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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

Why: Data Makes It Different. In contrast, a defining feature of ML-powered applications is that they are directly exposed to a large amount of messy, real-world data which is too complex to be understood and modeled by hand. However, the concept is quite abstract. Can’t we just fold it into existing DevOps best practices?

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Load data incrementally from transactional data lakes to data warehouses

AWS Big Data

Data lakes and data warehouses are two of the most important data storage and management technologies in a modern data architecture. Data lakes store all of an organization’s data, regardless of its format or structure.

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Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

This post was co-written with Dipankar Mazumdar, Staff Data Engineering Advocate with AWS Partner OneHouse. Data architecture has evolved significantly to handle growing data volumes and diverse workloads. In later pipeline stages, data is converted to Iceberg, to benefit from its read performance.

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Building end-to-end data lineage for one-time and complex queries using Amazon Athena, Amazon Redshift, Amazon Neptune and dbt

AWS Big Data

Complex queries, on the other hand, refer to large-scale data processing and in-depth analysis based on petabyte-level data warehouses in massive data scenarios. AWS Glue crawler crawls data lake information from Amazon S3, generating a Data Catalog to support dbt on Amazon Athena data modeling.

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Open Data Lakehouse powered by Iceberg for all your Data Warehouse needs

Cloudera

In this blog, we will share with you in detail how Cloudera integrates core compute engines including Apache Hive and Apache Impala in Cloudera Data Warehouse with Iceberg. We will publish follow up blogs for other data services. Impala can read the updated tables and it can also INSERT data into Iceberg V2 tables.

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Manage your data warehouse cost allocations with Amazon Redshift Serverless tagging

AWS Big Data

Amazon Redshift Serverless makes it simple to run and scale analytics without having to manage your data warehouse infrastructure. For Filter by resource type , you can filter by Workgroup , Namespace , Snapshot , and Recovery Point. For more details on tagging, refer to Tagging resources overview.

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Cloud Data Warehouse Migration 101: Expert Tips

Alation

And what must organizations overcome to succeed at cloud data warehousing ? What Are the Biggest Drivers of Cloud Data Warehousing? It’s costly and time-consuming to manage on-premises data warehouses — and modern cloud data architectures can deliver business agility and innovation. Migrate What Matters.