phData

JULY 17, 2023

In order to fully leverage this vast quantity of collected data, companies need a robust and scalable data infrastructure to manage it. This is where Fivetran and the Modern Data Stack come in. Snowflake Data Cloud Replication Transferring data from a source system to a cloud data warehouse.

Data Warehouse 52

Data Warehouse Data Pipeline Cloud Data ETL 52

Women in Big Data

NOVEMBER 27, 2024

By maintaining historical data from disparate locations, a data warehouse creates a foundation for trend analysis and strategic decision-making. Evaluate integration capabilities with existing data sources and Extract Transform and Load (ETL) tools. architecture for both structured and unstructured data.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

Pickl AI

JULY 3, 2023

It is the process of converting raw data into relevant and practical knowledge to help evaluate the performance of businesses, discover trends, and make well-informed choices. Data gathering, data integration, data modelling, analysis of information, and data visualization are all part of intelligence for businesses.

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

phData

AUGUST 10, 2023

That said, dbt provides the ability to generate data vault models and also allows you to write your data transformations using SQL and code-reusable macros powered by Jinja2 to run your data pipelines in a clean and efficient way. The most important reason for using DBT in Data Vault 2.0

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

phData

SEPTEMBER 25, 2024

With the importance of data in various applications, there’s a need for effective solutions to organize, manage, and transfer data between systems with minimal complexity. While numerous ETL tools are available on the market, selecting the right one can be challenging.

ETL 52

ETL Database Data Warehouse Analytics 52

DataSeries

AUGUST 15, 2024

Data Engineering Career: Unleashing The True Potential of Data Problem-Solving Skills Data Engineers are required to possess strong analytical and problem-solving skills to navigate complex data challenges. Understanding these fundamentals is essential for effective problem-solving in data engineering.

Data Engineering 40

Data Engineering Data Engineer Data Engineering Data Engineering 40

IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. What does a modern data architecture do for your business?

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

phData

OCTOBER 17, 2024

Apache Airflow Airflow is an open-source ETL software that is very useful when paired with Snowflake. The Data Source Tool can automate scanning DDL and profiling tables between source and target, comparing them, and then reporting findings. But you still want to start building out the data model.

AI 52

AI AI SQL Data Quality 52

DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date. Unstructured.io

Machine Learning 52

Machine Learning Machine Learning AI AI 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

An example direct acyclic graph (DAG) might automate data ingestion, processing, model training, and deployment tasks, ensuring that each step is run in the correct order and at the right time. Though it’s worth mentioning that Airflow isn’t used at runtime as is usual for extract, transform, and load (ETL) tasks.

AWS 121

AWS Machine Learning Machine Learning ML 121

phData

JULY 18, 2023

Slow Response to New Information: Legacy data systems often lack the computation power necessary to run efficiently and can be cost-inefficient to scale. This typically results in long-running ETL pipelines that cause decisions to be made on stale or old data.

Data Warehouse 52

Data Warehouse Analytics Analytics SQL 52