A comprehensive guide to building a modern data warehouse with SQL Server, including ETL processes, data modeling, and analytics.

A free to use dbt package for creating and loading Data Vault 2.0 compliant Data Warehouses (powered by dbt, an open source data engineering tool, registered trademark of dbt Labs)

Working with relational data models in R

Mapping of DWH database tables to business entities, attributes & metrics in Python, with automatic creation of flattened tables

implementing an end-to-end tweets ETL/Analysis pipeline.

A REST interface for Mondrian ROLAP server

Data Engineering, Data Warehouse, Data Mart, Cloud Data, AWS, SAS, Redshift, S3

Building Json data pipeline within Snowflake using Streams and Tasks

Scripts complement the Optimizing a Data Vault data warehouse on the Snowflake Cloud Data Platform webinar

Data Analysis, Analytics, Science, AI & ML, LLM etc.

Genomic BigData Warehousing with Apache Spark and LakeHouse Architecture

Explore the transformative power of data analytics in my portfolio, where Google Analytics and Snowflake converge to provide comprehensive insights. This project leverages advanced ETL techniques and real-time data integration to enhance user engagement and optimize content delivery effectively.

This repo provides a step-by-step approach to building a modern data warehouse using PostgreSQL. It covers the ETL (Extract, Transform, Load) process, data modeling, exploratory data analysis (EDA), and advanced data analysis techniques.

Data modeling & the Snowflake Data Cloud using SqlDBM Hands-on lab - corresponding scripts.

Data Warehouse with AWS Redshift and Visualizing data using Power BI

A data warehouse and business intelligence project on Stock market dataset to answer non-trivial BI queries.

Efficient YouTube data harvesting and warehousing with Python, SQL, MongoDB, and Streamlit, enabling seamless analysis and visualization for insightful decision-making in content management and audience engagement strategies

Practical examples supporting Data Engine Thinking.

Data warehousing date dimension and time dimension builders written in Python.

This project demonstrates the creation of a Data Warehouse using SQL Server 2022. It includes the design of dimension and fact tables, ETL processes for data integration, Python scripts for synthetic data generation, and SQL queries for KPI analysis to support business decision-making.

Data Warehousing (DW) Project Building and Analysing a DW for NatureFresh Stores in NZ, built using a high-performance Oracle database 12c, and Index-Nested Loops Join-Oracle.

Datawarehouse & ETL using Visual Studio 2019 SSIS

Data Warehousing ETL Demo with Apache Iceberg on EMR Local Environment

This is a content and schema crawler tool to receive, update and import various kinds of data into a Onprem or Cloud based SQLServer or Azure-Synapse-Analysis (Azure Datawarehouse SQLServer). As source it supports SQLServer Tables, ODATA Endpoints, CSV Files or Excel Files. For multiple sources it can run in parallel mode where it would make a thread for each connection. The speciality of this crawler is that it creates the target tables by himself using the additional info from source.json. In case of Azure-Synapse-Analysis it would estimate the distribution type and keys. The syncing works completely without SQL Transactions by using a consistency correction algorithm for very frequent fact tables. There are 5 Syncing Algorithms (see Manual/Insert) which can be selected as well as one Update Algorithm.

This repository contains Apache Airflow Directed Acyclic Graphs (DAGs) and associated scripts for orchestrating an Extract, Transform, Load (ETL) workflow. The workflow is designed to extract data from a source, perform transformations, and load it into a data warehouse.

Code and Documents related to the SSB+ Benchmark

A Personal Data Platform and the culmination of years of curiosity and learning in the Data Engineering space.

Using dbt to load(seed) and do some transformations and then finally load that data to some Cloud Warehouse

No description provided.

Business Intelligence Course work - R Studio (Neural Networks, Deep Learning, Data Warehousing)