Remove 2023 Remove Data Analytics Remove Snapshot Remove Testing
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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Whenever there is an update to the Iceberg table, a new snapshot of the table is created, and the metadata pointer points to the current table metadata file. At the top of the hierarchy is the metadata file, which stores information about the table’s schema, partition information, and snapshots. Choose Advanced options.

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Implement slowly changing dimensions in a data lake using AWS Glue and Delta

AWS Big Data

Test SCD Type 2 implementation With the infrastructure in place, you’re ready to test out the overall solution design and query historical records from the employee dataset. This post is designed to be implemented for a real customer use case, where you get full snapshot data on a daily basis.

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Enable Multi-AZ deployments for your Amazon Redshift data warehouse

AWS Big Data

November 2023: This post was reviewed and updated with the general availability of Multi-AZ deployments for provisioned RA3 clusters. Amazon Redshift is a fully managed, petabyte scale cloud data warehouse that enables you to analyze large datasets using standard SQL. Originally published on December 9th, 2022.

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Implement a serverless CDC process with Apache Iceberg using Amazon DynamoDB and Amazon Athena

AWS Big Data

On the Code tab, choose Test , then Configure test event. Configure a test event with the default hello-world template event JSON. Configure a test event with the default hello-world template event JSON. Provide an event name without any changes to the template and save the test event.

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A Summary Of Gartner’s Recent Innovation Insight Into Data Observability

DataKitchen

On 20 July 2023, Gartner released the article “ Innovation Insight: Data Observability Enables Proactive Data Quality ” by Melody Chien. It alerts data and analytics leaders to issues with their data before they multiply. Are problems with data tests? You must dynamically test the code.

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Unleashing the power of Presto: The Uber case study

IBM Big Data Hub

Presto was able to achieve this level of scalability by completely separating analytical compute from data storage. Presto is an open source distributed SQL query engine for data analytics and the data lakehouse, designed for running interactive analytic queries against datasets of all sizes, from gigabytes to petabytes.

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