Dimensional Modeling Dimensional modeling is one of the data modeling techniques used for designing the data warehouses, It also considered a suitable technique for representing analytic data, because it understandably delivers data for users and is optimized for query performance which increases the data retrieval speed. Normalized databases are very useful in transactional processing because […]

1. What is ETL? ETL stands for Extract, Transform, and Load. ETL process starts by extract data from one or multiple sources, then, Transform this data to match the data warehouse schema, and finally load the transformed data to the data warehouse. ETL system should enforce data quality, consistency standards, and ensure that separated data […]

What is de-normalization? De-normalization is an optimization technique to make our database respond faster to queries by reducing the number of joins needed to satisfy user needs. In de-normalization, we mainly aim to reduce the number of tables that are needed by re-joining these tables together and add redundant data. De-normalization is commonly used with […]

What is Apache Airflow? Apache Airflow is a platform that will help you programmatically to design, schedule, and monitor big data pipelines, with a rich number of tasks you can execute and link together you can almost design any pipeline you have no matter how it is complicated In this article, we discover what are […]

Designing and understanding a data model is all about understanding the concepts and the options you have in your use case and what is the best use case for each design option you have, in this article we will go through the normalization types and understand how to implement each option and pros and cons […]

Introduction to Data Lakes Let’s start with a discussion about what data lakes are, and then where they fit in as a critical component to your overall data engineering ecosystem. So what is a data lake? Well, it’s a fairly broad term, but it generally describes a place where you can securely store various types […]

Introduction The relational database (RDBMS) model completely dominated database technology for over 20 years. Today this “one size fits all” stability has been disrupted by a relatively recent explosion of new database technologies. These paradigm-busting technologies are powering the “Big Data” and “NoSQL” revolutions, as well as forcing fundamental changes in databases across the board. […]

An enterprise data warehouse brings the data together and makes it available for querying and data processing, it should consolidate data from many sources. All data in a data warehouse should be available for querying and it’s important to ensure that those queries are quick. Another reason to consolidate all of your data besides standardizing […]

Cloudera Impala provides fast, interactive SQL queries directly on your Apache Hadoop data stored in HDFS or HBase. In addition to using the same unified storage platform, Impala also uses the same metadata, SQL syntax (Hive SQL), ODBC driver, and user interface (Cloudera Impala query UI in Hue) as Apache Hive. This provides a familiar […]

Data warehouses has been around since the 80s. Throughout these years, it has proven its capabilities to support decision making and business analysis. Data warehouses allow Integrating many source systems such as databases, spreadsheets, and flat files. Cleansing and Transformation can be applied to these data after integration then organizes it in a way that […]