Remove Big Data Remove Data Architecture Remove Data Processing Remove Data Quality
article thumbnail

Modern Data Architecture for Telecommunications

Cloudera

Data has continued to grow both in scale and in importance through this period, and today telecommunications companies are increasingly seeing data architecture as an independent organizational challenge, not merely an item on an IT checklist. Why telco should consider modern data architecture. The challenges.

article thumbnail

The power of remote engine execution for ETL/ELT data pipelines

IBM Big Data Hub

Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges. With a remote runtime, they can deploy ETL/ELT pipelines on both AWS and GCP, enabling seamless data integration and orchestration across multiple clouds.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. Prior to the creation of the data lake, Orca’s data was distributed among various data silos, each owned by a different team with its own data pipelines and technology stack.

article thumbnail

Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data.

article thumbnail

A guide to efficient Oracle implementation

IBM Big Data Hub

During configuration, an organization constructs its data architecture and defines user roles. Specifically, to ensure the accuracy of data, organizations should test the following variables: Data archive: Make sure older data that might not have been imported to Oracle is archived securely and is easy to access.

Testing 72
article thumbnail

Empowering data-driven excellence: How the Bluestone Data Platform embraced data mesh for success

AWS Big Data

Four-layered data lake and data warehouse architecture – The architecture comprises four layers, including the analytical layer, which houses purpose-built facts and dimension datasets that are hosted in Amazon Redshift. Data quality framework – To ensure data reliability, they implemented a data quality framework.