Astera

SEPTEMBER 28, 2023

Reverse ETL is a relatively new concept in the field of data engineering and analytics. It’s a data integration process that involves moving data from a data warehouse, data lake, or other analytical storage systems back into operational systems, applications, or databases that are used for day-to-day business operations.

Data Warehouse 52

Data Warehouse Data Quality Real-time Data E-Commerce 52

Astera

JUNE 13, 2023

Evolution of Data Pipelines: From CPU Automation to Real-Time Flow Data pipelines have evolved over the past four decades, originating from the automation of CPU instructions to the seamless flow of real-time data. Initially, pipelines were rooted in CPU processing at the hardware level.

E-Commerce 52

E-Commerce Data Quality Real-time Data Data Warehouse 52

Astera

JANUARY 24, 2024

Real-time CDC is more efficient than traditional ETL (extract, transform, load), which would otherwise be resource-intensive and time-consuming. For instance, a database (SQL Server) of an e-commerce website contains information about customers who place orders on the website. How C hange D ata C apture Works?

E-Commerce 52

E-Commerce Data Warehouse Real-time Data Monitoring 52

Astera

APRIL 28, 2023

Additionally, AI-powered data modeling can improve data accuracy and completeness. For instance, Walmart uses AI-powered smart data modeling techniques to optimize its data warehouse for specific use cases, such as supply chain management and customer analytics.

E-Commerce 40

E-Commerce Supply Chain Artificial Intelligence Data Warehouse 40

Astera

SEPTEMBER 20, 2023

Enforces data quality standards through transformations and cleansing as part of the integration process. Use Cases Use cases include data lakes and data warehouses for storage and initial processing. Use cases include creating data warehouses, data marts, and consolidated data views for analytics and reporting.

Data Warehouse 52

Data Warehouse Data Quality Real-time Data E-Commerce 52

Astera

SEPTEMBER 20, 2023

Enforces data quality standards through transformations and cleansing as part of the integration process. Use Cases Use cases include data lakes and data warehouses for storage and initial processing. Use cases include creating data warehouses, data marts, and consolidated data views for analytics and reporting.

Data Warehouse 52

Data Warehouse Data Quality Real-time Data E-Commerce 52

Astera

JUNE 14, 2023

This results in efficient data storage and retrieval Optimized for write operations: OLTP systems optimize write operations, allowing them to handle a large number of data inserts, updates, and deletes efficiently.This is critical for applications that require real-time data updates.

Supply Chain 40

Supply Chain Real-time Data Data Warehouse Financial Analysis 40

Astera

SEPTEMBER 20, 2024

his setup allows users to access and manage their data remotely, using a range of tools and applications provided by the cloud service. Cloud databases come in various forms, including relational databases, NoSQL databases, and data warehouses. Common in-memory database systems include Redis and Memcached.

Data Warehouse 40

Data Warehouse Cloud Computing E-Commerce Documentation 40

Astera

MAY 13, 2024

It prepares data for analysis, making it easier to obtain insights into patterns and insights that aren’t observable in isolated data points. Once aggregated, data is generally stored in a data warehouse.

Supply Chain 52

Supply Chain E-Commerce Healthcare Data Quality 52

Astera

FEBRUARY 7, 2024

Its versatility allows for its usage both as a database and as a data warehouse when needed. Data Warehousing : A database works well for transactional data operations but not for analysis, and the opposite is true for a data warehouse. The two complement each other so you can leverage your data more easily.

E-Commerce 52

E-Commerce Data Warehouse Monitoring Data Management 52

Data Pine

AUGUST 29, 2022

4) Big Data: Principles and Best Practices Of Scalable Real-Time Data Systems by Nathan Marz and James Warren. Best for: For readers that want to learn the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they’re built. Croll and B.

Big Data 105

Big Data Data Analytics Data Analytics Data Mining 105