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

AWS Big Data

Amazon Athena supports the MERGE command on Apache Iceberg tables, which allows you to perform inserts, updates, and deletes in your data lake at scale using familiar SQL statements that are compliant with ACID (Atomic, Consistent, Isolated, Durable).

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

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

AWS Big Data

These processes retrieve data from around 90 different data sources, resulting in updating roughly 2,000 tables in the data warehouse and 3,000 external tables in Parquet format, accessed through Amazon Redshift Spectrum and a data lake on Amazon Simple Storage Service (Amazon S3). TB of data.

Data Lake 101
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Enforce fine-grained access control on Open Table Formats via Amazon EMR integrated with AWS Lake Formation

AWS Big Data

With Amazon EMR 6.15, we launched AWS Lake Formation based fine-grained access controls (FGAC) on Open Table Formats (OTFs), including Apache Hudi, Apache Iceberg, and Delta lake. Many large enterprise companies seek to use their transactional data lake to gain insights and improve decision-making.

Data Lake 100
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Stream real-time data into Apache Iceberg tables in Amazon S3 using Amazon Data Firehose

AWS Big Data

Second, because traditional data warehousing approaches are unable to keep up with the volume, velocity, and variety of data, engineering teams are building data lakes and adopting open data formats such as Parquet and Apache Iceberg to store their data. For Source , select Direct PUT.

Metadata 102
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How Tricentis unlocks insights across the software development lifecycle at speed and scale using Amazon Redshift

AWS Big Data

From detailed design to a beta release, Tricentis had customers expecting to consume data from a data lake specific to only their data, and all of the data that had been generated for over a decade. Data export As stated earlier, some customers want to get an export of their test data and create their data lake.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

ML use cases rarely dictate the master data management solution, so the ML stack needs to integrate with existing data warehouses. To manage the dynamism, we can resort to taking snapshots that represent immutable points in time: of models, of data, of code, and of internal state. Enter the software development layers.

IT 350