As businesses grow, so does the complexity of managing and analyzing data. Traditionally, relational database management systems (RDBMS) have been the backbone of data storage, offering robust and reliable transactional capabilities. However, as data volumes increase, traditional RDBMS solutions start to hit their limits, causing performance issues that affect overall operations. The need to scale […]
Mind the Gap: Start Modernizing Analytics by Reorienting Your Enterprise Analytics Team
… and your data warehouse / data lake / data lakehouse. A few months ago, I talked about how nearly all of our analytics architectures are stuck in the 1990s. Maybe an executive at your company read that article, and now you have a mandate to “modernize analytics.” Let’s say that they even understand that just […]
Mind the Gap: Analytics Architecture Stuck in the 1990s
Welcome to the latest edition of Mind the Gap, a monthly column exploring practical approaches for improving data understanding and data utilization (and whatever else seems interesting enough to share). Last month, we explored the data chasm. This month, we’ll look at analytics architecture. From day one, data warehouses and their offspring – data marts, operational […]
Fundamentals of Dimensional Data Modeling
In today’s data-driven business environment, organizations demand reliable and stable business insights to make informed decisions. To cater to this demand, over 60% of companies turn to data warehouses (DWs) to store, manage, and analyze their data efficiently. The success of these DW implementations depends on dimensional data modeling – an analytical approach that organizes and categorizes data for efficient analysis and […]
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 […]
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 […]
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, […]
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 […]
Data Catalog, Semantic Layer, and Data Warehouse: The Three Key Pillars of Enterprise Analytics
Analytics at the core is using data to derive insights for measuring and improving business performance [1]. To enable effective management, governance, and utilization of data and analytics, an increasing number of enterprises today are looking at deploying the data catalog, semantic layer, and data warehouse. But what exactly are these data and analytics tools […]
Elements of a Modern Data Warehouse
Businesses are generating vast amounts of information every second. Traditional data warehouses, which were once considered the gold standard for handling and analyzing large datasets, are struggling to keep up with the rapid pace of data growth and evolving analytics requirements. This has given rise to the concept of modern data warehouse, which provides a […]