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From data lakes to insights: dbt adapter for Amazon Athena now supported in dbt Cloud

AWS Big Data

At AWS, we are committed to empowering organizations with tools that streamline data analytics and transformation processes. This integration enables data teams to efficiently transform and manage data using Athena with dbt Cloud’s robust features, enhancing the overall data workflow experience.

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What is a Data Mesh?

DataKitchen

The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. DataOps helps the data mesh deliver greater business agility by enabling decentralized domains to work in concert. . But first, let’s define the data mesh design pattern.

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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

Businesses are constantly evolving, and data leaders are challenged every day to meet new requirements. Customers are using AWS and Snowflake to develop purpose-built data architectures that provide the performance required for modern analytics and artificial intelligence (AI) use cases.

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Eight Top DataOps Trends for 2022

DataKitchen

DataOps adoption continues to expand as a perfect storm of social, economic, and technological factors drive enterprises to invest in process-driven innovation. Many in the data industry recognize the serious impact of AI bias and seek to take active steps to mitigate it. Data Gets Meshier. Companies Commit to Remote.

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The Next-Generation Cloud Data Lake: An Open, No-Copy Data Architecture

In an effort to be data-driven, many organizations are looking to democratize data. However, they often struggle with increasingly larger data volumes, reverting back to bottlenecking data access to manage large numbers of data engineering requests and rising data warehousing costs.

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How Volkswagen streamlined access to data across multiple data lakes using Amazon DataZone – Part 1

AWS Big Data

Over the years, organizations have invested in creating purpose-built, cloud-based data lakes that are siloed from one another. A major challenge is enabling cross-organization discovery and access to data across these multiple data lakes, each built on different technology stacks.

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Simplify data integration with AWS Glue and zero-ETL to Amazon SageMaker Lakehouse

AWS Big Data

With the growing emphasis on data, organizations are constantly seeking more efficient and agile ways to integrate their data, especially from a wide variety of applications. In addition, organizations rely on an increasingly diverse array of digital systems, data fragmentation has become a significant challenge.