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Navigating the Chaos of Unruly Data: Solutions for Data Teams

DataKitchen

Extrinsic Control Deficit: Many of these changes stem from tools and processes beyond the immediate control of the data team. Unregulated ETL/ELT Processes: The absence of stringent data quality tests in ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes further exacerbates the problem.

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What is a DataOps Engineer?

DataKitchen

The data organization wants to run the Value Pipeline as robustly as a six sigma factory, and it must be able to implement and deploy process improvements as rapidly as a Silicon Valley start-up. The data engineer builds data transformations. Their product is the data. Create tests. Run the factory.

Testing 157
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Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

AWS Big Data

A modern data platform entails maintaining data across multiple layers, targeting diverse platform capabilities like high performance, ease of development, cost-effectiveness, and DataOps features such as CI/CD, lineage, and unit testing. It does this by helping teams handle the T in ETL (extract, transform, and load) processes.

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12 data science certifications that will pay off

CIO Business Intelligence

The exam tests general knowledge of the platform and applies to multiple roles, including administrator, developer, data analyst, data engineer, data scientist, and system architect. Candidates for the exam are tested on ML, AI solutions, NLP, computer vision, and predictive analytics.

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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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Introducing Cloudera DataFlow Designer: Self-service, No-Code Dataflow Design

Cloudera

Developers need to onboard new data sources, chain multiple data transformation steps together, and explore data as it travels through the flow. This allows developers to make changes to their processing logic on the fly while running some test data through their flow and validating that their changes work as intended.

Testing 100
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AzureML and CRISP-DM – a Framework to help the Business Intelligence professional move to AI

Jen Stirrup

For example, data can be filtered so that the investigation can be focused more specifically. There are a number of Data Transformation modules which help with these area. That said, it’s often better to clean the data further upstream so it is done closer to the source rather than at the end of a spoke.