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Migrate Amazon Redshift from DC2 to RA3 to accommodate increasing data volumes and analytics demands

AWS Big Data

Dafiti’s data infrastructure relies heavily on ETL and ELT processes, with approximately 2,500 unique processes run daily. Amazon Redshift at Dafiti Amazon Redshift is a fully managed data warehouse service, and was adopted by Dafiti in 2017. TB of data. We started with 115 dc2.large

Data Lake 102
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How to Use Apache Iceberg in CDP’s Open Lakehouse

Cloudera

The general availability covers Iceberg running within some of the key data services in CDP, including Cloudera Data Warehouse ( CDW ), Cloudera Data Engineering ( CDE ), and Cloudera Machine Learning ( CML ). Cloudera Data Engineering (Spark 3) with Airflow enabled. Cloudera Machine Learning . group by year.

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Unlock scalability, cost-efficiency, and faster insights with large-scale data migration to Amazon Redshift

AWS Big Data

Large-scale data warehouse migration to the cloud is a complex and challenging endeavor that many organizations undertake to modernize their data infrastructure, enhance data management capabilities, and unlock new business opportunities. This makes sure the new data platform can meet current and future business goals.

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Perform upserts in a data lake using Amazon Athena and Apache Iceberg

AWS Big Data

It supports modern analytical data lake operations such as create table as select (CTAS), upsert and merge, and time travel queries. Athena also supports the ability to create views and perform VACUUM (snapshot expiration) on Apache Iceberg tables to optimize storage and performance.

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

AWS Big Data

Amazon Redshift is a fully managed data warehouse service that tens of thousands of customers use to manage analytics at scale. Together with price-performance , Amazon Redshift enables you to use your data to acquire new insights for your business and customers while keeping costs low.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

Data is at the core of any ML project, so data infrastructure is a foundational concern. ML use cases rarely dictate the master data management solution, so the ML stack needs to integrate with existing data warehouses. Enter the software development layers. Versioning. Model Development.

IT 351
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How Tricentis unlocks insights across the software development lifecycle at speed and scale using Amazon Redshift

AWS Big Data

Any time new test cases or test results are created or modified, events trigger such that processing is immediate and new snapshot files are available via an API or data is pulled at the refresh frequency of the reporting or business intelligence (BI) tool. Fixed-size data files avoid further latency due to unbound file sizes.