navigating data gathered with the exponential growth of businesses becomes a challenge and this can be handled with the help of Data fabric. In this blog, we will help you understand the following about Data Fabric for Business-

Understand Data Fabric and its components.

Growth with Data Fabric, the scope of gaining key insights for decision making.

Value of Data Fabric in digital transformation.

Driving business innovation and growth. 

Data fabric combines both human and machine functionalities to help businesses access data in place or support its consolidation where required. It continuously identifies and integrates data from disparate applications to discover insightful, business-relevant relationships between available data points. Further, it also handles the repair of failed data integration jobs and auto-profiling of datasets. 

Components of data fabric- 

Data Processing helps provide clear analytics-ready data by curating and transforming data for Business Intelligence and Artificial Intelligence.

Data Orchestration coordinates data flow and helps the business with a comprehensive view of the data pipeline.

Data Ingestion works with data spread across various sources such as databases, cloud source applications, and data streams.

Data governance centralizes the entire data governance process of the business and helps it manage metadata locally and in compliance with corporate policies. 

Click on enterprise data fabric to know more.

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Benefits of Data Modernization for State & Local Governments

LumenData has been dedicated partner to state, local governments, as well as higher education institutions to help them in data modernizatio