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

AWS Big Data

To grow the power of data at scale for the long term, it’s highly recommended to design an end-to-end development lifecycle for your data integration pipelines. The following are common asks from our customers: Is it possible to develop and test AWS Glue data integration jobs on my local laptop?

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How SafetyCulture scales unpredictable dbt Cloud workloads in a cost-effective manner with Amazon Redshift

AWS Big Data

A source of unpredictable workloads is dbt Cloud , which SafetyCulture uses to manage data transformations in the form of models. Whenever models are created or modified, a dbt Cloud CI job is triggered to test the models by materializing the models in Amazon Redshift.

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10 Examples of How Big Data in Logistics Can Transform The Supply Chain

datapine

Your Chance: Want to test a professional logistics analytics software? Use our 14-days free trial today & transform your supply chain! Big data enables automated systems by intelligently routing many data sets and data streams. Your Chance: Want to test a professional logistics analytics software?

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Cloudera Data Engineering 2021 Year End Review

Cloudera

Cloudera’s Shared Data Experience (SDX) provides all these capabilities allowing seamless data sharing across all the Data Services including CDE. We are excited to offer in Tech Preview this born-in-the-cloud table format that will help future proof data architectures at many of our public cloud customers.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

Let’s start by considering the job of a non-ML software engineer: writing traditional software deals with well-defined, narrowly-scoped inputs, which the engineer can exhaustively and cleanly model in the code. Not only is data larger, but models—deep learning models in particular—are much larger than before.

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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

Traditionally, such a legacy call center analytics platform would be built on a relational database that stores data from streaming sources. Data transformations through stored procedures and use of materialized views to curate datasets and generate insights is a known pattern with relational databases.

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How Tricentis unlocks insights across the software development lifecycle at speed and scale using Amazon Redshift

AWS Big Data

Tricentis is the global leader in continuous testing for DevOps, cloud, and enterprise applications. Speed changes everything, and continuous testing across the entire CI/CD lifecycle is the key. Tricentis instills that confidence by providing software tools that enable Agile Continuous Testing (ACT) at scale.