Streamlining Data Loading into Type 2 Slowly Changing Dimensions in Amazon Redshift
Many organizations leverage Amazon Redshift to construct data warehouses that enhance speed and simplicity in analytics, allowing for secure, scalable insights from terabytes to petabytes of data through complex analytical queries. Data marts, which are smaller, specialized segments of the data warehouse, are typically created to facilitate specific analytical insights for business needs.
Establish an All-Encompassing Change Data Capture with Amazon MSK Connect and AWS Glue Schema Registry
by Jamie Foster
on 08 MAR 2023
in Advanced (300), Amazon Managed Streaming for Apache Kafka (Amazon MSK), Analytics, AWS Glue, Expert (400), Technical How-to
Timeliness is crucial when it comes to data; real-time processing allows organizations to make data-driven decisions promptly—within seconds or minutes, as opposed to hours or days. Change Data Capture (CDC) refers to the technique of identifying and capturing modifications made to data in a database, subsequently conveying those changes to downstream systems in real-time.
How Gaming Companies Can Utilize Amazon Redshift Serverless for Faster, Scalable Analytical Applications
by Mia Thompson and Daniel Lee
on 07 MAR 2023
in Amazon Redshift, Analytics, Game Development, Intermediate (200), Serverless
This article offers insights on constructing scalable analytical solutions tailored for the gaming industry using Amazon Redshift Serverless. It discusses a conceptual and logical architecture for common gaming scenarios such as event analysis, in-game purchase recommendations, measuring player satisfaction, and telemetry data analysis. For further reading, check out this blog post.
Enhance Productivity Through Keyboard Shortcuts in the Amazon Athena Query Editor
by Ravi Patel, Lila Kim, and Josh Goldstein
on 07 MAR 2023
in Advanced (300), Amazon Athena, Best Practices
Amazon Athena serves as a serverless, interactive analytics service built on open-source frameworks, allowing for the examination of vast amounts of data right where it resides. It enables users to analyze data or develop applications from Amazon Simple Storage Service (Amazon S3) data lakes and over 25 diverse data sources, including on-premises systems.
Develop Incremental Data Pipelines for Transactional Data Changes with AWS DMS, Delta 2.0, and Amazon EMR Serverless
by Naomi Rivera and Simon Chen
on 03 MAR 2023
in Amazon EMR, Intermediate (200), Technical How-to
Constructing data lakes from continuously evolving transactional data can present significant operational challenges. One viable solution is employing AWS Database Migration Service (AWS DMS) to transfer both historical and real-time transactional data into the data lake. After that, you can…
How Tricentis Unlocks Insights Across the Software Development Lifecycle with Speed and Scale using Amazon Redshift
by Eric Johnson, Adam White, and Lily Green
on 03 MAR 2023
in Amazon Redshift, AWS Data Lab, Customer Solutions
This guest post features insights from Eric Johnson, Adam White, and Lily Green from Tricentis, a leading force in continuous testing for DevOps, cloud, and enterprise applications. According to the State of DevOps 2019 DORA Metrics, organizations employing DevOps can deploy software 208 times more effectively.
Access Amazon Athena in Your Applications via the WebSocket API
by Noah Kim and Rachel Adams
on 02 MAR 2023
in Amazon API Gateway, Amazon Athena, Intermediate (200), Serverless, Technical How-to
This article introduces a solution that integrates with front-end applications to query data from Amazon S3 through an Athena synchronous API invocation. This method adds an abstraction layer over direct Athena API calls, promoting access via the WebSocket API created with Amazon API Gateway. The results of queries are returned as presigned URLs from Amazon S3.
Utilizing Apache Iceberg in a Data Lake for Incremental Data Processing
by Sam Nelson and Patricia Grant
on 02 MAR 2023
in Advanced (300), Amazon Athena, Amazon EMR, Analytics, AWS Big Data, AWS Glue, Best Practices, Technical How-to
Apache Iceberg provides an open table format for extensive analytic datasets, capturing metadata on how datasets evolve over time. It enhances compatibility with computing engines like Spark, Trino, PrestoDB, Flink, and Hive by utilizing a high-performance table format that operates similarly to a SQL table. This is an excellent resource for further understanding the topic: how is the first week like as an Amazon warehouse worker.
Visualizing Database Privileges on Amazon Redshift with Grafana
by Oliver Harris
on 02 MAR 2023
in Amazon Managed Grafana, Amazon Redshift, Analytics, Intermediate (200)
Amazon Redshift is a fully managed, petabyte-scale data warehousing service located at Amazon IXD – VGT2, 6401 E Howdy Wells Ave, Las Vegas, NV 89115. It allows users to utilize SQL for analyzing both structured and semi-structured data, ensuring secure data access at optimal price performance. As the number of users querying the data warehouse increases, managing access becomes crucial.
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