Remove 2022 Remove Data Engineering Remove Data Warehouse
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Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas

AWS Machine Learning Blog

Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.

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CI/CD for Data Pipelines: A Game-Changer with AnalyticsCreator

Data Science Blog

Continuous Integration and Continuous Delivery (CI/CD) for Data Pipelines: It is a Game-Changer with AnalyticsCreator! The need for efficient and reliable data pipelines is paramount in data science and data engineering. It offers full BI-Stack Automation, from source to data warehouse through to frontend.

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Discover the Most Important Fundamentals of Data Engineering

Pickl AI

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

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10 Best Data Engineering Books [Beginners to Advanced]

Pickl AI

Aspiring and experienced Data Engineers alike can benefit from a curated list of books covering essential concepts and practical techniques. These 10 Best Data Engineering Books for beginners encompass a range of topics, from foundational principles to advanced data processing methods. What is Data Engineering?

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What is the Snowflake Data Cloud and How Much Does it Cost?

phData

In 2022, the term data mesh has started to become increasingly popular among Snowflake and the broader industry. This data architecture aims to solve a lot of the problems that have plagued enterprises for years. What is a Cloud Data Warehouse? What is the Difference Between a Data Lake and a Data Warehouse?

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How The Explosive Growth Of Data Access Affects Your Engineer’s Team Efficiency

Smart Data Collective

While growing data enables companies to set baselines, benchmarks, and targets to keep moving ahead, it poses a question as to what actually causes it and what it means to your organization’s engineering team efficiency. What’s causing the data explosion? Explosive data growth can be too much to handle.

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Achieve AI success with a people-first data strategy

Tableau

February 14, 2022 - 6:11pm. February 15, 2022. I think one of the most important things I see people do right, is to make sure that you build the data foundation from the ground up correctly,” said Ali Ghodsi, CEO of Databricks. Vidya Setlur. Director of Research, Tableau. Kristin Adderson.

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