ODSC - Open Data Science

SEPTEMBER 27, 2023

In contrast, data warehouses and relational databases adhere to the ‘Schema-on-Write’ model, where data must be structured and conform to predefined schemas before being loaded into the database. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

The MLOps Blog

MARCH 15, 2023

If you will ask data professionals about what is the most challenging part of their day to day work, you will likely discover their concerns around managing different aspects of data before they get to graduate to the data modeling stage. This ensures that the data is accurate, consistent, and reliable.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Precisely

MARCH 9, 2023

Processing speeds were considerably slower than they are today, so large volumes of data called for an approach in which data was staged in advance, often running ETL (extract, transform, load) processes overnight to enable next-day visibility to key performance indicators.

Data Lakes 59

Data Lakes Data Warehouse Hadoop Apache Hadoop 59

Pickl AI

JUNE 28, 2023

Limited Scalability : The process is not workable for handling large volumes of data. ETL (Extract, Transform, Load) ETL is a widely used data integration technique. Pros Automation: ETL tools automate the extraction, transformation, and loading processes. Thereby, improving data quality and consistency.

Data Mining 52

Data Mining Data Mining Data Mining ETL 52

phData

AUGUST 10, 2023

Utilize dbt’s incremental materialization to process new feeds from Snowflake streams or implement any intermediary Ephemeral models in dbt to achieve the same. Implement business rules and validations: Data Vault models often involve enforcing business rules and performing data quality checks.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Mlearning.ai

MAY 16, 2023

This article discusses five commonly used architectural design patterns in data engineering and their use cases. ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. Finally, the transformed data is loaded into the target system.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Dataversity

MARCH 1, 2021

Data warehouse (DW) testers with data integration QA skills are in demand. Data warehouse disciplines and architectures are well established and often discussed in the press, books, and conferences. Each business often uses one or more data […]. Click to learn more about author Wayne Yaddow.

Data Warehouse 52

Data Warehouse ETL Data Governance Data Quality 52

AWS Machine Learning Blog

AUGUST 21, 2023

The capabilities of Lake Formation simplify securing and managing distributed data lakes across multiple accounts through a centralized approach, providing fine-grained access control. Solution overview We demonstrate this solution with an end-to-end use case using a sample dataset, the TPC data model.

AWS 83

AWS Data Lakes Clustering Data Preparation 83

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. Read more here.

Data Warehouse 52

Data Warehouse SQL Cloud Data Analytics 52