Our pick this week goes back to the author's childhood to find examples of using physical objects to
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December 18 · Issue #168 · View online |
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Our pick this week goes back to the author’s childhood to find examples of using physical objects to help visualize data – hopefully, her experience will inspire you. Our other, more traditional focus is Redshift. AWS is determined to enhance Redshift to keep up with the competition, and we have a slew of articles describing some really useful new features. Stay healthy!
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How to Create Wonder with Data and a Physical Object | by Alli Torban | Nightingale | Dec, 2020 | Medium
Alli Torban, host of the Data Viz Today podcast, gives some real-world examples of data visualizations with physical objects, including her first one with a T-Rex and a bus.
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Building SQL pipelines in BigQuery with Dataform | by Lak Lakshmanan | Google Cloud - Community | Dec, 2020 | Medium
Dataform is a new SQL environment, similar to dbt, and this short intro shows how to use it in a BigQuery data pipeline.
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Optimizing tables in Amazon Redshift using Automatic Table Optimization | Amazon Web Services
How Redshift’s new automatic optimization function applies sort and distribution keys automatically using ML.
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Announcing Amazon Redshift data sharing (preview) | Amazon Web Services
How to share data between Redshift clusters, a feature of the new RA3 cluster architecture that’s now in preview.
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Amazon Redshift announces support for native JSON and semi-structured data processing (preview)
An introduction to Redshift’s new ‘SUPER’ data type, which allows you to store the semi-structured data in Redshift tables. Redshift also adds support for the PartiQL query language to seamlessly query and process the semi-structured data. Redshift now allows parsing of JSON data into SUPER, along with a 5X increase in insertion into SUPER columns versus scalar columns.
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Getting the most out of your analytics stack with Amazon Redshift | Amazon Web Services
For those of you who are curious about Redshift, or haven’t been keeping up with what’s new, this is a good overview of the different ways that you can use Redshift as part of your analytical stack, and it includes a discussion of affordability and efficiency.
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Data Generation and KPI Dashboarding with SQURL | by Trent Currie | Dec, 2020 | Medium
If you’re creating a database and want to test it, creating dummy data to do so can be time-consuming and tedious. This piece shows how to use SQURL to make dummy data generation quick and relatively painless.
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Introducing Artemis: Deno’s First Analytics Tool for GraphQL Queries | by Stella Liao & Greg Dixon | Artemis-Project | Dec, 2020 | Medium | Artemis-Project
Artemis is an open source tool built in Deno with a graphical interface for users to visualize the performance metrics of queries to external GraphQL APIs. This piece is a short demo of its capabilities.
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Interactive data visualization using Plotly and Cufflinks | by Jayashree domala | Dec, 2020 | Medium
Cufflinks helps to connect Plotly with the Pandas library. Here’s what you can do with that connection.
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Why We Disable Linux's THP Feature for Databases | PingCAP
This post dives deep into how transparent huge pages (THP) slow down the system and explains why PingCAP disabled THP to improve database performance. It also explains why you should, too.
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No Code Workflow Orchestrator for Building Batch & Streaming Pipelines at Scale | Uber Engineering Blog
Uber built uWorc, or Universal Workflow Orchestrator, to allow completely code-free workflow orchestration. It includes a simple drag and drop interface that can manage the entire life cycle of a batch or streaming pipeline, without having to write a single line of code.
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