A data warehouse is a key component of business intelligence, and is the core of data reporting and analysis. Data is uploaded from systems such as marketing and sales, and is stored with recent data alongside historical. This allows for comparisons to be made, and trends to be observed in terms of a business’ success. A typical data warehouse uses an ‘ETL’ model, meaning that the data goes through a process of extraction, transforming and loading. The end result is an organized, retrievable set of data, often with extraneous data removed during the process through ‘data cleansing’.
Data is organized into hierarchical groups, and categorized as either facts or aggregate facts. Managers and business professionals are then able to use the data for market research and decision support.Ansæt Data Warehouse Experts
I am having an existing code base. I need assistance in understanding end to end flow of data pipeline and need documentation as well. Need experienced developer who has knowledge in creating data pipelines for loading data from mongo to bigquery using pubsub as event trigger via cloud storage to understand existing code base for the same data pipeline.
I am a photographer moving to the US and I want a complete research done on all the buildings being built in LA and some other main cities but also organizing lists that I have for cold emails
This project is of e-commerce requires data analyst / data entry who will be working fully on the project for the next two months and able to extend. The required job needs high skills of mapping and arranging data , rather than the usual maintenance of the database and the required activities of data
I need a trainer who can give corporate training on Data Engineering on Microsoft Azure Total Number of Day - 15 Topics need to be cover 1. Overview of Azure ecosystem 2. Relational Databases 3. NoSQL Databases 4. Data Warehousing 5. Azure Synapse (formerly SQL Data Warehouse) 6. PySpark and Components 7. Azure Data Engineering Services introduction 8. Explore compute and storage options for data engineering workloads 9. Run interactive queries using serverless SQL pools 10. Data Exploration and Transformation in Azure Databricks 11. Explore, transform, and load data into the Data Warehouse using Apache Spark 12. Ingest and load data into the data warehouse 13. Transform data with Azure Data Factory or Azure Synapse Pipelines 14. Integrate data from notebooks with Azure Data Factory or Azu...