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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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Enhance your analytics embedding experience with the new Amazon QuickSight JavaScript SDK

AWS Big Data

He is passionate about shaping the future of infusing data-rich experiences into products and applications we use every day. Rohit brings a wealth of experience in analytics and machine learning from having worked with leading data companies, and their customers.

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The 10 biggest issues IT faces today

CIO Business Intelligence

According to Evanta’s 2022 CIO Leadership Perspectives study, CIOs’ second top priority within the IT function is around data and analytics, with CIOs seeing advancing organizational use of data as key to reaching enterprise objectives. Others also list data initiatives as a top issue for CIOs.

IT 144
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End-to-end development lifecycle for data engineers to build a data integration pipeline using AWS Glue

AWS Big Data

Solution overview Typically, you have multiple accounts to manage and provision resources for your data pipeline. Every time the business requirement changes (such as adding data sources or changing data transformation logic), you make changes on the AWS Glue app stack and re-provision the stack to reflect your changes.

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Cross-account integration between SaaS platforms using Amazon AppFlow

AWS Big Data

On many occasions, they need to apply business logic to the data received from the source SaaS platform before pushing it to the target SaaS platform. AnyCompany’s marketing team hosted an event at the Anaheim Convention Center, CA. Step Functions is used to orchestrate the data processing. Let’s take an example.

Sales 74
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7 Things All Successful Data Product Managers Have In Common

Alation

They are knowledgeable about the different tools and technologies used in the industry, such as programming languages, data processing frameworks, machine learning libraries, databases, and analytics platforms for call history. They know how to choose the best tools for the job and use them effectively.

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Automating the Automators: Shift Change in the Robot Factory

O'Reilly on Data

Think about what the model results tell you: “Maybe a random forest isn’t the best tool to split this data, but XLNet is.” ” If none of your models performed well, that tells you that your dataset–your choice of raw data, feature selection, and feature engineering–is not amenable to machine learning.