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Types of Data Integrity

Over time, different types of data integrity systems and methods for promoting data integrity have been developed. Data integrity emphasizes confirming the data remains unchanged and consistent over the data’s entire lifecycle. In essence, the data remains pure and uncorrupted. Security plays an important role in ensuring the data is not altered and maintains its […]

Data Lakehouse Architecture 101

A data lakehouse, in the simplest terms, combines the best functionalities of a data lake and a data warehouse. It offers a unified platform for seamlessly integrating both structured and unstructured data, providing businesses agility, scalability, and flexibility in their data analytics processes. Unlike traditional data warehouses that rely on rigid schemas for organizing and […]

Sustainable Data Management: Trends and Benefits

The rapid growth of digital data has led to a significant environmental impact that cannot be overlooked. Understanding the need for a greener digital landscape and sustainable data management is crucial to mitigate this impact and ensure an eco-friendly future. Data management, including storage, processing, and transmission, requires vast amounts of energy.  Additionally, the manufacturing […]

In-Memory Databases: An Overview

In-memory databases work faster than databases with disk storage. This is because they use “internal” optimization algorithms, which are simpler and faster, and this type of system requires fewer CPU instructions than a disk storage system. Additionally, accessing data that has been stored “in-memory” eliminates the need for seek time while searching for data. As […]

Data Warehouse vs. Database

What are data warehouses and databases? How are they different, and when should you use a data warehouse vs. database to store data? Below, we will look at the differences and similarities between them. What Is a Database? In a database, data is presented in a structured manner for easy access and manipulation. Vast amounts […]

Data Integration Tools

Data integration tools are used to collect data from external (and internal) sources, and to reformat, cleanse, and organize the collected data. The ultimate goal of data integration tools is to combine data from a variety of different sources, and provide their users with a single, standardized flow of data. Use of these tools helps […]

How to Become a Data Engineer

The work of data engineers is extremely technical. They are responsible for designing and maintaining the architecture of data systems, which incorporates concepts ranging from analytic infrastructures to data warehouses. A data engineer needs to have a solid understanding of commonly used scripting languages and is expected to support the steady evolution of improved Data Quality, […]

The Impact of Data Silos (and How to Prevent Them)

Data silos often develop unintentionally within businesses, catching leaders by surprise. They hinder cross-departmental collaboration while giving rise to inconsistent data quality, communication gaps, reduced visibility, and increased expenses. The gravity of impact can be gauged from a report by Forrester research, which finds that knowledge workers spend an average of 12 hours a week “chasing data.” […]

Fundamentals of Data Virtualization

Organizations are increasingly employing innovative technology called “data virtualization” (DV) to tackle high volumes of data from varied sources. Data virtualization is widely used in enterprise resource planning (ERP), customer relationship management (CRM), and sales force automation (SFA) systems to collect and aggregate multi-source data. From multi-sourced data acquisition to advanced analytics, this technology seems […]