In today’s data-driven world, two data professional roles that play crucial roles are data engineers and data analysts. Both these professionals aid the process of extracting data-driven insights, but they possess distinct skill sets and responsibilities. Below are some key facts about educational backgrounds and job roles of the data engineer vs. data analyst, as well as the […]
Managing Data as a Product: What, Why, How
The concept of managing “data as a product” involves a paradigm shift. By treating data as a product designed for consumer use, rather than a pool of semi-chaotic information, businesses can increase their profits. Many businesses have set up customized data pipelines – or other extreme and expensive steps – in unsuccessful efforts to maximize the […]
Core Data Concepts for Digital Transformation
Without a clear understanding of core data concepts, communications around implementing an organizational Data Management initiative can become a muddle. As different teams come together to plan and organize data activities, they must integrate what they mean about data with any technologies. For example, take the term “Data Governance.” Data engineers building systems and tools to enable […]
NoSQL vs. SQL: Five Key Differences
NoSQL and SQL are the two primary forms of database used to store and manage digital data, with each providing key differences that support advantages and disadvantages. SQL deals with relational databases and NoSQL deals with non-relational databases. Both methods store data effectively but differ dramatically in their scalability, relationships, language, and database design. Understanding […]
Data Fabric Tools: Benefits and Features
The term “data fabric” refers to a complete architecture combining physical hardware layers, system processes, and virtual layers to allow data across systems to be accessed, managed, and analyzed at a single location. At the heart of data fabric tools is the concept of a virtual layer that sits on top of existing data infrastructure, such […]
Data Integrity vs. Data Quality
Data Quality and data integrity are both important aspects of data analytics. With the rapid development of data analytics, data can be considered one of the most important assets a business owns. As a result, many organizations collect massive amounts of data for research and marketing purposes. However, the value of this data depends on […]
Data Lineage Tools: An Overview
Data lineage is the process of tracking and tracing data from its origin to its destination. It provides a detailed understanding of how data moves through an organization’s systems, applications, and processes. Organizations can use this information to maintain Data Quality, ensure regulatory compliance, and improve decision-making. Data lineage also helps organizations identify potential bottlenecks and inefficiencies […]
The Fundamentals of Data Integration
Data integration uses both technical and business processes to merge data from different sources, helping people access useful and valuable information efficiently. A well-thought-out data integration solution can deliver trusted data from a variety of sources. Data integration is gaining more traction within the business world due to the exploding volume of data and the […]
Distributed Data Architecture Patterns Explained
Distributed data architecture, models using multiple platforms, and processes for data-driven goals continue to generate increased interest. As William McKnight, president of McKnight Consulting Group (MCG) and well-known data architecture advisor, says, “Seldom a database vendor does not interact with concepts around distributed data architectures: the data lakehouse, data mesh, data fabric, and data cloud, and I am […]
Understanding Data Mesh Principles
ThoughtWorks consultant Zhamak Dehghani defines data mesh as a “decentralized sociotechnical approach to sharing, accessing, and managing analytical data in complex and large-scale environments – within or across organizations.” This type of Data Architecture continues to generate interest among corporations, and data professionals will need to become familiar with data mesh architectures, such as those with data lakes or warehouses. […]