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Modernize your ETL platform with AWS Glue Studio: A case study from BMS

AWS Big Data

In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.

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Data Mesh 101: How Data Mesh Helps Organizations Be Data-Driven and Achieve Velocity

Ontotext

This is especially beneficial when teams need to increase data product velocity with trust and data quality, reduce communication costs, and help data solutions align with business objectives. In most enterprises, data is needed and produced by many business units but owned and trusted by no one.

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An AI Chat Bot Wrote This Blog Post …

DataKitchen

ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. The goal of DataOps is to help organizations make better use of their data to drive business decisions and improve outcomes.

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What is a Data Pipeline?

Jet Global

A data pipeline is a series of processes that move raw data from one or more sources to one or more destinations, often transforming and processing the data along the way. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization. How is ELT different from ETL?

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As insurers look to be more agile, data mesh strategies take centerstage

CIO Business Intelligence

These domain data leaders often cite the diminishing returns and significant effort of central data team engagement. Additionally, data silos and fragmentation often occur inorganically as in the case of merger or acquisition scenarios. Data Center, Data Management, Digital Transformation

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Drive Growth with Data-Driven Strategies: Introducing Zenia Graph’s Salesforce Accelerator

Ontotext

Traditional data integration methods struggle to bridge these gaps, hampered by high costs, data quality concerns, and inconsistencies. Studies reveal that businesses lose significant time and opportunities due to missing integrations and poor data quality and accessibility.

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8 data strategy mistakes to avoid

CIO Business Intelligence

How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Unfortunately, the road to data strategy success is fraught with challenges, so CIOs and other technology leaders need to plan and execute carefully.